diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index 469b82ea..b0d564de 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -5,7 +5,7 @@ - Ensure code follows [PEP8](https://www.python.org/dev/peps/pep-0008/). - Provide tests. - Ideally, provide examples and/or tutorials. -- Make sure you follow the checklists on the [pull request template](PULL_REQUEST_TEMPLATE.md) +- Make sure you follow the checklists on the [pull request template](PULL_REQUEST_TEMPLATE/PULL_REQUEST_TEMPLATE.md) - Branch off and request merges to the `dev` branch # Workflow diff --git a/.github/PULL_REQUEST_TEMPLATE/NEW_RELEASE_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE/NEW_RELEASE_TEMPLATE.md new file mode 100644 index 00000000..ff6d35da --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/NEW_RELEASE_TEMPLATE.md @@ -0,0 +1,20 @@ +## Release Checklist + +### Pull Request +- [ ] Branch is from `dev` to `rel/v[x.y.z]` +- [ ] Update the [`CHANGELOG.md`](https://github.com/UKRIN-MAPS/ukat/blob/master/CHANGELOG.md) file with the new version number, release date and add the new features/fixes along with any tags of PRs/issues +- [ ] Bump the version number in [`setup.py`](https://github.com/UKRIN-MAPS/ukat/blob/master/setup.py) +- [ ] Add any new contributors and bump the version number in [`CITATION.cff`](https://github.com/UKRIN-MAPS/ukat/blob/master/CITATION.cff) +- [ ] Do a final check if anything in [readme.MD](rhttps://github.com/UKRIN-MAPS/ukat/blob/master/README.md) needs updating +- [ ] The pull requests is from `rel/v[x.y.z]` to `master` +- [ ] All tests pass +- [ ] Merge `rel/v[x.y.z]` into `master` +- [ ] Merge `rel/v[x.y.z]` into `dev` +- [ ] Create a tag on the merge commit on master with the same version number as the release and push to upstream + +### Post Pull Request Checks +- [ ] Close any issues from the milestone that didn't automatically close on merge +- [ ] Close the milestone itself +- [ ] Check the new version has appeared in the [releases list](https://github.com/UKRIN-MAPS/ukat/releases) +- [ ] Check the new version has appeared on [PyPI](https://pypi.org/project/ukat/) +- [ ] Check a now DOI has been minted on [Zenodo](https://zenodo.org/account/settings/github/repository/UKRIN-MAPS/ukat) \ No newline at end of file diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE/PULL_REQUEST_TEMPLATE.md similarity index 68% rename from .github/PULL_REQUEST_TEMPLATE.md rename to .github/PULL_REQUEST_TEMPLATE/PULL_REQUEST_TEMPLATE.md index 56470c24..352ae31c 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE/PULL_REQUEST_TEMPLATE.md @@ -6,11 +6,14 @@ Describe the big picture of your changes here to communicate to the maintainers - [ ] I have read and followed the [CONTRIBUTING](.github/CONTRIBUTING.md) document - [ ] This pull request is from and to the dev branch +- [ ] I have added tests that demonstrate the feature/fix works - [ ] I have added necessary documentation (if appropriate) - [ ] I have updated documentation which becomes obsolete after my changes (if appropriate) +- [ ] I have added/updated a notebook to demonstrate the changes (if appropriate) - [ ] Files added follow the repository structure (if appropriate) If adding test data? - [ ] Data is anonymised - [ ] Ensure imaging data is in NIfTI format and was converted using [`d2n`](https://github.com/UKRIN-MAPS/d2n) -- [ ] Update the [`data/README.md`](data/README.md) file with info about the origin of the data +- [ ] Update the [`data/README.md`](data/README.md) file in both `ukat/data/` and `ukat/data/contrast` with info about the origin of the data +- [ ] Added tests for the new functions in `data.fetch.py` diff --git a/.github/workflows/python_CI.yml b/.github/workflows/python_CI.yml index 065e8611..2430b90a 100644 --- a/.github/workflows/python_CI.yml +++ b/.github/workflows/python_CI.yml @@ -10,14 +10,18 @@ on: pull_request: branches: [ master, dev ] +env: + NUMBA_DISABLE_JIT: 1 + jobs: build: runs-on: ${{ matrix.os }} strategy: + fail-fast: false matrix: os: [ubuntu-latest, windows-latest] - python-version: [3.8, 3.9, "3.10"] + python-version: [3.8, 3.9, "3.10", "3.11"] steps: - uses: actions/checkout@v2 @@ -40,9 +44,8 @@ jobs: run: | pytest --cov-report=xml --cov=ukat - name: Upload coverage to Codecov - uses: codecov/codecov-action@v2 + uses: codecov/codecov-action@v3 with: - token: ${{ secrets.CODECOV_TOKEN }} env_vars: OS,PYTHON fail_ci_if_error: true verbose: false diff --git a/CHANGELOG.md b/CHANGELOG.md index c327de7e..a041fbd6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,18 @@ +## [0.7.0] - 2023-09-07 + +### Added +* T2StimFit - A method of accounting for stimulated echoes when performing T2 mapping #207, #209 +* R-Squared values for curve fitting maps #198 #205 +* New PR template for releases #205 #210 + +### Changed +* `ukat` is now tested against Python 3.11 +* Dependencies are now a little less strict for some packages + +### Fixed +* Mapping should now scale better over large images/multiple cores #165 #205 +* Quite a lot of PEP8 formatting issues + ## [0.6.5] - 2023-02-17 ### Added diff --git a/CITATION.cff b/CITATION.cff index e733f9a3..1fad8fb2 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -17,7 +17,7 @@ authors: given-names: "Susan T" orcid: "https://orcid.org/0000-0003-0903-7507" title: "UKRIN Kidney Analysis Toolbox" -version: 0.6.5 +version: 0.7.0 doi: 10.5281/zenodo.4742470 -date-released: 2023-02-17 +date-released: 2023-09-07 url: "https://github.com/UKRIN-MAPS/ukat" \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 81af9459..5287ff1d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,14 @@ -dipy~=1.6.0 -matplotlib~=3.7.0 -nibabel~=5.0.1 -notebook~=6.5.1 -numpy~=1.24.1 -pandas~=1.5.1 -renalsegmentor~=1.3.6 -scikit-image~=0.19.1 -scipy~=1.10.0 -scikit-learn~=1.2.1 -tabulate~=0.9.0 -tqdm~=4.64.1 +dipy>=1.6.0 +matplotlib>=3.7.0 +nibabel>=5.0.1 +notebook>=6.5.1 +numba>=0.55.1, <0.58 +numpy>=1.24.1, <1.26 +pandas>=2.0.0 +pathos>=0.3.0 +renalsegmentor>=1.3.5 +scikit-image>=0.20.0, <0.22 +scipy>=1.9.1, <1.12 +scikit-learn~=1.2.1 # Changing versions is known to cause changes in iSNR results +tabulate>=0.9.0 +tqdm>=4.64.1 diff --git a/setup.py b/setup.py index e4336cf9..99910293 100644 --- a/setup.py +++ b/setup.py @@ -10,7 +10,7 @@ setup( name="ukat", - version="0.6.5", + version="0.7.0", description="UKRIN Kidney Analysis Toolbox", long_description=long_description, long_description_content_type="text/markdown", @@ -32,6 +32,7 @@ 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11', 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', ], ) diff --git a/tutorials/segmentation.ipynb b/tutorials/segmentation.ipynb index ba40d64f..49c10e20 100644 --- a/tutorials/segmentation.ipynb +++ b/tutorials/segmentation.ipynb @@ -17,7 +17,7 @@ "cell_type": "code", "source": [ "import os\n", - "import numpy as np\n", + "\n", "import matplotlib.pyplot as plt\n", "\n", "from ukat.data import fetch\n", @@ -35,6 +35,10 @@ "collapsed": false, "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2023-06-29T15:26:25.006295800Z", + "start_time": "2023-06-29T15:24:59.886272Z" } }, "execution_count": 1, @@ -60,6 +64,13 @@ "cell_type": "code", "execution_count": 2, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2/2 [==============================] - 3s 827ms/step\n" + ] + }, { "data": { "text/plain": "(-0.5, 255.5, -0.5, 255.5)" @@ -70,12 +81,10 @@ }, { "data": { - "text/plain": "
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\n" 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" }, + "metadata": {}, "output_type": "display_data" } ], @@ -105,6 +114,10 @@ "collapsed": false, "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2023-06-29T15:26:33.154156600Z", + "start_time": "2023-06-29T15:26:25.010295100Z" } } }, @@ -122,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "outputs": [ { "name": "stdout", @@ -137,18 +150,16 @@ "data": { "text/plain": "(-0.5, 255.5, -0.5, 255.5)" }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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" }, + "metadata": {}, "output_type": "display_data" } ], @@ -182,6 +193,10 @@ "collapsed": false, "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2023-06-29T15:26:33.554929400Z", + "start_time": "2023-06-29T15:26:33.152158500Z" } } }, @@ -200,32 +215,28 @@ }, { "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR! Session/line number was not unique in database. History logging moved to new session 203\n" - ] - } - ], + "execution_count": 4, + "outputs": [], "source": [ - "segmentation.to_nifti(output_directory=OUTPUT_DIR, base_file_name='Sub_01', maps=['mask', 'left', 'right', 'individual'])\n" + "segmentation.to_nifti(output_directory=OUTPUT_DIR, base_file_name='Sub_01', maps=['mask', 'left', 'right', 'individual'])" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" + }, + "ExecuteTime": { + "end_time": "2023-06-29T15:26:33.644878800Z", + "start_time": "2023-06-29T15:26:33.555928800Z" } } } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "name": "python3", "language": "python", - "name": "python3" + "display_name": "Python 3 (ipykernel)" }, "language_info": { "codemirror_mode": { @@ -242,4 +253,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/tutorials/t2_calculation.ipynb b/tutorials/t2_calculation.ipynb index 8f02ee0f..7a6931f7 100644 --- a/tutorials/t2_calculation.ipynb +++ b/tutorials/t2_calculation.ipynb @@ -4,25 +4,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# $T_2$ map calculation tutorial" + "# $T_2$ map calculation tutorial\n", + "There are two methods of calculating $T_2$ maps in `ukat`, a standard curve fitting approach (with multiple models\n", + "available) or the `StimFit` approach that models stimulated echoes in the refocusing pulse train using extended phase graphs\n", + " as in [Marc Lebel R. StimFit: A Toolbox for Robust T2 Mapping with Stimulated Echo Compensation. In: Proc. Intl. Soc. Mag.\n", + " Reson. Med. 20. Melbourne; 2012:2558](https://archive.ismrm.org/2012/2558.html).\n", + "\n", + "In this tutorial we'll demonstrate both methods on example renal data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "## Curve Fitting Methods\n", "Start by importing the required libraries and defining some settings:" ] }, { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "is_executing": true + }, "outputs": [], "source": [ "import os\n", - "import nibabel as nib\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from ukat.data import fetch\n", @@ -72,9 +79,9 @@ "outputs": [], "source": [ "# Fetch test data\n", - "image, affine, te = fetch.t2_philips(1)\n", + "image, affine, te = fetch.t2_philips(2)\n", "te = te * 1000 # convert TE to ms\n", - "mask = image[..., -1] > 50 # Generate a mask based on the signal intensity of the last echo" + "mask = image[..., 0] > 20000 # Generate a mask based on the signal intensity of the first echo" ] }, { @@ -90,7 +97,7 @@ "\n", "Multiple models are available to fit data to. `2p_exp` fits the data to equation $S = S_0 \\cdot e^{\\frac{-t}{T2}}$ and \n", "`3p_exp` fits the data to equation $S = S_0 \\cdot e^{\\frac{-t}{T2}}+b$ where $b$ is a baseline noise term comprising of \n", - "noise and very long $T_2$ components. Additionally a noise threshold can be specified to ommit late echos when the signal \n", + "noise and very long $T_2$ components. Additionally, a noise threshold can be specified to ommit late echos when the signal\n", "has recovered to the noise floor from the fitting process.\n", "\n", "![t2_fitting_methods.png](attachment:t2_fitting_methods.png)\n", @@ -102,26 +109,15 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 33946/33946 [05:38<00:00, 100.24it/s]\n", - "100%|██████████| 33946/33946 [06:09<00:00, 91.81it/s] \n", - "D:\\ppxad2\\ownCloud\\University\\Renal Imaging\\ukat\\ukat\\mapping\\t2.py:257: RuntimeWarning: divide by zero encountered in reciprocal\n", - " return np.reciprocal(self.t2_map)\n" - ] - } - ], + "outputs": [], "source": [ - "# Initialise the mapping objects\n", + "# Initialise the mapping objects. This step can take a while as its calculating the T2 for every voxel in the image\n", "mapper_2p = T2(image, te, affine, mask, method='2p_exp')\n", "mapper_3p = T2(image, te, affine, mask, method='3p_exp')\n", "\n", "# Save output maps to Nifti\n", - "mapper_2p.to_nifti(output_directory=OUTPUT_DIR, base_file_name='two_param_fit', maps='all')\n", - "mapper_3p.to_nifti(output_directory=OUTPUT_DIR, base_file_name='three_param_fit', maps='all')" + "mapper_2p.to_nifti(output_directory=OUTPUT_DIR, base_file_name='two_param_curve_fit', maps='all')\n", + "mapper_3p.to_nifti(output_directory=OUTPUT_DIR, base_file_name='three_param_curve_fit', maps='all')" ] }, { @@ -137,54 +133,152 @@ "metadata": { "pycharm": { "name": "#%%\n" - } + }, + "scrolled": true }, "outputs": [ { "data": { - "text/plain": "
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\n" 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "fig, ax = plt.subplots(2, 2, figsize=(8, 6))\n", + "fig, ax = plt.subplots(2, 3, figsize=(12, 6))\n", "\n", "# Display a central slice of the two parameter T2 map\n", - "im = ax[0, 0].imshow(np.rot90(mapper_2p.t2_map[:, :, 2]), cmap='inferno', clim=(75, 150))\n", + "im = ax[0, 0].imshow(mapper_2p.t2_map[:, :, 2].T, origin='lower', cmap='inferno', clim=(75, 175))\n", "cb = fig.colorbar(im, ax=ax[0, 0])\n", "cb.set_label('Two Parameter $T_2$ (ms)')\n", "ax[0, 0].axis('off')\n", "\n", "# Display a central slice of the two parameter M0 map\n", - "im = ax[0, 1].imshow(np.rot90(mapper_2p.m0_map[:, :, 2]), cmap='viridis', clim=(0, 2000))\n", + "im = ax[0, 1].imshow(mapper_2p.m0_map[:, :, 2].T, origin='lower', cmap='viridis', clim=(0, 200000))\n", "cb = fig.colorbar(im, ax=ax[0, 1])\n", "cb.set_label('Two Parameter $M_0$')\n", "ax[0, 1].axis('off')\n", "\n", + "# Display a central slice of the two parameter r-squared map\n", + "im = ax[0, 2].imshow(mapper_2p.r2[:, :, 2].T, origin='lower', cmap='hot', clim=(0.95, 1))\n", + "cb = fig.colorbar(im, ax=ax[0, 2])\n", + "cb.set_label('Two Parameter $R^2$')\n", + "ax[0, 2].axis('off')\n", + "\n", "# Display a central slice of the three parameter T2 map\n", - "im = ax[1, 0].imshow(np.rot90(mapper_3p.t2_map[:, :, 2]), cmap='inferno', clim=(75, 150))\n", + "im = ax[1, 0].imshow(mapper_3p.t2_map[:, :, 2].T, origin='lower', cmap='inferno', clim=(75, 175))\n", "cb = fig.colorbar(im, ax=ax[1, 0])\n", - "cb.set_label('Three Parameter $T_2$ (ms))')\n", + "cb.set_label('Three Parameter $T_2$ (ms)')\n", "ax[1, 0].axis('off')\n", "\n", "# Display a central slice of the three parameter M0 map\n", - "im = ax[1, 1].imshow(np.rot90(mapper_3p.m0_map[:, :, 2]), cmap='viridis', clim=(0, 2000))\n", + "im = ax[1, 1].imshow(mapper_3p.m0_map[:, :, 2].T, origin='lower', cmap='viridis', clim=(0, 200000))\n", "cb = fig.colorbar(im, ax=ax[1, 1])\n", "cb.set_label('Three Parameter $M_0$')\n", "ax[1, 1].axis('off')\n", "\n", + "# Display a central slice of the two parameter r-squared map\n", + "im = ax[1, 2].imshow(mapper_3p.r2[:, :, 2].T, origin='lower', cmap='hot', clim=(0.95, 1))\n", + "cb = fig.colorbar(im, ax=ax[1, 2])\n", + "cb.set_label('Three Parameter $R^2$')\n", + "ax[1, 2].axis('off')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## StimFit\n", + "Now we'll use the stimulated echo fitting approach on the same data. This method uses information about the excitation and refocusing RF pulses to minimise the confounding effects of stimulated echoes in the multi-echo pulse train and estimate $T_2$, $B_1^+$ and $M_0$. For the UKRIN-MAPS $T_2$ mapping sequence, the RF pulse profiles and default values are all easily accessible via the `ukrin_vendor` property, however adapted sequences should have these parameters input to the model manually." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from ukat.mapping.t2_stimfit import StimFitModel, T2StimFit\n", + "model = StimFitModel(mode='selective', ukrin_vendor='philips')\n", + "mapper_stimfit = T2StimFit(image, affine, model, mask)\n", + "mapper_stimfit.to_nifti(output_directory=OUTPUT_DIR, base_file_name='stimfit')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets display the central slice of the generated maps." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 3, figsize=(12, 6))\n", + "\n", + "# Display a central slice of the two parameter T2 map\n", + "im = ax[0, 0].imshow(mapper_2p.t2_map[:, :, 2].T, origin='lower', cmap='inferno', clim=(75, 175))\n", + "cb = fig.colorbar(im, ax=ax[0, 0])\n", + "cb.set_label('Two Parameter $T_2$ (ms)')\n", + "ax[0, 0].axis('off')\n", + "\n", + "# Display a central slice of the two parameter M0 map\n", + "im = ax[0, 1].imshow(mapper_2p.m0_map[:, :, 2].T, origin='lower', cmap='viridis', clim=(0, 200000))\n", + "cb = fig.colorbar(im, ax=ax[0, 1])\n", + "cb.set_label('Two Parameter $M_0$')\n", + "ax[0, 1].axis('off')\n", + "\n", + "# Display a central slice of the two parameter r-squared map\n", + "im = ax[0, 2].imshow(mapper_2p.r2[:, :, 2].T, origin='lower', cmap='hot', clim=(0.95, 1))\n", + "cb = fig.colorbar(im, ax=ax[0, 2])\n", + "cb.set_label('Two Parameter $R^2$')\n", + "ax[0, 2].axis('off')\n", + "\n", + "# Display a central slice of the stimfit T2 map\n", + "im = ax[1, 0].imshow(mapper_stimfit.t2_map[:, :, 2].T, origin='lower', cmap='inferno', clim=(75, 175))\n", + "cb = fig.colorbar(im, ax=ax[1, 0])\n", + "cb.set_label('StimFit $T_2$ (ms)')\n", + "ax[1, 0].axis('off')\n", + "\n", + "# Display a central slice of the stimfit M0 map\n", + "im = ax[1, 1].imshow(mapper_stimfit.m0_map[:, :, 2].T, origin='lower', cmap='viridis', clim=(0, 1))\n", + "cb = fig.colorbar(im, ax=ax[1, 1])\n", + "cb.set_label('StimFit $M_0$')\n", + "ax[1, 1].axis('off')\n", + "\n", + "# Display a central slice of the stimfit B1 map\n", + "im = ax[1, 2].imshow(mapper_stimfit.b1_map[:, :, 2].T, origin='lower', cmap='hot', clim=(0.5, 1))\n", + "cb = fig.colorbar(im, ax=ax[1, 2])\n", + "cb.set_label('StimFit $B_1^+$')\n", + "ax[1, 2].axis('off')\n", + "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -198,9 +292,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.9" + "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/ukat/data/README.md b/ukat/data/README.md index 07003d34..9893837b 100644 --- a/ukat/data/README.md +++ b/ukat/data/README.md @@ -37,6 +37,9 @@ With the exception of datasets marked with :star:, all test data is from the tra ### T2 * `t2/philips_1`: not part of travelling kidney pilot study 2019, data acquired on 06 Aug 2020 in Nottingham +* `t2/philips_2`: 2021 Travelling kidney study Philips subject 6 +* `t2/ge_1`: 2021 Travelling kidney study GE subject 6 +* `t2/siemens_2`: 2021 Travelling kidney study Siemens subject 2 ### MT * `mt/philips`: Part of travelling kidney study 2021, data acquired on 05 July 2021 in Nottingham diff --git a/ukat/data/fetch.py b/ukat/data/fetch.py index 98c9cc43..1397025b 100644 --- a/ukat/data/fetch.py +++ b/ukat/data/fetch.py @@ -266,14 +266,41 @@ ['02f90f0fc8277e09144c21d3fc75a8b7'], doc='Downloading Philips T1W data') -fetch_t2_philips = _make_fetcher('fetch_t2_philips', - pjoin(ukat_home, 't2_philips'), - 'https://zenodo.org/record/4762380/files/', - ['philips_1.zip'], - ['philips_1.zip'], - ['a8adc351219339737b3f0a50404e2c54'], - unzip=True, - doc='Downloading Philips T2 data') +fetch_t2_ge_1 = _make_fetcher('fetch_t2_ge_1', + pjoin(ukat_home, 't2_ge_1'), + 'https://zenodo.org/record/8160807/files/', + ['ge_t2.zip'], + ['ge_t2.zip'], + ['164997465af0cb55c58022f8f8773b04'], + unzip=True, + doc='Downloading GE T2 data') + +fetch_t2_philips_1 = _make_fetcher('fetch_t2_philips_1', + pjoin(ukat_home, 't2_philips_1'), + 'https://zenodo.org/record/4762380/files/', + ['philips_1.zip'], + ['philips_1.zip'], + ['a8adc351219339737b3f0a50404e2c54'], + unzip=True, + doc='Downloading Philips T2 data') + +fetch_t2_philips_2 = _make_fetcher('fetch_t2_philips_2', + pjoin(ukat_home, 't2_philips_2'), + 'https://zenodo.org/record/8160764/files/', + ['philips_2.zip'], + ['philips_2.zip'], + ['5ce51450e37da30d562443ed03c23274'], + unzip=True, + doc='Downloading Philips T2 data') + +fetch_t2_siemens_1 = _make_fetcher('fetch_t2_siemens_1', + pjoin(ukat_home, 't2_siemens_1'), + 'https://zenodo.org/record/8160856/files/', + ['siemens_t2.zip'], + ['siemens_t2.zip'], + ['77b726b9b6c0ed61ffc5ff9f091d7de5'], + unzip=True, + doc='Downloading Siemens T2 data') fetch_t2star_ge = _make_fetcher('fetch_t2star_ge', pjoin(ukat_home, 't2star_ge'), @@ -415,8 +442,23 @@ def get_fnames(name): fnames = sorted(glob.glob(pjoin(folder, '*'))) return fnames - elif name == 't2_philips': - files, folder = fetch_t2_philips() + elif name == 't2_ge_1': + files, folder = fetch_t2_ge_1() + fnames = sorted(glob.glob(pjoin(folder, '*'))) + return fnames + + elif name == 't2_philips_1': + files, folder = fetch_t2_philips_1() + fnames = sorted(glob.glob(pjoin(folder, '*'))) + return fnames + + elif name == 't2_philips_2': + files, folder = fetch_t2_philips_2() + fnames = sorted(glob.glob(pjoin(folder, '*RespTrig_SE*'))) + return fnames + + elif name == 't2_siemens_1': + files, folder = fetch_t2_siemens_1() fnames = sorted(glob.glob(pjoin(folder, '*'))) return fnames @@ -665,7 +707,7 @@ def phase_contrast_left_philips(): velocity_encoding = 100 for file in fnames: if ((file.endswith(".nii.gz") and "_ph_" in file) or - file.endswith("_ph.nii.gz")): + file.endswith("_ph.nii.gz")): # Load NIfTI and only save the phase data data = nib.load(file) phase.append(np.squeeze(data.get_fdata())) @@ -705,7 +747,7 @@ def phase_contrast_right_philips(): for file in fnames: if ((file.endswith(".nii.gz") and "_ph_" in file) or - file.endswith("_ph.nii.gz")): + file.endswith("_ph.nii.gz")): # Load NIfTI and only save the phase data data = nib.load(file) phase.append(np.squeeze(data.get_fdata())) @@ -834,6 +876,60 @@ def t1w_volume_philips(): return image, data.affine +def t2_ge(dataset_id=1): + """Fetches t2/ge_{dataset_id} dataset + dataset_id : int + Number of the dataset to load: + - dataset_id = 1 to load "t2/ge_1" + Returns + ------- + numpy.ndarray + image data + numpy.ndarray + affine matrix for image data + numpy.ndarray + array of echo times, in seconds + """ + possible_dataset_ids = [1] + + if dataset_id not in possible_dataset_ids: + error_msg = f"`dataset_id` must be one of {possible_dataset_ids}" + raise ValueError(error_msg) + + # See README.md in ukat/data/t2 for information about the acquisition. + if dataset_id == 1: + fnames = get_fnames('t2_ge_1') + # Load magnitude data and corresponding echo times (in the orig) + magnitude = [] + echo_list = [] + for file in fnames: + + if file.endswith(".nii.gz"): + + # Load NIfTI + data = nib.load(file) + magnitude.append(data.get_fdata()) + + elif file.endswith(".json"): + + # Retrieve list of echo times in the original order + with open(file, 'r') as json_file: + hdr = json.load(json_file) + echo_list.append(hdr["EchoTime"]) + + # Move echo dimension to 4th dimension + magnitude = np.moveaxis(np.array(magnitude), 0, -1) + echo_list = np.array(echo_list) + + # Sort by increasing echo time + sort_idxs = np.argsort(echo_list) + echo_list = echo_list[sort_idxs] + magnitude = magnitude[:, :, :, sort_idxs] + affine = data.affine + + return magnitude, affine, echo_list + + def t2_philips(dataset_id=1): """Fetches t2/philips_{dataset_id} dataset dataset_id : int @@ -857,7 +953,93 @@ def t2_philips(dataset_id=1): # See README.md in ukat/data/t2 for information about the acquisition. if dataset_id == 1: - fnames = get_fnames('t2_philips') + fnames = get_fnames('t2_philips_1') + # Load magnitude data and corresponding echo times (in the orig) + magnitude = [] + echo_list = [] + for file in fnames: + + if file.endswith(".nii.gz"): + + # Load NIfTI + data = nib.load(file) + magnitude.append(data.get_fdata()) + + elif file.endswith(".json"): + + # Retrieve list of echo times in the original order + with open(file, 'r') as json_file: + hdr = json.load(json_file) + echo_list.append(hdr["EchoTime"]) + + # Move echo dimension to 4th dimension + magnitude = np.moveaxis(np.array(magnitude), 0, -1) + echo_list = np.array(echo_list) + + # Sort by increasing echo time + sort_idxs = np.argsort(echo_list) + echo_list = echo_list[sort_idxs] + magnitude = magnitude[:, :, :, sort_idxs] + affine = data.affine + + return magnitude, affine, echo_list + + elif dataset_id == 2: + fnames = get_fnames('t2_philips_2') + # Load magnitude data and corresponding echo times (in the orig) + magnitude = [] + echo_list = [] + for file in fnames: + + if file.endswith(".nii.gz"): + + # Load NIfTI + data = nib.load(file) + magnitude.append(data.get_fdata()) + + elif file.endswith(".json"): + + # Retrieve list of echo times in the original order + with open(file, 'r') as json_file: + hdr = json.load(json_file) + echo_list.append(hdr["EchoTime"]) + + # Move echo dimension to 4th dimension + magnitude = np.moveaxis(np.array(magnitude), 0, -1) + echo_list = np.array(echo_list) + + # Sort by increasing echo time + sort_idxs = np.argsort(echo_list) + echo_list = echo_list[sort_idxs] + magnitude = magnitude[:, :, :, sort_idxs] + affine = data.affine + + return magnitude, affine, echo_list + + +def t2_siemens(dataset_id=1): + """Fetches t2/siemens_{dataset_id} dataset + dataset_id : int + Number of the dataset to load: + - dataset_id = 1 to load "t2/siemens_1" + Returns + ------- + numpy.ndarray + image data + numpy.ndarray + affine matrix for image data + numpy.ndarray + array of echo times, in seconds + """ + possible_dataset_ids = [1] + + if dataset_id not in possible_dataset_ids: + error_msg = f"`dataset_id` must be one of {possible_dataset_ids}" + raise ValueError(error_msg) + + # See README.md in ukat/data/t2 for information about the acquisition. + if dataset_id == 1: + fnames = get_fnames('t2_siemens_1') # Load magnitude data and corresponding echo times (in the orig) magnitude = [] echo_list = [] diff --git a/ukat/data/t2/README.md b/ukat/data/t2/README.md index 6ebd3a07..ad6c2ace 100644 --- a/ukat/data/t2/README.md +++ b/ukat/data/t2/README.md @@ -2,4 +2,10 @@ ## Philips-specific information -* `t2/philips_1`: contains a dataset collected on subject on 06/08/2020 \ No newline at end of file +* `t2/philips_1`: contains a dataset collected on subject on 06/08/2020 + +The following datasets are collected from the same participant as part of the travelling kidney study. + +* `t2/philips_2`: 2021 Travelling kidney study Philips subject 6 +* `t2/ge_1`: 2021 Travelling kidney study GE subject 6 +* `t2/siemens_2`: 2021 Travelling kidney study Siemens subject 2 \ No newline at end of file diff --git a/ukat/data/tests/test_fetch.py b/ukat/data/tests/test_fetch.py index 32855b9f..72103100 100644 --- a/ukat/data/tests/test_fetch.py +++ b/ukat/data/tests/test_fetch.py @@ -243,6 +243,23 @@ def test_philips_t1w(self): assert len(np.shape(image)) == 3 assert np.shape(affine) == (4, 4) + def test_ge_t2(self): + # Test if the fetch function works + magnitude, affine, echo_times = fetch.t2_ge() + + # Check the format of the outputs + assert isinstance(magnitude, np.ndarray) + assert np.unique(np.isnan(magnitude)) != [True] + assert isinstance(affine, np.ndarray) + assert isinstance(echo_times, np.ndarray) + assert len(np.shape(magnitude)) == 4 + assert np.shape(affine) == (4, 4) + assert len(np.shape(echo_times)) == 1 + + # If an incorrect dataset_id is given + with pytest.raises(ValueError): + magnitude, affine, echo_times = fetch.t2_ge(2) + def test_philips_t2(self): # Test if the fetch function works magnitude, affine, echo_times = fetch.t2_philips(1) @@ -256,10 +273,39 @@ def test_philips_t2(self): assert np.shape(affine) == (4, 4) assert len(np.shape(echo_times)) == 1 + # Test if the fetch function works + magnitude, affine, echo_times = fetch.t2_philips(2) + + # Check the format of the outputs + assert isinstance(magnitude, np.ndarray) + assert np.unique(np.isnan(magnitude)) != [True] + assert isinstance(affine, np.ndarray) + assert isinstance(echo_times, np.ndarray) + assert len(np.shape(magnitude)) == 4 + assert np.shape(affine) == (4, 4) + assert len(np.shape(echo_times)) == 1 + # If an incorrect dataset_id is given with pytest.raises(ValueError): magnitude, affine, echo_times = fetch.t2_philips(3) + def test_siemens_t2(self): + # Test if the fetch function works + magnitude, affine, echo_times = fetch.t2_siemens() + + # Check the format of the outputs + assert isinstance(magnitude, np.ndarray) + assert np.unique(np.isnan(magnitude)) != [True] + assert isinstance(affine, np.ndarray) + assert isinstance(echo_times, np.ndarray) + assert len(np.shape(magnitude)) == 4 + assert np.shape(affine) == (4, 4) + assert len(np.shape(echo_times)) == 1 + + # If an incorrect dataset_id is given + with pytest.raises(ValueError): + magnitude, affine, echo_times = fetch.t2_siemens(2) + def test_ge_t2star(self): # Test if the fetch function works magnitude, affine, echo_times = fetch.t2star_ge() diff --git a/ukat/mapping/__init__.py b/ukat/mapping/__init__.py index 68cdd347..8de1ecf7 100644 --- a/ukat/mapping/__init__.py +++ b/ukat/mapping/__init__.py @@ -1 +1,8 @@ -from . import b0, diffusion, mtr, t1, t2, t2star +from . import b0, diffusion, mtr, t1, t2, t2_stimfit, t2star +from .b0 import B0 +from .diffusion import ADC, DTI +from .mtr import MTR +from .t1 import T1 +from .t2 import T2 +from .t2_stimfit import StimFitModel, T2StimFit +from .t2star import T2Star diff --git a/ukat/mapping/b0.py b/ukat/mapping/b0.py index 96fb7c54..adb8abf4 100644 --- a/ukat/mapping/b0.py +++ b/ukat/mapping/b0.py @@ -111,7 +111,7 @@ def __init__(self, pixel_array, echo_list, affine, mask=None, # B0 Map Offset Correction self.b0_map -= (np.round(mean_central_b0 / b0_offset_step)) * \ b0_offset_step - + # Mask B0 Map self.b0_map[np.squeeze(~self.mask)] = 0 else: diff --git a/ukat/mapping/diffusion.py b/ukat/mapping/diffusion.py index b9bfb9a9..8bb8055c 100644 --- a/ukat/mapping/diffusion.py +++ b/ukat/mapping/diffusion.py @@ -9,6 +9,7 @@ from dipy.core.gradients import gradient_table, unique_bvals_tolerance from dipy.reconst.dti import TensorModel +from sklearn.metrics import r2_score from tqdm import tqdm @@ -89,8 +90,15 @@ class ADC: ---------- adc : np.ndarray The estimated ADC in mm^2/s. + s0 : np.ndarray + The estimated S0. adc_err : np.ndarray The certainty in the fit of `adc` in mm^2/s. + s0_err : np.ndarray + The certainty in the fit of `s0`. + r2 : np.ndarray + The R-Squared value of the fit, values close to 1 indicate a good + fit, lower values indicate a poorer fit shape : tuple The shape of the ADC map. n_vox : int @@ -170,7 +178,8 @@ def __init__(self, pixel_array, affine, bvals, mask=None, ukrin_b=False): self.pixel_array_mean = self.__mean_over_directions__() - self.adc, self.adc_err = self.__fit__() + self.adc, self.s0, self.adc_err, self.s0_err, self.r2 = \ + self.__fit__() def __mean_over_directions__(self): """ @@ -193,7 +202,10 @@ def __mean_over_directions__(self): def __fit__(self): # Initialise maps adc_map = np.zeros(self.n_vox) + s0_map = np.zeros(self.n_vox) adc_err = np.zeros(self.n_vox) + s0_err = np.zeros(self.n_vox) + r2 = np.zeros(self.n_vox) mask = self.mask.flatten() signal = self.pixel_array_mean.reshape(-1, self.n_bvals) @@ -201,17 +213,24 @@ def __fit__(self): with tqdm(total=idx.size) as progress: for ind in idx: sig = signal[ind, :] - adc_map[ind], adc_err[ind] = \ + adc_map[ind], s0_map[ind], adc_err[ind], s0_err[ind], \ + r2[ind] = \ self.__fit_signal__(sig, self.u_bvals) progress.update(1) adc_map[adc_map < 0] = 0 + s0_map[adc_map < 0] = 0 adc_err[adc_map < 0] = 0 + s0_err[adc_map < 0] = 0 + r2[adc_map < 0] = 0 # Reshape results into raw data shape adc_map = adc_map.reshape(self.shape) + s0_map = s0_map.reshape(self.shape) adc_err = adc_err.reshape(self.shape) + s0_err = s0_err.reshape(self.shape) + r2 = r2.reshape(self.shape) - return adc_map, adc_err + return adc_map, s0_map, adc_err, s0_err, r2 @staticmethod def __fit_signal__(sig, bvals): @@ -219,12 +238,18 @@ def __fit_signal__(sig, bvals): popt, pvar = np.polyfit(bvals[sig > 0], np.log(sig[sig > 0]), 1, cov=True) adc = -popt[0] + s0 = np.exp(popt[1]) adc_err = np.sqrt(pvar[0, 0]) + s0_err = np.exp(np.sqrt(pvar[1, 1])) except np.linalg.LinAlgError: adc = 0 + s0 = 0 adc_err = 0 + s0_err = 0 - return adc, adc_err + fit_sig = adc_eq(bvals, adc, s0) + r2 = r2_score(sig, fit_sig) + return adc, s0, adc_err, s0_err, r2 def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps='all'): @@ -239,21 +264,34 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', Eg., base_file_name = 'Output' will result in 'Output.nii.gz'. maps : list or 'all', optional List of maps to save to NIFTI. This should either the string "all" - or a list of maps from ["adc", "adc_err", "mask"]. + or a list of maps from ["adc", "s0", "adc_err", "s0_err", + "r2", "mask"]. """ os.makedirs(output_directory, exist_ok=True) base_path = os.path.join(output_directory, base_file_name) if maps == 'all' or maps == ['all']: - maps = ['adc', 'adc_err', 'mask'] + maps = ['adc', 's0', 'adc_err', 's0_err', 'r2', 'mask'] if isinstance(maps, list): for result in maps: if result == 'adc' or result == 'adc_map': adc_nifti = nib.Nifti1Image(self.adc, affine=self.affine) nib.save(adc_nifti, base_path + '_adc_map.nii.gz') + elif result == 's0' or result == 's0_map': + s0_nifti = nib.Nifti1Image(self.s0, + affine=self.affine) + nib.save(s0_nifti, base_path + '_s0_map.nii.gz') elif result == 'adc_err' or result == 'adc_err_map': adc_err_nifti = nib.Nifti1Image(self.adc_err, affine=self.affine) nib.save(adc_err_nifti, base_path + '_adc_err.nii.gz') + elif result == 's0_err' or result == 's0_err_map': + s0_err_nifti = nib.Nifti1Image(self.s0_err, + affine=self.affine) + nib.save(s0_err_nifti, base_path + '_s0_err.nii.gz') + elif result == 'r2' or result == 'r2_map': + r2_nifti = nib.Nifti1Image(self.r2, + affine=self.affine) + nib.save(r2_nifti, base_path + '_r2.nii.gz') elif result == 'mask': mask_nifti = nib.Nifti1Image(self.mask.astype(np.uint16), affine=self.affine) @@ -264,6 +302,29 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', '"["adc", "adc_err", "mask"]".') +def adc_eq(bvals, adc, s0): + """ + The ADC equation. + + Parameters + ---------- + bvals : np.ndarray + The b-values used in the experiment in s/mm^2. + adc : float + The estimated ADC value in mm^2/s. + s0 : float + The estimated S0 value. + + Returns + ------- + signal : np.ndarray + The estimated signal values. + """ + with np.errstate(divide='ignore'): + signal = s0 * np.exp(-bvals * adc) + return signal + + class DTI: """ Attributes @@ -342,8 +403,8 @@ def __init__(self, pixel_array, affine, bvals, bvecs, mask=None, if bvecs.shape[1] != 3 and bvecs.shape[0] == 3: bvecs = bvecs.T warnings.warn(f'bvecs should be (N, 3). Because your bvecs array ' - 'is {bvecs.shape} it has been transposed to {' - 'bvecs.T.shape}.') + f'is {bvecs.shape} it has been transposed to ' + f'{bvecs.T.shape}.') assert (bvecs.shape[1] == 3) assert (pixel_array.shape[-1] == bvecs.shape[0]), 'Number of bvecs ' \ 'does not match ' \ diff --git a/ukat/mapping/fitting/__init__.py b/ukat/mapping/fitting/__init__.py new file mode 100644 index 00000000..66f35dab --- /dev/null +++ b/ukat/mapping/fitting/__init__.py @@ -0,0 +1 @@ +from .relaxation import Model, fit_image, fit_signal diff --git a/ukat/mapping/fitting/relaxation.py b/ukat/mapping/fitting/relaxation.py new file mode 100644 index 00000000..eb95d569 --- /dev/null +++ b/ukat/mapping/fitting/relaxation.py @@ -0,0 +1,178 @@ +import inspect +import numpy as np + +from pathos.pools import ProcessPool +from scipy.optimize import curve_fit +from sklearn.metrics import r2_score +from tqdm import tqdm + + +class Model: + def __init__(self, pixel_array, x, eq, mask=None, multithread=True): + """ + A template class for fitting models to pixel arrays. + + Parameters + ---------- + pixel_array : np.ndarray + An array containing the signal from each voxel with the last + dimension being the dependent variable axis + x : np.ndarray + An array containing the dependent variable e.g. time + eq : function + A function that takes the dependent variable as the first + argument and the parameters to fit as the remaining arguments + mask : np.ndarray, optional + A boolean mask of voxels to fit. Should be the shape of the desired + map rather than the raw data i.e. omit the dependent variable axis + multithread : bool, optional + Default True + If True, the fitting will be performed in parallel using all + available cores + """ + # Attributes that can be set from default inputs + self.pixel_array = pixel_array + self.map_shape = pixel_array.shape[:-1] + self.x = x + self.eq = eq + self.mask = mask + self.multithread = multithread + self.n_x = pixel_array.shape[-1] + self.n_params = self._get_n_params() + + # Placeholder attributes that will be overwritten by the child class + self.initial_guess = None + self.signal_list = None + self.x_list = None + self.p0_list = None + self.mask_list = None + + def generate_lists(self): + """ + Generate the lists of data, dependent variables, initial guesses and + masks to be used in the fitting process + """ + self.signal_list = self.pixel_array.reshape(-1, self.n_x).tolist() + self.x_list = [self.x] * len(self.signal_list) + self.p0_list = [self.initial_guess] * len(self.signal_list) + self.mask_list = self._get_mask_list() + + def _get_n_params(self): + """ + Get the number of parameters to fit + + Returns + ------- + n_params : int + The number of parameters to fit + """ + n_params = len(inspect.signature(self.eq).parameters) - 1 + return n_params + + def _get_mask_list(self): + """ + Get a list of masks to be used in the fitting process, if no mask + has been specified it will be a list of True i.e. all voxels will be + fit + + Returns + ------- + mask_list : list + A list of booleans indicating whether to fit a voxel or not + """ + if self.mask is None: + mask_list = [True] * len(self.signal_list) + return mask_list + else: + mask_list = self.mask.reshape(-1).tolist() + return mask_list + + +def fit_image(model): + """ + Fit an image to a relaxometry curve fitting model + + Parameters + ---------- + model : ukat.mapping.fitting.relaxation.Model + A model object containing the data and model to fit to + + Returns + ------- + popt_list : list + A list of nD arrays containing the fitted parameters + error_list : list + A list of nD arrays containing the error in the fitted parameters + r2 : np.ndarray + An nD array containing the R2 value of the fit + """ + if model.multithread: + with ProcessPool() as executor: + results = executor.map(fit_signal, + model.signal_list, + model.x_list, + model.p0_list, + model.mask_list, + [model] * len(model.signal_list)) + else: + results = list(tqdm(map(fit_signal, + model.signal_list, + model.x_list, + model.p0_list, + model.mask_list, + [model] * len(model.signal_list)), + total=len(model.signal_list))) + + popt_array = np.array([result[0] for result in results]) + popt_list = [popt_array[:, p].reshape(model.map_shape) for p in range( + model.n_params)] + error_array = np.array([result[1] for result in results]) + error_list = [error_array[:, p].reshape(model.map_shape) for p in range( + model.n_params)] + r2 = np.array([result[2] for result in results]).reshape(model.map_shape) + return popt_list, error_list, r2 + + +def fit_signal(sig, x, p0, mask, model): + """ + Fit a signal to a model + + Parameters + ---------- + sig : np.array + Numpy array containing the signal to fit + x : np.array + Numpy array containing the x values for the signal (e.g. TE) + p0 : np.array + Numpy array containing the initial guess for the parameters + mask : bool + A boolean indicating whether to fit the signal or not + model : Model + A Model object containing the model to fit to + + Returns + ------- + popt : np.array + Numpy array containing the fitted parameters + error : np.array + Numpy array containing the standard error of the fitted parameters + r2 : float + The R^2 value of the fit + """ + if mask is True: + try: + popt, pcov = curve_fit(model.eq, x, sig, p0=p0, + bounds=model.bounds) + fit_sig = model.eq(x, *popt) + r2 = r2_score(sig, fit_sig) + except (RuntimeError, ValueError): + popt = np.zeros(model.n_params) + pcov = np.zeros((model.n_params, model.n_params)) + r2 = -1E6 + else: + popt = np.zeros(model.n_params) + pcov = np.zeros((model.n_params, model.n_params)) + r2 = -1E6 + + error = np.sqrt(np.diag(pcov)) + return popt, error, r2 diff --git a/ukat/mapping/fitting/tests/__init__.py b/ukat/mapping/fitting/tests/__init__.py new file mode 100644 index 00000000..540f0ab9 --- /dev/null +++ b/ukat/mapping/fitting/tests/__init__.py @@ -0,0 +1 @@ +from . import test_relaxation diff --git a/ukat/mapping/fitting/tests/test_relaxation.py b/ukat/mapping/fitting/tests/test_relaxation.py new file mode 100644 index 00000000..59f4476f --- /dev/null +++ b/ukat/mapping/fitting/tests/test_relaxation.py @@ -0,0 +1,145 @@ +import numpy as np +import numpy.testing as npt + +from ukat.mapping.fitting import Model, fit_image, fit_signal + + +class TestModel: + pixel_array = np.zeros((10, 10, 3, 8)) + x = np.linspace(0, 1000, 8) + mask = np.ones((10, 10, 3), dtype=bool) + mask[:5] = False + + @staticmethod + def two_param_eq(x, a, b): + return a * x + b + + @staticmethod + def three_param_eq(x, a, b, c): + return a * x + (b * c) + + def test_init(self): + model = Model(self.pixel_array, self.x, self.two_param_eq, self.mask, + multithread=True) + assert model.map_shape == (10, 10, 3) + assert model.n_x == 8 + + def test_n_params(self): + model = Model(self.pixel_array, self.x, self.two_param_eq, self.mask, + multithread=True) + assert model.n_params == 2 + + model = Model(self.pixel_array, self.x, self.three_param_eq, self.mask, + multithread=True) + assert model.n_params == 3 + + def test_generate_lists(self): + model = Model(self.pixel_array, self.x, self.two_param_eq, self.mask, + multithread=True) + model.initial_guess = [1, 1] + model.generate_lists() + assert type(model.signal_list) == list + assert type(model.x_list) == list + assert type(model.p0_list) == list + assert type(model.mask_list) == list + + assert len(model.signal_list) == 300 + assert len(model.x_list) == 300 + assert len(model.p0_list) == 300 + assert len(model.mask_list) == 300 + + assert len(model.signal_list[0]) == 8 + assert len(model.x_list[0]) == 8 + assert len(model.p0_list[0]) == 2 + + model = Model(self.pixel_array, self.x, self.two_param_eq, + multithread=True) + model.initial_guess = [1, 1] + model.generate_lists() + assert type(model.mask_list) == list + assert len(model.mask_list) == 300 + assert model.mask_list[0] is True + + +class TestFitSignal: + x = np.arange(1, 9) + + @staticmethod + def linear_eq(x, m, c): + return m * x + c + + def test_fit_signal(self): + sig = np.array([1, 2, 3, 4, 5, 6, 7, 8]) + pixel_array = np.tile(sig, (10, 10, 3, 1)) + model = Model(pixel_array, self.x, self.linear_eq, + multithread=True) + model.initial_guess = [0.9, 0.9] + model.bounds = ([0, 0], [2, 2]) + model.generate_lists() + popt, error, r2 = fit_signal(sig, self.x, model.initial_guess, True, + model) + npt.assert_allclose(popt, [1, 0], rtol=1e-5, atol=1e4) + npt.assert_allclose(error, [0, 0], rtol=1e-5, atol=1e4) + npt.assert_almost_equal(r2, 1) + + def test_mask(self): + sig = np.array([1, 2, 3, 4, 5, 6, 7, 8]) + pixel_array = np.tile(sig, (10, 10, 3, 1)) + model = Model(pixel_array, self.x, self.linear_eq, + multithread=True) + model.initial_guess = [0.9, 0.9] + model.bounds = ([0, 0], [2, 2]) + model.generate_lists() + popt, error, r2 = fit_signal(sig, self.x, model.initial_guess, False, + model) + npt.assert_allclose(popt, [0, 0]) + npt.assert_allclose(error, [0, 0]) + npt.assert_almost_equal(r2, -1E6) + + +class TestFitImage: + x = np.arange(1, 9) + sig = np.array([1, 2, 3, 4, 5, 6, 7, 8]) + pixel_array = np.tile(sig, (10, 10, 3, 1)) + + @staticmethod + def linear_eq(x, m, c): + return m * x + c + + def test_single_threaded(self): + model = Model(self.pixel_array, self.x, self.linear_eq, + multithread=False) + model.initial_guess = [0.9, 0.9] + model.bounds = ([0, 0], [2, 2]) + model.generate_lists() + popt, error, r2 = fit_image(model) + + assert len(popt) == 2 + assert len(error) == 2 + + assert popt[0].shape == (10, 10, 3) + assert error[0].shape == (10, 10, 3) + assert r2.shape == (10, 10, 3) + + npt.assert_almost_equal(popt[0].mean(), 1, decimal=5) + npt.assert_almost_equal(error[0].mean(), 0, decimal=5) + npt.assert_almost_equal(r2.mean(), 1, decimal=5) + + def test_multi_threaded(self): + model = Model(self.pixel_array, self.x, self.linear_eq, + multithread=True) + model.initial_guess = [0.9, 0.9] + model.bounds = ([0, 0], [2, 2]) + model.generate_lists() + popt, error, r2 = fit_image(model) + + assert len(popt) == 2 + assert len(error) == 2 + + assert popt[0].shape == (10, 10, 3) + assert error[0].shape == (10, 10, 3) + assert r2.shape == (10, 10, 3) + + npt.assert_almost_equal(popt[0].mean(), 1, decimal=5) + npt.assert_almost_equal(error[0].mean(), 0, decimal=5) + npt.assert_almost_equal(r2.mean(), 1, decimal=5) diff --git a/ukat/mapping/mtr.py b/ukat/mapping/mtr.py index d8175778..f1c8713e 100644 --- a/ukat/mapping/mtr.py +++ b/ukat/mapping/mtr.py @@ -49,7 +49,7 @@ def __init__(self, pixel_array, affine, mask=None): 'dimension of the input ' \ 'pixel_array must be 2.' if np.sum(pixel_array[..., 1]) >= np.sum(pixel_array[..., 0]): - warnings.warn(f'The average intensity of the MT_ON image is more ' + warnings.warn('The average intensity of the MT_ON image is more ' 'than the average intensity of the MT_OFF image. ' 'This will lead to negative MTR values which is not ' 'usually desirable. Please check that you\'ve input ' @@ -69,7 +69,8 @@ def __init__(self, pixel_array, affine, mask=None): self.mt_on = np.squeeze(self.pixel_array[..., 1] * self.mask) # Magnetisation Transfer Ratio calculation self.mtr_map = np.squeeze(np.nan_to_num(((self.mt_off - self.mt_on) / - self.mt_off), posinf=0, neginf=0)) + self.mt_off), + posinf=0, neginf=0)) def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps='all'): diff --git a/ukat/mapping/resources/t2_stimfit/rf_pulses.py b/ukat/mapping/resources/t2_stimfit/rf_pulses.py new file mode 100644 index 00000000..6907baee --- /dev/null +++ b/ukat/mapping/resources/t2_stimfit/rf_pulses.py @@ -0,0 +1,261 @@ +import numpy as np + +ge_90 = np.array([0.000000000000000000e+00, + 0.000000000000000000e+00, + 0.000000000000000000e+00, + 0.000000000000000000e+00, + 0.000000000000000000e+00, + 0.000000000000000000e+00, + -1.266768749857178165e+02, + -3.124522490114486573e+04, + -9.531547336557127710e+04, + -9.721048020529435598e+04, + -1.179229383718024765e+05, + -1.456733869440756971e+05, + -1.721247993207764812e+05, + -1.952492802694011480e+05, + -2.129515095199028729e+05, + -2.212653870648781303e+05, + -2.176550316485261428e+05, + -1.986209674288319075e+05, + -1.608037579581205500e+05, + -1.015676326677684265e+05, + -1.764346979966967410e+04, + 9.146498080684282468e+04, + 2.305132406684918096e+05, + 3.992299908046545461e+05, + 5.973996433025186416e+05, + 8.234028000181980897e+05, + 1.076599458585872315e+06, + 1.353354095860138303e+06, + 1.648617102658187039e+06, + 1.960097276725076837e+06, + 2.279507916006019339e+06, + 2.598834868547144346e+06, + 2.914556274034465663e+06, + 3.215262450139994733e+06, + 3.492515551519702189e+06, + 3.743626426648607478e+06, + 3.955383802293300163e+06, + 4.122795531721453648e+06, + 4.245528126540713944e+06, + 4.312751237523826770e+06, + 4.325134271489643492e+06, + 4.287280772804215550e+06, + 4.193747129049188923e+06, + 4.051372835346973035e+06, + 3.868604124899462331e+06, + 3.644595798674946185e+06, + 3.390452242988429498e+06, + 3.116637709659715649e+06, + 2.824276558167716488e+06, + 2.525030573960933834e+06, + 2.228156430497014429e+06, + 1.934771772715587635e+06, + 1.653738064701012336e+06, + 1.391487375758768991e+06, + 1.147340323923531920e+06, + 9.273275685164701426e+05, + 7.332337711992086843e+05, + 5.631421786069724476e+05, + 4.191517663029067335e+05, + 3.006847090521778446e+05, + 2.252239997526510851e+05, + 1.585568372310573177e+05, + 2.501030593846463671e+04, + 1.223713984113714659e+01]) + +ge_180 = np.array([2.628067099684659042e+05, + 1.812536453020582558e+05, + 5.814370884872322495e+04, + -4.110320687328568602e+04, + -1.374178347772504785e+05, + -2.306666085725722369e+05, + -3.182438513767363620e+05, + -3.943806634919878561e+05, + -4.532441780498687876e+05, + -4.879111396010195022e+05, + -4.922629923736716155e+05, + -4.584898011221469496e+05, + -3.801620427679839777e+05, + -2.525084479537750303e+05, + -6.868080375989120512e+04, + 1.734608747904833872e+05, + 4.798435415184603771e+05, + 8.509221243205775972e+05, + 1.286536533087235410e+06, + 1.785914593650705181e+06, + 2.343766314660373144e+06, + 2.955048761526284739e+06, + 3.610983570142784156e+06, + 4.298010399946169928e+06, + 5.002087344977931120e+06, + 5.701459471400410868e+06, + 6.373684782939250581e+06, + 6.991767440126724541e+06, + 7.526621337772894651e+06, + 7.951718050802586600e+06, + 8.241118240750433877e+06, + 8.378189295735327527e+06, + 8.354026371893344447e+06, + 8.170212088494279422e+06, + 7.837891504246501252e+06, + 7.377730451304072514e+06, + 6.815066800326613709e+06, + 6.177579216060877778e+06, + 5.494161889734255150e+06, + 4.790092515738011338e+06, + 4.088859494925408624e+06, + 3.408608516213723458e+06, + 2.764577993263987359e+06, + 2.168364262965844013e+06, + 1.626976905518055893e+06, + 1.147140237310670083e+06, + 7.305744826368239010e+05, + 3.791304701537272194e+05, + 9.221733370468940120e+04, + -1.313390917462947546e+05, + -2.978557992391907610e+05, + -4.099621404116821941e+05, + -4.737573230025937082e+05, + -4.947706974138531368e+05, + -4.808528154712727410e+05, + -4.383707892266485142e+05, + -3.731866142973536626e+05, + -2.931676076587194111e+05, + -2.034967062652181776e+05, + -1.086659747288654326e+05, + -1.190326363416896857e+04, + 9.080151677670537902e+04, + 2.349733636560339364e+05, + -2.059012535436650978e+03]) + +philips_90 = np.array([6.600000000000000000e+01, + -2.642857142857142776e+02, + -6.307142857142857792e+02, + -1.032142857142857338e+03, + -1.458000000000000000e+03, + -1.902285714285714221e+03, + -2.353000000000000000e+03, + -2.800000000000000000e+03, + -3.227428571428571558e+03, + -3.625142857142856883e+03, + -3.974000000000000000e+03, + -4.260714285714285325e+03, + -4.471142857142856883e+03, + -4.582000000000000000e+03, + -4.591000000000000000e+03, + -4.463714285714286234e+03, + -4.206714285714286234e+03, + -3.794857142857142208e+03, + -3.225142857142857338e+03, + -2.491999999999999545e+03, + -1.580000000000000909e+03, + -5.100000000000000000e+02, + 7.482857142857126291e+02, + 2.157857142857139024e+03, + 3.728857142857144026e+03, + 5.440285714285713766e+03, + 7.275999999999996362e+03, + 9.221857142857144936e+03, + 1.124700000000000000e+04, + 1.333671428571428260e+04, + 1.545514285714285143e+04, + 1.757542857142857247e+04, + 1.966757142857142753e+04, + 2.170200000000000728e+04, + 2.364200000000000364e+04, + 2.547100000000000000e+04, + 2.713457142857142753e+04, + 2.863385714285714494e+04, + 2.992314285714285506e+04, + 3.099285714285714494e+04, + 3.182485714285714494e+04, + 3.239100000000000000e+04, + 3.271200000000000000e+04, + 3.273557142857142753e+04, + 3.250800000000000000e+04, + 3.197042857142856519e+04, + 3.124314285714286234e+04, + 3.024142857142856519e+04, + 2.900242857142856883e+04, + 2.757000000000000000e+04, + 2.593514285714285870e+04, + 2.415500000000000728e+04, + 2.224042857142856155e+04, + 2.022942857142856519e+04, + 1.815042857142856519e+04, + 1.603457142857142571e+04, + 1.391100000000000000e+04, + 1.181442857142857429e+04, + 9.765714285714289872e+03, + 7.798000000000007276e+03, + 5.929714285714278958e+03, + 4.179714285714280777e+03, + 2.184857142857139024e+03, + 2.660000000000000000e+02]) + +philips_180 = np.array([1.148000000000000000e+03, + 1.754761904761904816e+03, + 2.009746031746031804e+03, + 2.101238095238095411e+03, + 2.124507936507936392e+03, + 2.076793650793650613e+03, + 1.934238095238095184e+03, + 1.694222222222222399e+03, + 1.279492063492063608e+03, + 6.062857142857147892e+02, + -3.720476190476181273e+02, + -1.550380952380951157e+03, + -2.703190476190476147e+03, + -3.637793650793649249e+03, + -4.326333333333333030e+03, + -4.843904761904761472e+03, + -5.259507936507936392e+03, + -5.505158730158730577e+03, + -5.302714285714286234e+03, + -4.534444444444445253e+03, + -3.094619047619050434e+03, + -1.095000000000006821e+03, + 1.362269841269835979e+03, + 4.290158730158726030e+03, + 7.697095238095237619e+03, + 1.166841269841270150e+04, + 1.591801587301586005e+04, + 2.024557142857142026e+04, + 2.438911111111110949e+04, + 2.811252380952380918e+04, + 3.091819047619046614e+04, + 3.250747619047618718e+04, + 3.250747619047618718e+04, + 3.091819047619046614e+04, + 2.811252380952380918e+04, + 2.438966666666666424e+04, + 2.024557142857142026e+04, + 1.591874603174601907e+04, + 1.166809523809524035e+04, + 7.697000000000000000e+03, + 4.290158730158726030e+03, + 1.362349206349189672e+03, + -1.094666666666673564e+03, + -3.093619047619046796e+03, + -4.533444444444450710e+03, + -5.302285714285715585e+03, + -5.504174603174604272e+03, + -5.259507936507935483e+03, + -4.843904761904761472e+03, + -4.325555555555557476e+03, + -3.636428571428570194e+03, + -2.702142857142859157e+03, + -1.550380952380948429e+03, + -3.720476190476193779e+02, + 6.062857142857187682e+02, + 1.280492063492064290e+03, + 1.695222222222221490e+03, + 1.935238095238095866e+03, + 2.076793650793650613e+03, + 2.124507936507936392e+03, + 2.102238095238094957e+03, + 2.009920634920635166e+03, + 1.755761904761903224e+03, + 1.149000000000000000e+03]) diff --git a/ukat/mapping/t1.py b/ukat/mapping/t1.py index 9244b379..e1096bb3 100644 --- a/ukat/mapping/t1.py +++ b/ukat/mapping/t1.py @@ -1,10 +1,92 @@ -import concurrent.futures import nibabel as nib import numpy as np import os import warnings -from tqdm import tqdm -from scipy.optimize import curve_fit + +from . import fitting + + +class T1Model(fitting.Model): + def __init__(self, pixel_array, ti, parameters=2, mask=None, tss=0, + tss_axis=-2, multithread=True): + """ + A class containing the T1 fitting model + + Parameters + ---------- + pixel_array : np.ndarray + An array containing the signal from each voxel at each echo + time with the last dimension being time i.e. the array needed to + generate a 3D T1 map would have dimensions [x, y, z, TE]. + ti : np.ndarray + An array of the inversion times used for the last dimension of the + pixel_array. In milliseconds. + parameters : {2, 3}, optional + Default `2` + The number of parameters to fit the data to. A two parameter fit + will estimate S0 and T1 while a three parameter fit will also + estimate the inversion efficiency. + mask : np.ndarray, optional + A boolean mask of the voxels to fit. Should be the shape of the + desired T1 map rather than the raw data i.e. omit the time + dimension. + tss : float, optional + Default 0 + The temporal slice spacing is the delay between acquisition of + slices in a T1 map. Including this information means the + inversion time is correct for each slice in a multi-slice T1 + map. In milliseconds. + tss_axis : int, optional + Default -2 i.e. last spatial axis + The axis over which the temporal slice spacing is applied. This + axis is relative to the full 4D pixel array i.e. tss_axis=-1 + would be along the TI axis and would be meaningless. + If `pixel_array` is single slice (dimensions [x, y, TI]), + then this should be set to None. + multithread : bool, optional + Default True + If True, the fitting will be performed in parallel using all + available cores + """ + self.parameters = parameters + self.tss = tss + self.tss_axis = tss_axis + + if np.min(pixel_array) < 0: + self.mag_corr = True + else: + self.mag_corr = False + + if self.parameters == 2: + if self.mag_corr: + super().__init__(pixel_array, ti, two_param_eq, mask, + multithread) + else: + super().__init__(pixel_array, ti, two_param_abs_eq, mask, + multithread) + self.bounds = ([0, 0], [5000, 1000000000]) + self.initial_guess = [1000, 30000] + elif self.parameters == 3: + if self.mag_corr: + super().__init__(pixel_array, ti, three_param_eq, mask, + multithread) + else: + super().__init__(pixel_array, ti, three_param_abs_eq, mask, + multithread) + self.bounds = ([0, 0, 1], [5000, 1000000000, 2]) + self.initial_guess = [1000, 30000, 2] + else: + raise ValueError(f'Parameters can be 2 or 3 only. You specified ' + f'{parameters}.') + + self.generate_lists() + if self.tss != 0: + self._tss_correct_ti() + + def _tss_correct_ti(self): + slices = np.indices(self.map_shape)[self.tss_axis].ravel() + for ind, (ti, slice) in enumerate(zip(self.x_list, slices)): + self.x_list[ind] = np.array(ti) + self.tss * slice class T1: @@ -24,10 +106,16 @@ class T1: pulse and 2 represents a 180 degree inversion eff_err : np.ndarray The certainty in the fit of `eff` + r2 : np.ndarray + The R-Squared value of the fit, values close to 1 indicate a good + fit, lower values indicate a poorer fit shape : tuple The shape of the T1 map n_ti : int The number of TI used to calculate the map + n_vox : int + The number of voxels in the map i.e. the product of all dimensions + apart from TI """ def __init__(self, pixel_array, inversion_list, affine, tss=0, tss_axis=-2, @@ -71,8 +159,8 @@ def __init__(self, pixel_array, inversion_list, affine, tss=0, tss_axis=-2, molli : bool, optional Default False. Apply MOLLI corrections to T1. - multithread : bool, optional - Default True. + multithread : bool or 'auto', optional + Default 'auto'. If True, fitting will be distributed over all cores available on the node. If False, fitting will be carried out on a single thread. Multithreading is useful when calculating the T1 for a large @@ -81,18 +169,26 @@ def __init__(self, pixel_array, inversion_list, affine, tss=0, tss_axis=-2, amounts of data e.g. a mean T1 signal decay over a ROI when the overheads of multithreading are more of a hindrance than the increase in speed distributing the calculation would generate. + 'auto' attempts to apply multithreading where appropriate based + on the number of voxels being fit. """ + assert multithread is True \ + or multithread is False \ + or multithread == 'auto', f'multithreaded must be True,' \ + f'False or auto. You entered ' \ + f'{multithread}' self.pixel_array = pixel_array self.shape = pixel_array.shape[:-1] self.dimensions = len(pixel_array.shape) self.n_ti = pixel_array.shape[-1] + self.n_vox = np.prod(self.shape) self.affine = affine # Generate a mask if there isn't one specified if mask is None: self.mask = np.ones(self.shape, dtype=bool) else: - self.mask = mask + self.mask = mask.astype(bool) # Don't process any nan values self.mask[np.isnan(np.sum(pixel_array, axis=-1))] = False self.inversion_list = inversion_list @@ -104,6 +200,11 @@ def __init__(self, pixel_array, inversion_list, affine, tss=0, tss_axis=-2, self.tss = 0 self.parameters = parameters self.molli = molli + if multithread == 'auto': + if self.n_vox > 20: + multithread = True + else: + multithread = False self.multithread = multithread # Some sanity checks @@ -122,160 +223,43 @@ def __init__(self, pixel_array, inversion_list, affine, tss=0, tss_axis=-2, warnings.warn('MOLLI requires a three parameter fit, ' 'using parameters=3.') + # Fit Data + fitting_model = T1Model(self.pixel_array, self.inversion_list, + self.parameters, self.mask, self.tss, + self.tss_axis, self.multithread) + popt, error, r2 = fitting.fit_image(fitting_model) + self.t1_map = popt[0] + self.m0_map = popt[1] + self.t1_err = error[0] + self.m0_err = error[1] + self.r2 = r2 - # Initialise output attributes - self.t1_map = np.zeros(self.shape) - self.t1_err = np.zeros(self.shape) - self.m0_map = np.zeros(self.shape) - self.m0_err = np.zeros(self.shape) - self.eff_map = np.zeros(self.shape) - self.eff_err = np.zeros(self.shape) - - # Fit data - if self.parameters == 2: - self.t1_map, self.t1_err, self.m0_map, self.m0_err = self.__fit__() - elif self.parameters == 3: - self.t1_map, self.t1_err, self.m0_map, self.m0_err, \ - self.eff_map, self.eff_err = self.__fit__() - else: - raise ValueError('Parameters can be 2 or 3 only. You specified ' - '{}'.format(self.parameters)) + if self.parameters == 3: + self.eff_map = popt[2] + self.eff_err = error[2] + + # Filter values that are very close to models upper bounds of T1 or + # M0 out. Not filtering based on eff as this should ideally be at + # the upper bound! + threshold = 0.999 # 99.9% of the upper bound + bounds_mask = ((self.t1_map > fitting_model.bounds[1][0] * threshold) | + (self.m0_map > fitting_model.bounds[1][1] * threshold)) + self.t1_map[bounds_mask] = 0 + self.m0_map[bounds_mask] = 0 + self.t1_err[bounds_mask] = 0 + self.m0_err[bounds_mask] = 0 + self.r2[bounds_mask] = 0 + if self.parameters == 3: + self.eff_map[bounds_mask] = 0 + self.eff_err[bounds_mask] = 0 + # Do MOLLI correction if self.molli: correction_factor = (self.m0_map * self.eff_map) / self.m0_map - 1 percentage_error = self.t1_err / self.t1_map self.t1_map = np.nan_to_num(self.t1_map * correction_factor) self.t1_err = np.nan_to_num(self.t1_map * percentage_error) - def __fit__(self): - n_vox = np.prod(self.shape) - # Initialise maps - t1_map = np.zeros(n_vox) - m0_map = np.zeros(n_vox) - t1_err = np.zeros(n_vox) - m0_err = np.zeros(n_vox) - if self.parameters == 3: - eff_map = np.zeros(n_vox) - eff_err = np.zeros(n_vox) - mask = self.mask.flatten() - signal = self.pixel_array.reshape(-1, self.n_ti) - slices = np.indices(self.shape)[self.tss_axis].ravel() - # Get indices of voxels to process - idx = np.argwhere(mask).squeeze() - - # Multithreaded method - if self.multithread: - with concurrent.futures.ProcessPoolExecutor() as pool: - with tqdm(total=idx.size) as progress: - futures = [] - - for ind in idx: - ti_slice_corrected = self.inversion_list + \ - slices[ind] * self.tss - future = pool.submit(self.__fit_signal__, - signal[ind, :], - ti_slice_corrected, - self.parameters) - future.add_done_callback(lambda p: progress.update()) - futures.append(future) - - results = [] - for future in futures: - result = future.result() - results.append(result) - - if self.parameters == 2: - t1_map[idx], t1_err[idx], \ - m0_map[idx], m0_err[idx] = [np.array(row) - for row in zip(*results)] - elif self.parameters == 3: - t1_map[idx], t1_err[idx], \ - m0_map[idx], m0_err[idx], \ - eff_map[idx], eff_err[idx] = [np.array(row) - for row in zip(*results)] - - # Single threaded method - else: - with tqdm(total=idx.size) as progress: - for ind in idx: - sig = signal[ind, :] - ti_slice_corrected = self.inversion_list + \ - slices[ind] * self.tss - if self.parameters == 2: - t1_map[ind], t1_err[ind], \ - m0_map[ind], m0_err[ind] = \ - self.__fit_signal__(sig, - ti_slice_corrected, - self.parameters) - elif self.parameters == 3: - t1_map[ind], t1_err[ind], \ - m0_map[ind], m0_err[ind], \ - eff_map[ind], eff_err[ind] = \ - self.__fit_signal__(sig, - ti_slice_corrected, - self.parameters) - progress.update(1) - - # Reshape results to raw data shape - t1_map = t1_map.reshape(self.shape) - m0_map = m0_map.reshape(self.shape) - t1_err = t1_err.reshape(self.shape) - m0_err = m0_err.reshape(self.shape) - - if self.parameters == 2: - return t1_map, t1_err, m0_map, m0_err - - elif self.parameters == 3: - eff_map = eff_map.reshape(self.shape) - eff_err = eff_err.reshape(self.shape) - return t1_map, t1_err, m0_map, m0_err, eff_map, eff_err - - def __fit_signal__(self, sig, t, parameters): - - # Initialise parameters and specify equation to fit to - if parameters == 2: - bounds = ([0, 0], [5000, 1000000000]) - initial_guess = [1000, 30000] - if sig.min() >= 0: - eq = two_param_abs_eq - else: - eq = two_param_eq - elif parameters == 3: - bounds = ([0, 0, 1], [5000, 1000000000, 2]) - initial_guess = [1000, 30000, 2] - if sig.min() >= 0: - eq = three_param_abs_eq - else: - eq = three_param_eq - - # Fit data to equation - try: - popt, pcov = curve_fit(eq, t, sig, - p0=initial_guess, bounds=bounds) - except RuntimeError: - popt = np.zeros(self.parameters) - pcov = np.zeros((self.parameters, self.parameters)) - - # Extract fits and errors from result variable - if popt[0] < bounds[1][0] - 1: - t1 = popt[0] - m0 = popt[1] - err = np.sqrt(np.diag(pcov)) - t1_err = err[0] - m0_err = err[1] - if self.parameters == 3: - eff = popt[2] - eff_err = err[2] - else: - t1, m0, t1_err, m0_err = 0, 0, 0, 0 - if self.parameters == 3: - eff, eff_err = 0, 0 - - if self.parameters == 2: - return t1, t1_err, m0, m0_err - elif self.parameters == 3: - return t1, t1_err, m0, m0_err, eff, eff_err - def r1_map(self): """ Generates the R1 map from the T1 map output by initialising this @@ -291,7 +275,10 @@ def r1_map(self): An array containing the R1 map generated by the function with R1 measured in ms. """ - return np.nan_to_num(np.reciprocal(self.t1_map), posinf=0, neginf=0) + with np.errstate(divide='ignore'): + r1_map = np.nan_to_num(np.reciprocal(self.t1_map), posinf=0, + neginf=0) + return r1_map def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps='all'): @@ -307,13 +294,13 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps : list or 'all', optional List of maps to save to NIFTI. This should either the string "all" or a list of maps from ["t1", "t1_err", "m0", "m0_err", "eff", - "eff_err", "r1", "mask"] + "eff_err", "r1", "r2", "mask"] """ os.makedirs(output_directory, exist_ok=True) base_path = os.path.join(output_directory, base_file_name) if maps == 'all' or maps == ['all']: maps = ['t1', 't1_err', 'm0', 'm0_err', 'eff', 'eff_err', 'r1_map', - 'mask'] + 'r2', 'mask'] if isinstance(maps, list): for result in maps: if result == 't1' or result == 't1_map': @@ -343,6 +330,10 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', r1_nifti = nib.Nifti1Image(T1.r1_map(self), affine=self.affine) nib.save(r1_nifti, base_path + '_r1_map.nii.gz') + elif result == 'r2': + r2_nifti = nib.Nifti1Image(self.r2, + affine=self.affine) + nib.save(r2_nifti, base_path + '_r2.nii.gz') elif result == 'mask': mask_nifti = nib.Nifti1Image(self.mask.astype(np.uint16), affine=self.affine) @@ -374,7 +365,9 @@ def two_param_abs_eq(t, t1, m0): ------- signal: ndarray """ - return np.abs(m0 * (1 - 2 * np.exp(-t / t1))) + with np.errstate(divide='ignore'): + signal = np.abs(m0 * (1 - 2 * np.exp(-t / t1))) + return signal def two_param_eq(t, t1, m0): @@ -395,7 +388,9 @@ def two_param_eq(t, t1, m0): ------- signal: ndarray """ - return m0 * (1 - 2 * np.exp(-t / t1)) + with np.errstate(divide='ignore'): + signal = m0 * (1 - 2 * np.exp(-t / t1)) + return signal def three_param_abs_eq(t, t1, m0, eff): @@ -419,7 +414,9 @@ def three_param_abs_eq(t, t1, m0, eff): ------- signal: ndarray """ - return np.abs(m0 * (1 - eff * np.exp(-t / t1))) + with np.errstate(divide='ignore'): + signal = np.abs(m0 * (1 - eff * np.exp(-t / t1))) + return signal def three_param_eq(t, t1, m0, eff): @@ -443,7 +440,9 @@ def three_param_eq(t, t1, m0, eff): ------- signal: ndarray """ - return m0 * (1 - eff * np.exp(-t / t1)) + with np.errstate(divide='ignore'): + signal = m0 * (1 - eff * np.exp(-t / t1)) + return signal def magnitude_correct(pixel_array): diff --git a/ukat/mapping/t2.py b/ukat/mapping/t2.py index af156235..7e0fae2d 100644 --- a/ukat/mapping/t2.py +++ b/ukat/mapping/t2.py @@ -1,9 +1,77 @@ import os + import nibabel as nib import numpy as np -import concurrent.futures -from tqdm import tqdm -from scipy.optimize import curve_fit + +from . import fitting + + +class T2Model(fitting.Model): + def __init__(self, pixel_array, te, method='2p_exp', mask=None, + multithread=True): + """ + A class containing the T2 fitting model + + Parameters + ---------- + pixel_array : np.ndarray + An array containing the signal from each voxel at each echo + time with the last dimension being time i.e. the array needed to + generate a 3D T2 map would have dimensions [x, y, z, TE]. + te : np.ndarray + An array of the echo times used for the last dimension of the + pixel_array. In milliseconds. + method : {'2p_exp', '3p_exp'}, optional + Default '2p_exp' + The model the data is fit to. 2p_exp uses a two parameter + exponential model (S = S0 * exp(-t / T2)) whereas 3p_exp uses a + three parameter exponential model (S = S0 * exp(-t / T2) + b) to + fit for noise/very long T2 components of the signal. + mask : np.ndarray, optional + A boolean mask of the voxels to fit. Should be the shape of the + desired T2 map rather than the raw data i.e. omit the time + dimension. + multithread : bool, optional + Default True + If True, the fitting will be performed in parallel using all + available cores + """ + self.method = method + + if self.method == '2p_exp': + super().__init__(pixel_array, te, two_param_eq, mask, multithread) + self.bounds = ([0, 0], [1000, 100000000]) + self.initial_guess = [20, 10000] + elif self.method == '3p_exp': + super().__init__(pixel_array, te, three_param_eq, mask, + multithread) + self.bounds = ([0, 0, 0], [1000, 100000000, 1000000]) + self.initial_guess = [20, 10000, 500] + + self.generate_lists() + + def threshold_noise(self, threshold=0): + """ + Remove voxel values below a certain threshold from the fitting + process, useful if long echo times have been collected and thus + thermal noise is being measured below a certain threshold rather + than the T2 decay. + + Parameters + ---------- + threshold : float, optional + Default 0 + The threshold below which to remove values + """ + for ind, (sig, te, p0) in enumerate(zip(self.signal_list, + self.x_list, + self.p0_list)): + self.signal_list[ind] = np.array( + [x for (x, b) in zip(sig, np.array(sig) > threshold) if b]) + self.x_list[ind] = np.array( + [x for (x, b) in zip(te, np.array(sig) > threshold) if b]) + self.p0_list[ind] = np.array( + [x for (x, b) in zip(p0, np.array(sig) > threshold) if b]) class T2: @@ -18,6 +86,9 @@ class T2: The estimated M0 values m0_err : np.ndarray The certainty in the fit of `m0` + r2 : np.ndarray + The R-Squared value of the fit, values close to 1 indicate a good + fit, lower values indicate a poorer fit shape : tuple The shape of the T2 map n_te : int @@ -71,13 +142,14 @@ def __init__(self, pixel_array, echo_list, affine, mask=None, 'number of time frames on the last axis ' \ 'of pixel_array' assert multithread is True \ - or multithread is False \ - or multithread == 'auto', 'multithreaded must be True, ' \ - 'False or auto. You entered {}' \ - .format(multithread) + or multithread is False \ + or multithread == 'auto', f'multithreaded must be True,' \ + f'False or auto. You entered ' \ + f'{multithread}' + if method != '2p_exp' and method != '3p_exp': - raise ValueError('method can be 2p_exp or 3p_exp only. You ' - 'specified {}'.format(method)) + raise ValueError(f'method can be 2p_exp or 3p_exp only. You ' + f'specified {method}') self.pixel_array = pixel_array self.shape = pixel_array.shape[:-1] @@ -88,8 +160,9 @@ def __init__(self, pixel_array, echo_list, affine, mask=None, if mask is None: self.mask = np.ones(self.shape, dtype=bool) else: - self.mask = mask - # Don't process any nan values + self.mask = mask.astype(bool) + + # Don't process any nan values self.mask[np.isnan(np.sum(pixel_array, axis=-1))] = False self.noise_threshold = noise_threshold self.method = method @@ -103,163 +176,40 @@ def __init__(self, pixel_array, echo_list, affine, mask=None, self.multithread = multithread # Fit data - if self.method == '2p_exp': - self.t2_map, self.t2_err, \ - self.m0_map, self.m0_err \ - = self.__fit__() - elif self.method == '3p_exp': - self.t2_map, self.t2_err, \ - self.m0_map, self.m0_err, \ - self.b_map, self.b_err \ - = self.__fit__() - - def __fit__(self): - - # Initialise maps - t2_map = np.zeros(self.n_vox) - t2_err = np.zeros(self.n_vox) - m0_map = np.zeros(self.n_vox) - m0_err = np.zeros(self.n_vox) - b_map = np.zeros(self.n_vox) - b_err = np.zeros(self.n_vox) - mask = self.mask.flatten() - signal = self.pixel_array.reshape(-1, self.n_te) - # Get indices of voxels to process - idx = np.argwhere(mask).squeeze() - - # Multithreaded method - if self.multithread: - with concurrent.futures.ProcessPoolExecutor() as pool: - with tqdm(total=idx.size) as progress: - futures = [] - - for ind in idx: - signal_thresh = signal[ind, :][ - signal[ind, :] > self.noise_threshold] - echo_list_thresh = self.echo_list[ - signal[ind, :] > self.noise_threshold] - future = pool.submit(self.__fit_signal__, - signal_thresh, - echo_list_thresh) - future.add_done_callback(lambda p: progress.update()) - futures.append(future) - - results = [] - for future in futures: - result = future.result() - results.append(result) - - if self.method == '2p_exp': - t2_map[idx], t2_err[idx], m0_map[idx], m0_err[idx] = [np.array( - row) for row in zip(*results)] - elif self.method == '3p_exp': - t2_map[idx], t2_err[idx], \ - m0_map[idx], m0_err[idx], \ - b_map[idx], b_err[idx] = \ - [np.array(row) for row in zip(*results)] - - # Single threaded method - else: - with tqdm(total=idx.size) as progress: - for ind in idx: - signal_thresh = signal[ind, :][ - signal[ind, :] > self.noise_threshold] - echo_list_thresh = self.echo_list[ - signal[ind, :] > self.noise_threshold] - if self.method == '2p_exp': - t2_map[ind], t2_err[ind], \ - m0_map[ind], m0_err[ind] \ - = self.__fit_signal__(signal_thresh, - echo_list_thresh) - elif self.method == '3p_exp': - t2_map[ind], t2_err[ind], \ - m0_map[ind], m0_err[ind], \ - b_map[ind], b_err[ind] \ - = self.__fit_signal__(signal_thresh, - echo_list_thresh) - progress.update(1) - - # Reshape results to raw data shape - t2_map = t2_map.reshape(self.shape) - t2_err = t2_err.reshape(self.shape) - m0_map = m0_map.reshape(self.shape) - m0_err = m0_err.reshape(self.shape) - - if self.method == '2p_exp': - return t2_map, t2_err, m0_map, m0_err - elif self.method == '3p_exp': - b_map = b_map.reshape(self.shape) - b_err = b_err.reshape(self.shape) - return t2_map, t2_err, m0_map, m0_err, b_map, b_err - - def __fit_signal__(self, sig, te): - - # Initialise parameters - if self.method == '2p_exp': - eq = two_param_eq - bounds = ([0, 0], [1000, 100000000]) - initial_guess = [20, 10000] - elif self.method == '3p_exp': - eq = three_param_eq - bounds = ([0, 0, 0], [1000, 100000000, 1000000]) - initial_guess = [20, 10000, 500] - - # Fit data to equation - try: - popt, pcov = curve_fit(eq, te, sig, p0=initial_guess, - bounds=bounds) - except (RuntimeError, ValueError): - popt = np.zeros(3) - pcov = np.zeros((3, 3)) - - # Extract fits and errors from result variables - if self.method == '2p_exp': - if popt[0] < bounds[1][0] - 1: - t2 = popt[0] - m0 = popt[1] - err = np.sqrt(np.diag(pcov)) - t2_err = err[0] - m0_err = err[1] - else: - t2, m0, t2_err, m0_err = 0, 0, 0, 0 - - return t2, t2_err, m0, m0_err - - elif self.method == '3p_exp': - if popt[0] < bounds[1][0] - 1: - t2 = popt[0] - m0 = popt[1] - b = popt[2] - err = np.sqrt(np.diag(pcov)) - t2_err = err[0] - m0_err = err[1] - b_err = err[2] - else: - t2, m0, t2_err, m0_err, b, b_err = 0, 0, 0, 0, 0, 0 - - return t2, t2_err, m0, m0_err, b, b_err - - def r2_map(self): - """ - Generates the R2 map from the T2 map output by initialising this - class. - - Parameters - ---------- - See class attributes in __init__ - - Returns - ------- - r2 : np.ndarray - An array containing the R2 map generated - by the function with R2 measured in ms. - """ - return np.reciprocal(self.t2_map) + fitting_model = T2Model(self.pixel_array, self.echo_list, + self.method, self.mask, self.multithread) + + if self.noise_threshold > 0: + fitting_model.threshold_noise(self.noise_threshold) + popt, error, r2 = fitting.fit_image(fitting_model) + self.t2_map = popt[0] + self.m0_map = popt[1] + self.t2_err = error[0] + self.m0_err = error[1] + self.r2 = r2 + + if self.method == '3p_exp': + self.b_map = popt[2] + self.b_err = error[2] + + # Filter values that are very close to models upper bounds of T2 or + # M0 out. + threshold = 0.999 # 99.9% of the upper bound + bounds_mask = ((self.t2_map > fitting_model.bounds[1][0] * threshold) | + (self.m0_map > fitting_model.bounds[1][1] * threshold)) + self.t2_map[bounds_mask] = 0 + self.m0_map[bounds_mask] = 0 + self.t2_err[bounds_mask] = 0 + self.m0_err[bounds_mask] = 0 + self.r2[bounds_mask] = 0 + if self.method == '3p_exp': + self.b_map[bounds_mask] = 0 + self.b_err[bounds_mask] = 0 def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps='all'): """Exports some of the T2 class attributes to NIFTI. - + Parameters ---------- output_directory : string, optional @@ -276,6 +226,9 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', base_path = os.path.join(output_directory, base_file_name) if maps == 'all' or maps == ['all']: maps = ['t2', 't2_err', 'm0', 'm0_err', 'r2', 'mask'] + if self.method == '3p_exp': + maps.append('b') + maps.append('b_err') if isinstance(maps, list): for result in maps: if result == 't2' or result == 't2_map': @@ -293,19 +246,26 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', affine=self.affine) nib.save(m0_err_nifti, base_path + '_m0_err.nii.gz') elif result == 'r2' or result == 'r2_map': - r2_nifti = nib.Nifti1Image(T2.r2_map(self), + r2_nifti = nib.Nifti1Image(self.r2, affine=self.affine) nib.save(r2_nifti, base_path + '_r2_map.nii.gz') elif result == 'mask': mask_nifti = nib.Nifti1Image(self.mask.astype(np.uint16), affine=self.affine) nib.save(mask_nifti, base_path + '_mask.nii.gz') + elif result == 'b' or result == 'b_map': + b_nifti = nib.Nifti1Image(self.b_map, + affine=self.affine) + nib.save(b_nifti, base_path + '_b_map.nii.gz') + elif result == 'b_err': + b_err_nifti = nib.Nifti1Image(self.b_err, + affine=self.affine) + nib.save(b_err_nifti, base_path + '_b_err.nii.gz') else: raise ValueError('No NIFTI file saved. The variable "maps" ' 'should be "all" or a list of maps from ' '"["t2", "t2_err", "m0", "m0_err", "r2", ' '"mask"]".') - return @@ -328,7 +288,9 @@ def two_param_eq(t, t2, m0): signal: np.ndarray The expected signal """ - return np.sqrt(np.square(m0 * np.exp(-t / t2))) + with np.errstate(divide='ignore'): + signal = m0 * np.exp(-t / t2) + return signal def three_param_eq(t, t2, m0, b): @@ -352,4 +314,6 @@ def three_param_eq(t, t2, m0, b): signal: np.ndarray The expected signal """ - return np.sqrt(np.square(m0 * np.exp(-t / t2) + b)) + with np.errstate(divide='ignore'): + signal = m0 * np.exp(-t / t2) + b + return signal diff --git a/ukat/mapping/t2_stimfit.py b/ukat/mapping/t2_stimfit.py new file mode 100644 index 00000000..baf71cb2 --- /dev/null +++ b/ukat/mapping/t2_stimfit.py @@ -0,0 +1,594 @@ +import os +import warnings + +import nibabel as nib +import numpy as np +from numba import jit +from pathos.pools import ProcessPool +from scipy import optimize +from sklearn.metrics import r2_score +from tqdm import tqdm + +from .resources.t2_stimfit import rf_pulses +from ukat.mapping.t2 import two_param_eq + + +class StimFitModel: + def __init__(self, mode='non_selective', n_comp=1, ukrin_vendor=None): + """ + A class to set up the T2 StimFit model. + + This model generates an optimisation dictionary (`opt`) containing the + model parameters and fitting options. + + Parameters + ---------- + mode : {'non_selective', 'selective'}, optional + Default 'non_selective' + Choose whether the refocusing pulses are selective on + non-selective. + n_comp : {1, 2, 3}, optional + Default 1 + The number of components to fit e.g. if n_comp=2, the model will + estimate two T2 values, two M0 values and one B1 value per voxel. + ukrin_vendor : {None, 'ge', 'philips', 'siemens'}, optional + Default None + The vendor of the MRI scanner used to acquire the data if the UKRIN + protocol was used. Specifying a vendor at this stage overrides + the relevant parameters in the model with those from the UKRIN + protocol. If no vendor is specified, the default parameters are + used but can be manually updated after instantiation. + + Key Parameters in Options Dictionary + ---------- + mode : {'non_selective', 'selective'} + Choose whether the refocusing pulses are slice selective or + non-selective. + esp : float + The echo spacing in seconds. + etl : int + The echo train length. + T1 : float + The approximate T1 value in seconds. + Dz : list + The start and end position of each slice in cm. + Nz : int + The number of positions along the slice profile to simulate signal + decay for. + Nrf : int + The number of resampled points in the RF waveform. + RFe : dict + The excitation pulse parameters, outlined below. + RFr : dict + The refocusing pulse parameters, outlined below. + lsq : dict + The least squares fitting parameters, outlined below. + + Key Parameters in RFe Dictionary + ---------- + RF : np.ndarray + The excitation pulse shape. + G : float + The amplitude of the excitation pulse in Gauss/cm. + tau : float + The excitation pulse duration in seconds. + phase : float + The relative phase of the excitation pulse in degrees (0 in CPMG). + angle : float + The flip angle of the excitation pulse in degrees (typically 90). + ref : float + The rephasing gradient fraction, times two. Near unity for + excitation. + alpha : list, optional + The actual tip angle distribution across the slice (degrees). If + not specified, the tip angle distribution is calculated. + + Key Parameters in RFr Dictionary + ---------- + RF : np.ndarray + The refocusing pulse shape. + G : float + The amplitude of the refocusing pulse in Gauss/cm. + tau : float + The refocusing pulse duration in seconds. + phase : float + The relative phase of the refocusing pulse in degrees (90 in CPMG). + angle : float + The flip angle of the refocusing pulse in degrees (typically 180). + ref : float + The rephasing gradient fraction, times two. Typically, 0 for + refocusing. + alpha : list, optional + The actual refocusing angle distribution across the slice + (degrees). If not specified, the tip angle distribution is + calculated. + + Key Parameters in lsq Dictionary + ---------- + Ncomp : int + The number of components to fit. + X0 : list + The initial guess for the fitting parameters in the order + [[T2_comp, M0_comp] * Ncomp, B1]. + XL : list + The lower bounds for the fitting parameters in the order + [[T2_comp, M0_comp] * Ncomp, B1]. + XU : list + The upper bounds for the fitting parameters in the order + [[T2_comp, M0_comp] * Ncomp, B1]. + xtol : float + Tolerance for termination by the change of the independent + variables. + ftol : float + Tolerance for termination by the change of the cost function. + """ + if mode != 'non_selective' and mode != 'selective': + raise ValueError(f'mode must be either "non_selective" or ' + f'"selective". You specified {mode}.') + self.mode = mode + if n_comp not in [1, 2, 3]: + raise ValueError(f'n_comp must be either 1, 2 or 3. You specified ' + f'{n_comp}.') + self.n_comp = n_comp + if ukrin_vendor not in ['ge', 'philips', 'siemens']: + warnings.warn('ukrin_vendor was not specified. Using default ' + 'pulse sequence parameters.') + self.opt = dict() + self.opt['mode'] = self.mode + self.opt['esp'] = 10e-3 + self.opt['etl'] = 20 + self.opt['T1'] = 3 + + self.opt['RFe'] = dict() + self.opt['RFr'] = dict() + if self.mode == 'selective': + self.opt['Dz'] = [-0.5, 0.5] + self.opt['Nz'] = 51 + self.opt['Nrf'] = 64 + self.opt['RFe'] = {'RF': [], + 'tau': 2e-3, + 'G': 0.5, + 'phase': 0, + 'ref': 1, + 'alpha': [], + 'angle': 90} + self.opt['RFr'] = {'RF': [], + 'tau': 2e-3, + 'G': 0.5, + 'phase': 90, + 'ref': 0, + 'alpha': [], + 'angle': 180, + 'FA_array': np.ones(self.opt['etl'])} + else: + self.opt['RFe'] = {'angle': 90} + self.opt['RFr'] = {'angle': 180, + 'FA_array': np.ones(self.opt['etl'])} + # Curve fitting parameters + self.opt['lsq'] = {'Ncomp': n_comp, + 'xtol': 5e-4, + 'ftol': 1e-9} + if self.opt['lsq']['Ncomp'] == 1: + # [T2(sec), amp, B1] + self.opt['lsq']['X0'] = [0.06, 0.1, 1] + self.opt['lsq']['XU'] = [3, 1e+3, 1.8] + self.opt['lsq']['XL'] = [0.015, 0, 0.2] + elif self.opt['lsq']['Ncomp'] == 2: + # [T2, amp, T2, amp, B1] + self.opt['lsq']['X0'] = [0.02, 0.1, 0.331, 0.1, 1] + self.opt['lsq']['XU'] = [0.25, 1e+3, 3, 1e+3, 1.8] + self.opt['lsq']['XL'] = [0.015, 0, 0.25, 0, 0.2] + elif self.opt['lsq']['Ncomp'] == 3: + # [T2, amp, T2, amp, T2, amp, B1] + self.opt['lsq']['X0'] = [0.02, 0.1, 0.036, 0.1, 0.131, 0.1, 1] + self.opt['lsq']['XU'] = [0.035, 1e+3, 0.13, 1e3, 3, 1e+3, 1.8] + self.opt['lsq']['XL'] = [0.015, 0, 0.035, 0, 0.13, 0, 0.2] + + if ukrin_vendor is not None: + self._set_ukrin_vendor(ukrin_vendor) + if self.mode == 'selective': + self.opt['RFe'] = self._set_rf(self.opt['RFe']) + self.opt['RFr'] = self._set_rf(self.opt['RFr']) + + def get_opt(self): + return self.opt + + def get_lsq(self): + return self.opt['lsq'] + + def get_rfe(self): + return self.opt['RFe'] + + def get_rfr(self): + return self.opt['RFr'] + + def _set_ukrin_vendor(self, vendor): + self.vendor = vendor + self.opt['T1'] = 1.5 + self.opt['esp'] = 0.0129 + self.opt['etl'] = 10 + self.opt['te'] = (np.arange(self.opt['etl']) + 1) * self.opt['esp'] + self.opt['RFr']['FA_array'] = np.ones(self.opt['etl']) + if self.vendor == 'ge': + self.opt['RFe']['tau'] = 2000 / 1e6 # Duration + self.opt['RFe']['G'] = 0.751599 # Amplitude + self.opt['RFr']['tau'] = 3136 / 1e6 + self.opt['RFr']['G'] = 0.276839 + self.opt['RFe']['RF'] = rf_pulses.ge_90 + self.opt['RFr']['RF'] = rf_pulses.ge_180 + self.opt['Dz'] = [0, 0.45] # Slice thickness + elif self.vendor == 'philips': + self.opt['RFe']['tau'] = 3820 / 1e6 + self.opt['RFe']['G'] = 0.392 + self.opt['RFr']['tau'] = 6010 / 1e6 + self.opt['RFr']['G'] = 0.327 + self.opt['RFe']['RF'] = rf_pulses.philips_90 + self.opt['RFr']['RF'] = rf_pulses.philips_180 + self.opt['Dz'] = [0, 0.45] + elif self.vendor == 'siemens': + self.opt['RFe']['tau'] = 3072 / 1e6 + self.opt['RFe']['G'] = 0.417 + self.opt['RFr']['tau'] = 3000 / 1e6 + self.opt['RFr']['G'] = 0.326 + self.opt['RFe']['RF'] = rf_pulses.ge_90 + self.opt['RFr']['RF'] = rf_pulses.ge_180 + self.opt['Dz'] = [0, 0.5] + else: + warnings.warn(f'{self.vendor} is not implemented. Please ' + f'manually specify the models parameters.') + + def _set_rf(self, rf): + dz = self.opt['Dz'] + nz = self.opt['Nz'] + nrf = self.opt['Nrf'] + + gamma = 2 * np.pi * 42.575e6 / 10000 # Gauss + z = np.linspace(dz[0], dz[1], nz) + scale = rf['angle'] / (gamma * rf['tau'] * abs(np.sum(rf['RF'])) / len( + rf['RF']) * 180 / np.pi) + rf['RF'] *= scale + + m = np.zeros([3, nz]) + m[2, :] = 1 + rf['RF'] = 1e-4 * rf['RF'] # approximation for + # small tip angle + + phi = gamma * rf['G'] * z * rf['tau'] / nrf + cphi = np.cos(phi) + sphi = np.sin(phi) + cp_rf = np.cos(rf['phase'] * np.pi / 180) + sp_rf = np.sin(rf['phase'] * np.pi / 180) + theta_rf = gamma * rf['RF'] * rf['tau'] / nrf + ct_rf = np.cos(theta_rf) + st_rf = np.sin(theta_rf) + + for i in range(nrf): + for j in range(nz): + rz = np.array([[cphi[j], sphi[j], 0], + [-sphi[j], cphi[j], 0], + [0, 0, 1]]) + m[:, j] = np.dot(rz, m[:, j]) + + r = np.array([[1, 0, 0], + [0, ct_rf[i], st_rf[i]], + [0, -st_rf[i], ct_rf[i]]]) + if rf['phase'] != 0: + rz = np.array([[cp_rf, sp_rf, 0], + [-sp_rf, cp_rf, 0], + [0, 0, 1]]) + rzm = np.array([[cp_rf, -sp_rf, 0], + [sp_rf, cp_rf, 0], + [0, 0, 1]]) + r = np.dot(rzm, np.dot(r, rz)) + m = np.dot(r, m) + + if rf['ref'] > 0: + psi = -rf['ref'] / 2 * gamma * rf['G'] * z * rf['tau'] + for j in range(nz): + rz = np.array([[np.cos(psi[j]), np.sin(psi[j]), 0], + [-np.sin(psi[j]), np.cos(psi[j]), 0], + [0, 0, 1]]) + m[:, j] = np.dot(rz, m[:, j]) + + rf['RF'] = 1e4 * rf['RF'] + rf['alpha'] = 1e4 * np.arccos(m[2, :]) + return rf + + +class T2StimFit: + """ + Attributes + ---------- + t2_map : np.ndarray + The estimated T2 values in ms + m0_map : np.ndarray + The estimated M0 values + r2_map : np.ndarray + The R-Squared value of the fit, values close to 1 indicate a good + fit, lower values indicate a poorer fit + shape : tuple + The shape of the T2 map + n_vox : int + The number of voxels in the map i.e. the product of all dimensions + apart from TE + """ + def __init__(self, pixel_array, affine, model, + mask=None, multithread='auto', norm=True): + """ + Class for performing stimulated echo T2 fitting as in Marc Lebel R. + StimFit: A Toolbox for Robust T2 Mapping with Stimulated Echo + Compensation. In: Proc. Intl. Soc. Mag. Reson. Med. 20. Melbourne; + 2012:2558. https://archive.ismrm.org/2012/2558.html. + + Parameters + ---------- + pixel_array : np.ndarray + An array containing the signal from each voxel at each echo + time with the last dimension being time i.e. the array needed to + generate a 3D T2 map would have dimensions [x, y, z, TE]. + affine : np.ndarray + A matrix giving the relationship between voxel coordinates and + world coordinates. + model : StimFitModel + A StimFitModel object containing the model parameters. + mask : np.ndarray, optional + A boolean mask of the voxels to fit. Should be the shape of the + desired T2 map rather than the raw data i.e. omit the time + dimension. + multithread : bool or 'auto', optional + Default 'auto'. + If True, fitting will be distributed over all cores available on + the node. If False, fitting will be carried out on a single thread. + 'auto' attempts to apply multithreading where appropriate based + on the number of voxels being fit. + norm : bool, optional + Default True. + StimFit is performed on normalised data. If norm is False, + it is assumed that the data has already been normalised. If norm + is True, the data will be normalised before fitting. + """ + self.pixel_array = np.copy(pixel_array) + self.shape = pixel_array.shape[:-1] + self.n_vox = np.prod(self.shape) + self.affine = affine + self.model = model + + assert multithread is True \ + or multithread is False \ + or multithread == 'auto', f'multithreaded must be True,' \ + f'False or auto. You entered ' \ + f'{multithread}' + if multithread == 'auto': + if self.n_vox > 20: + multithread = True + else: + multithread = False + self.multithread = multithread + + # Generate a mask if there isn't one specified + if mask is None: + self.mask = np.ones(self.shape, dtype=bool) + else: + self.mask = mask + # Don't process any nan values + self.mask[np.isnan(np.sum(pixel_array, axis=-1))] = False + + # Normalise the data + if norm: + self.pixel_array /= np.nanmax(self.pixel_array) + + if np.nanmax(self.pixel_array) > 1: + warnings.warn('Pixel array contains values greater than 1. ' + 'Data should be normalised, please set norm=True ' + 'or manually normalise your data.') + + # Perform the fit + self._fit() + + def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', + maps='all'): + """Exports some of the T2StimFit class attributes to NIFTI. + + Parameters + ---------- + output_directory : string, optional + Path to the folder where the NIFTI files will be saved. + base_file_name : string, optional + Filename of the resulting NIFTI. This code appends the extension. + Eg., base_file_name = 'Output' will result in 'Output.nii.gz'. + maps : list or 'all', optional + List of maps to save to NIFTI. This should either the string "all" + or a list of maps from ["t2", "m0", "b1", "r2", "mask"]. + """ + os.makedirs(output_directory, exist_ok=True) + base_path = os.path.join(output_directory, base_file_name) + if maps == 'all' or maps == ['all']: + maps = ['t2', 'm0', 'b1', 'r2', 'mask'] + if isinstance(maps, list): + for result in maps: + if result == 't2' or result == 't2_map': + t2_nifti = nib.Nifti1Image(self.t2_map, affine=self.affine) + nib.save(t2_nifti, base_path + '_t2_map.nii.gz') + elif result == 'm0' or result == 'm0_map': + m0_nifti = nib.Nifti1Image(self.m0_map, affine=self.affine) + nib.save(m0_nifti, base_path + '_m0_map.nii.gz') + elif result == 'b1': + m0_err_nifti = nib.Nifti1Image(self.b1_map, + affine=self.affine) + nib.save(m0_err_nifti, base_path + '_b1_map.nii.gz') + elif result == 'r2' or result == 'r2_map': + r2_nifti = nib.Nifti1Image(self.r2_map, + affine=self.affine) + nib.save(r2_nifti, base_path + '_r2_map.nii.gz') + elif result == 'mask': + mask_nifti = nib.Nifti1Image(self.mask.astype(np.uint16), + affine=self.affine) + nib.save(mask_nifti, base_path + '_mask.nii.gz') + else: + raise ValueError('No NIFTI file saved. The variable "maps" ' + 'should be "all" or a list of maps from ' + '"["t2", "m0", "b1", "r2", ' + '"mask"]".') + + def _fit(self): + mask = self.mask.flatten() + signal = self.pixel_array.reshape(self.n_vox, self.model.opt['etl']) + idx = np.argwhere(mask).squeeze() + signal = signal[idx, :] + + if self.multithread: + with ProcessPool() as executor: + results = executor.map(self._fit_signal, signal) + else: + results = list(tqdm(map(self._fit_signal, signal), + total=np.sum(self.mask))) + t2 = np.array([result[0] for result in results]) + m0 = np.array([result[1] for result in results]) + b1 = np.array([result[2] for result in results]) + r2 = np.array([result[3] for result in results]) + + if self.model.n_comp > 1: + t2_map = np.zeros((self.n_vox, self.model.n_comp)) + m0_map = np.zeros((self.n_vox, self.model.n_comp)) + r2_map = np.zeros((self.n_vox, self.model.n_comp)) + else: + t2_map = np.zeros(self.n_vox) + m0_map = np.zeros(self.n_vox) + r2_map = np.zeros(self.n_vox) + b1_map = np.zeros(self.n_vox) + t2_map[idx] = t2 * 1000 # Convert to ms + m0_map[idx] = m0 + b1_map[idx] = b1 + r2_map[idx] = r2 + self.t2_map = np.squeeze(t2_map.reshape((*self.shape, + self.model.n_comp))) + self.m0_map = np.squeeze(m0_map.reshape((*self.shape, + self.model.n_comp))) + self.b1_map = b1_map.reshape(self.shape) + self.r2_map = np.squeeze(r2_map.reshape((*self.shape, + self.model.n_comp))) + + def _fit_signal(self, signal): + if len(signal) != self.model.opt['etl']: + raise Exception('Inconsistent echo train length') + + # Two component fitting + if self.model.opt['lsq']['Ncomp'] == 2: + x = optimize.least_squares(self._residual2, + self.model.opt['lsq']['X0'], + args=(signal, self.model.opt, + self.model.mode), + bounds=(self.model.opt['lsq']['XL'], + self.model.opt['lsq']['XU']), + xtol=self.model.opt['lsq']['xtol'], + ftol=self.model.opt['lsq']['ftol']).x + t2, amp, b1 = [x[0], x[2]], [x[1], x[3]], x[4] + r2 = [r2_score(signal, two_param_eq(self.model.opt['te'], t2[0], + amp[0])), + r2_score(signal, two_param_eq(self.model.opt['te'], t2[1], + amp[1]))] + + # Three component fitting + elif self.model.opt['lsq']['Ncomp'] == 3: + x = optimize.least_squares(self._residual3, + self.model.opt['lsq']['X0'], + args=(signal, self.model.opt, + self.model.mode), + bounds=(self.model.opt['lsq']['XL'], + self.model.opt['lsq']['XU']), + xtol=self.model.opt['lsq']['xtol'], + ftol=self.model.opt['lsq']['ftol']).x + t2, amp, b1 = [x[0], x[2], x[4]], [x[1], x[3], x[5]], x[6] + r2 = [r2_score(signal, two_param_eq(self.model.opt['te'], t2[0], + amp[0])), + r2_score(signal, two_param_eq(self.model.opt['te'], t2[1], + amp[1])), + r2_score(signal, two_param_eq(self.model.opt['te'], t2[2], + amp[2]))] + + # One component fitting + else: + x = optimize.least_squares(self._residual1, + self.model.opt['lsq']['X0'], + args=(signal, self.model.opt, + self.model.mode), + bounds=(self.model.opt['lsq']['XL'], + self.model.opt['lsq']['XU']), + xtol=self.model.opt['lsq']['xtol'], + ftol=self.model.opt['lsq']['ftol']).x + t2, amp, b1 = x + fit_sig = two_param_eq(self.model.opt['te'], t2, amp) + r2 = r2_score(signal, fit_sig) + return t2, amp, b1, r2 + + @staticmethod + def _residual1(p, y, opt, mode): + return y - _epgsig(p[0], p[2], opt, mode) * p[1] + + @staticmethod + def _residual2(p, y, opt, mode): + return y - (_epgsig(p[0], p[4], opt, mode) * p[1] - + _epgsig(p[2], p[4], opt, mode) * p[3]) + + @staticmethod + def _residual3(p, y, opt, mode): + return y - (_epgsig(p[0], p[6], opt, mode) * p[1] - + _epgsig(p[4], p[6], opt, mode) * p[5] - + _epgsig(p[2], p[6], opt, mode) * p[3]) + + +def _epgsig(t2, b1, opt, mode): + sig = np.zeros(opt['etl']) + if mode == 'non_selective': + fa = np.pi / 180 * opt['RFr']['angle'] * np.array([ + opt['RFr']['FA_array']]) + sig = _epg(t2, b1, opt['T1'], + opt['esp'], fa, + opt['RFe']['angle'] * np.pi / 180) + elif mode == 'selective': + fa = np.array([opt['RFr']['alpha']]).T * \ + opt['RFr']['FA_array'] + m = _epg(t2, b1, opt['T1'], opt['esp'], + fa, opt['RFe']['alpha']) + sig = np.sum(m, 0) / opt['Nz'] + return sig.ravel() + + +@jit(nopython=True) +def _epg(x2, b1, x1, esp, ar, ae): # TE = 6.425ms. TR = 1500ms. 90, 175, + # 145, 110, 110, 110. + echo_intensity = np.zeros(ar.shape, dtype=np.float64) + omiga = np.zeros((ar.shape[0], 3, 1 + 2 * ar.shape[1]), + dtype=np.float64) + ar = b1 * ar + ae = b1 * ae + x2 = np.exp(-0.5 * esp / x2) + x1 = np.exp(-0.5 * esp / x1) + + for i in range(omiga.shape[2]): + if i == 0: + omiga[:, 0, i] = np.sin(ae) + omiga[:, 1, i] = np.sin(ae) + omiga[:, 2, i] = np.cos(ae) + continue + omiga[:, 0, 1:i + 1] = omiga[:, 0, 0:i] + omiga[:, 1, 0:i] = omiga[:, 1, 1:i + 1] + omiga[:, 0, 0] = np.conj(omiga[:, 1, 0]) + omiga[:, 0:2, :] = x2 * omiga[:, 0:2, :] + omiga[:, 2, :] = x1 * omiga[:, 2, :] + omiga[:, 2, 0] += 1 - x1 + if i % 2 == 1: + for runs in range(ar.shape[0]): + ari = ar[runs, i // 2] + t = np.array( + [[np.cos(0.5 * ari) ** 2, np.sin(0.5 * ari) ** 2, + np.sin(ari)], + [np.sin(0.5 * ari) ** 2, np.cos(0.5 * ari) ** 2, + -np.sin(ari)], + [-0.5 * np.sin(ari), +0.5 * np.sin(ari), + np.cos(ari)]], dtype=np.float64) + omiga[runs, :, :] = np.dot(t, np.ascontiguousarray( + omiga[runs, :, :])) + if i % 2 == 0: + echo_intensity[:, i // 2 - 1] = omiga[:, 0, 0] + return echo_intensity diff --git a/ukat/mapping/t2star.py b/ukat/mapping/t2star.py index d76288ef..2227b428 100644 --- a/ukat/mapping/t2star.py +++ b/ukat/mapping/t2star.py @@ -2,9 +2,42 @@ import warnings import numpy as np import nibabel as nib -import concurrent.futures + +from . import fitting + +from pathos.pools import ProcessPool from tqdm import tqdm -from scipy.optimize import curve_fit +from sklearn.metrics import r2_score + + +class T2StarExpModel(fitting.Model): + def __init__(self, pixel_array, te, mask=None, multithread=True): + """ + A class for fitting T2* data to a mono-exponential model. + + Parameters + ---------- + pixel_array : np.ndarray + An array containing the signal from each voxel at each echo + time with the last dimension being time i.e. the array needed to + generate a 3D T2* map would have dimensions [x, y, z, TE]. + te : np.ndarray + An array of the echo times used for the last dimension of the + pixel_array. In milliseconds. + mask : np.ndarray, optional + A boolean mask of the voxels to fit. Should be the shape of the + desired T2* map rather than the raw data i.e. omit the time + dimension. + multithread : bool, optional + Default True + If True, the fitting will be performed in parallel using all + available cores + """ + + super().__init__(pixel_array, te, two_param_eq, mask, multithread) + self.bounds = ([0, 0], [700, 100000000]) + self.initial_guess = [20, 10000] + self.generate_lists() class T2Star: @@ -21,6 +54,9 @@ class T2Star: m0_err : np.ndarray The certainty in the fit of `m0_map`. Only returned if `2p_exp` method is used, otherwise is an array of nan + r2 : np.ndarray + The R-Squared value of the fit, values close to 1 indicate a good + fit, lower values indicate a poorer fit shape : tuple The shape of the T2* map n_te : int @@ -75,27 +111,29 @@ def __init__(self, pixel_array, echo_list, affine, mask=None, 'number of time frames on the last axis ' \ 'of pixel_array' assert method == 'loglin' \ - or method == '2p_exp', 'method must be loglin or 2p_exp. You ' \ - 'entered {}'.format(method) + or method == '2p_exp', f'method must be loglin or 2p_exp. You ' \ + f'entered {method}' assert multithread is True \ or multithread is False \ - or multithread == 'auto', 'multithreaded must be True, False or ' \ - 'auto. You entered {}'\ - .format(multithread) + or multithread == 'auto', f'multithreaded must be True, False ' \ + f'or auto. You entered {multithread}' self.pixel_array = pixel_array self.shape = pixel_array.shape[:-1] self.n_te = pixel_array.shape[-1] self.n_vox = np.prod(self.shape) self.affine = affine + # Generate a mask if there isn't one specified if mask is None: self.mask = np.ones(self.shape, dtype=bool) else: - self.mask = mask - # Don't process any nan values + self.mask = mask.astype(bool) + + # Don't process any nan values self.mask[np.isnan(np.sum(pixel_array, axis=-1))] = False self.echo_list = echo_list self.method = method + # Auto multithreading conditions if multithread == 'auto': if self.method == '2p_exp' and self.n_vox > 20: @@ -105,8 +143,33 @@ def __init__(self, pixel_array, echo_list, affine, mask=None, self.multithread = multithread # Fit data - self.t2star_map, self.t2star_err, self.m0_map, self.m0_err\ - = self.__fit__() + + # Initialise an exponential model, even if we're using loglin fit, + # so we're using the same limits etc + self._exp_model = T2StarExpModel(self.pixel_array, self.echo_list, + self.mask, self.multithread) + if self.method == 'loglin': + popt, error, r2 = self._loglin_fit() + else: + popt, error, r2 = fitting.fit_image(self._exp_model) + + self.t2star_map = popt[0] + self.m0_map = popt[1] + self.t2star_err = error[0] + self.m0_err = error[1] + self.r2 = r2 + + # Filter values that are very close to models upper bounds of T2* or + # M0 out. + threshold = 0.999 # 99.9% of the upper bound + bounds_mask = ((self.t2star_map > + self._exp_model.bounds[1][0] * threshold) | + (self.m0_map > self._exp_model.bounds[1][1] * threshold)) + self.t2star_map[bounds_mask] = 0 + self.m0_map[bounds_mask] = 0 + self.t2star_err[bounds_mask] = 0 + self.m0_err[bounds_mask] = 0 + self.r2[bounds_mask] = 0 # Warn if using loglin method to produce a map with a large # proportion of T2* < 20 ms i.e. where loglin isn't as accurate. @@ -122,128 +185,88 @@ def __init__(self, pixel_array, echo_list, affine, mask=None, 'interest, consider using the 2p_exp fitting' ' method'.format(proportion_less_than_20)) - def __fit__(self): - - # Initialise maps - t2star_map = np.zeros(self.n_vox) - t2star_err = np.zeros(self.n_vox) - m0_map = np.zeros(self.n_vox) - m0_err = np.zeros(self.n_vox) - mask = self.mask.flatten() - signal = self.pixel_array.reshape(-1, self.n_te) - # Get indices of voxels to process - idx = np.argwhere(mask).squeeze() - - # Multithreaded method + def _loglin_fit(self): if self.multithread: - with concurrent.futures.ProcessPoolExecutor() as pool: - with tqdm(total=idx.size) as progress: - futures = [] - - for ind in idx: - future = pool.submit(self.__fit_signal__, - signal[ind, :], - self.echo_list, - self.method) - future.add_done_callback(lambda p: progress.update()) - futures.append(future) - - results = [] - for future in futures: - result = future.result() - results.append(result) - t2star_map[idx], t2star_err[idx], m0_map[idx], m0_err[idx] \ - = [np.array(row) for row in zip(*results)] - - # Single threaded method + with ProcessPool() as executor: + results = executor.map(self._fit_loglin_signal, + self._exp_model.signal_list, + self._exp_model.x_list, + self._exp_model.mask_list, + [self._exp_model] * self.n_vox) else: - with tqdm(total=idx.size) as progress: - for ind in idx: - sig = signal[ind, :] - t2star_map[ind], t2star_err[ind], \ - m0_map[ind], m0_err[ind] \ - = self.__fit_signal__(sig, self.echo_list, self.method) - progress.update(1) - - # Reshape results to raw data shape - t2star_map = t2star_map.reshape(self.shape) - t2star_err = t2star_err.reshape(self.shape) - m0_map = m0_map.reshape(self.shape) - m0_err = m0_err.reshape(self.shape) - - return t2star_map, t2star_err, m0_map, m0_err + results = list(tqdm(map(self._fit_loglin_signal, + self._exp_model.signal_list, + self._exp_model.x_list, + self._exp_model.mask_list, + [self._exp_model] * self.n_vox), + total=self.n_vox)) + popt_array = np.array([result[0] for result in results]) + popt_list = [popt_array[:, p].reshape(self._exp_model.map_shape) for p + in range(self._exp_model.n_params)] + error_array = np.array([result[1] for result in results]) + error_list = [error_array[:, p].reshape(self._exp_model.map_shape) + for p in range(self._exp_model.n_params)] + r2 = np.array([result[2] for result in results]).reshape( + self._exp_model.map_shape) + return popt_list, error_list, r2 @staticmethod - def __fit_signal__(sig, te, method): - if method == 'loglin': - s_w = 0.0 - s_wx = 0.0 - s_wx2 = 0.0 - s_wy = 0.0 - s_wxy = 0.0 - n_te = len(sig) - - noise = sig.sum() / n_te - sd = np.abs(np.sum(sig ** 2) / n_te - noise ** 2) - if sd > 1e-10: - for t in range(n_te): - if sig[t] > 0: - te_tmp = te[t] - if sig[t] > sd: - sigma = np.log(sig[t] / (sig[t] - sd)) - else: - sigma = np.log(sig[t] / 0.0001) - logsig = np.log(sig[t]) - weight = 1 / sigma ** 2 - - s_w += weight - s_wx += weight * te_tmp - s_wx2 += weight * te_tmp ** 2 - s_wy += weight * logsig - s_wxy += weight * te_tmp * logsig - - delta = (s_w * s_wx2) - (s_wx ** 2) - if delta > 1e-5: - a = (1 / delta) * (s_wx2 * s_wy - s_wx * s_wxy) - b = (1 / delta) * (s_w * s_wxy - s_wx * s_wy) - t2star = np.real(-1 / b) - m0 = np.real(np.exp(a)) - if t2star < 0 or t2star > 700 or np.isnan(t2star): + def _fit_loglin_signal(sig, te, mask, model): + if mask is True: + with np.errstate(divide='ignore', invalid='ignore'): + sig = np.array(sig) + te = np.array(te) + s_w = 0.0 + s_wx = 0.0 + s_wx2 = 0.0 + s_wy = 0.0 + s_wxy = 0.0 + n_te = len(sig) + + noise = sig.sum() / n_te + sd = np.abs(np.sum(sig ** 2) / n_te - noise ** 2) + if sd > 1e-10: + for t in range(n_te): + if sig[t] > 0: + te_tmp = te[t] + if sig[t] > sd: + sigma = np.log(sig[t] / (sig[t] - sd)) + else: + sigma = np.log(sig[t] / 0.0001) + logsig = np.log(sig[t]) + weight = 1 / sigma ** 2 + + s_w += weight + s_wx += weight * te_tmp + s_wx2 += weight * te_tmp ** 2 + s_wy += weight * logsig + s_wxy += weight * te_tmp * logsig + + delta = (s_w * s_wx2) - (s_wx ** 2) + if delta > 1e-5: + a = (1 / delta) * (s_wx2 * s_wy - s_wx * s_wxy) + b = (1 / delta) * (s_w * s_wxy - s_wx * s_wy) + t2star = np.real(-1 / b) + m0 = np.real(np.exp(a)) + if t2star < 0 or t2star > model.bounds[1][0] or \ + np.isnan(t2star): + t2star = 0 + m0 = 0 + else: t2star = 0 m0 = 0 else: t2star = 0 m0 = 0 - else: - t2star = 0 - m0 = 0 - t2star_err = np.nan - m0_err = np.nan - - elif method == '2p_exp': - # Initialise parameters - bounds = ([0, 0], [700, 100000000]) - initial_guess = [20, 10000] - - # Fit data to equation - try: - popt, pcov = curve_fit(two_param_eq, te, sig, - p0=initial_guess, bounds=bounds) - except RuntimeError: - popt = np.zeros(2) - pcov = np.zeros((2, 2)) - - # Extract fits and errors from result variables - if popt[0] < bounds[1][0] - 1: - t2star = popt[0] - m0 = popt[1] - err = np.sqrt(np.diag(pcov)) - t2star_err = err[0] - m0_err = err[1] - else: - t2star, m0, t2star_err, m0_err = 0, 0, 0, 0 + else: + t2star = 0 + m0 = 0 - return t2star, t2star_err, m0, m0_err + fit_sig = two_param_eq(te, t2star, m0) + r2 = r2_score(sig, fit_sig) + t2star_err = np.nan + m0_err = np.nan + return (t2star, m0), (t2star_err, m0_err), r2 def r2star_map(self): """ @@ -258,15 +281,17 @@ def r2star_map(self): ------- r2star_map : np.ndarray An array containing the R2* map generated - by the function with R2* measured in ms. + by the function with R2* measured in ms^-1. """ - return np.nan_to_num(np.reciprocal(self.t2star_map), - posinf=0, neginf=0) + with np.errstate(divide='ignore'): + r2star = np.nan_to_num(np.reciprocal(self.t2star_map), + posinf=0, neginf=0) + return r2star def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps='all'): """Exports some of the T2Star class attributes to NIFTI. - + Parameters ---------- output_directory : string, optional @@ -277,12 +302,12 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps : list or 'all', optional List of maps to save to NIFTI. This should either the string "all" or a list of maps from ["t2star", "t2star_err", "m0", - "m0_err", "r2star", "mask"]. + "m0_err", "r2star", "r2", "mask"]. """ os.makedirs(output_directory, exist_ok=True) base_path = os.path.join(output_directory, base_file_name) if maps == 'all' or maps == ['all']: - maps = ['t2star', 'm0', 'r2star', 'mask'] + maps = ['t2star', 'm0', 'r2star', 'r2', 'mask'] if self.method == '2p_exp': maps += ['t2star_err', 'm0_err'] if isinstance(maps, list): @@ -317,6 +342,10 @@ def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', r2star_nifti = nib.Nifti1Image(T2Star.r2star_map(self), affine=self.affine) nib.save(r2star_nifti, base_path + '_r2star_map.nii.gz') + elif result == 'r2' or result == 'r2_map': + r2_nifti = nib.Nifti1Image(self.r2, + affine=self.affine) + nib.save(r2_nifti, base_path + '_r2.nii.gz') elif result == 'mask': mask_nifti = nib.Nifti1Image(self.mask.astype(np.uint16), affine=self.affine) @@ -348,4 +377,6 @@ def two_param_eq(t, t2star, m0): ------- signal: np.ndarray """ - return np.sqrt(np.square(m0 * np.exp(-t / t2star))) + with np.errstate(divide='ignore'): + signal = m0 * np.exp(-t / t2star) + return signal diff --git a/ukat/mapping/tests/__init__.py b/ukat/mapping/tests/__init__.py index 2e9f2657..9577513f 100644 --- a/ukat/mapping/tests/__init__.py +++ b/ukat/mapping/tests/__init__.py @@ -1 +1,2 @@ -from . import test_b0, test_diffusion, test_mtr, test_t1, test_t2, test_t2star +from . import test_b0, test_diffusion, test_mtr, test_t1, test_t2, \ + test_t2_stimfit, test_t2star diff --git a/ukat/mapping/tests/test_b0.py b/ukat/mapping/tests/test_b0.py index 650c2891..75e4aee8 100644 --- a/ukat/mapping/tests/test_b0.py +++ b/ukat/mapping/tests/test_b0.py @@ -18,7 +18,8 @@ class TestB0: correct_array = np.angle(np.exp(1j * correct_array)) one_echo_array = np.arange(100).reshape((10, 10, 1)) multiple_echoes_array = (np.concatenate((correct_array, - np.arange(300).reshape((10, 10, 3))), axis=2)) + np.arange(300).reshape( + (10, 10, 3))), axis=2)) affine = np.eye(4) correct_echo_list = [4, 7] one_echo_list = [4] @@ -33,7 +34,7 @@ def test_b0_calculation_without_unwrapping(self): unwrap=False).b0_map b0maps_stats = arraystats.ArrayStats(b0_map_calculated).calculate() npt.assert_allclose([b0maps_stats["mean"], b0maps_stats["std"], - b0maps_stats["min"], b0maps_stats["max"]], + b0maps_stats["min"], b0maps_stats["max"]], self.gold_standard, rtol=1e-7, atol=1e-9) def test_inputs(self): @@ -182,7 +183,7 @@ def test_real_data(self): mapper = B0(images, te, affine, unwrap=True) b0map_stats = arraystats.ArrayStats(mapper.b0_map).calculate() npt.assert_allclose([b0map_stats["mean"], b0map_stats["std"], - b0map_stats["min"], b0map_stats["max"]], + b0map_stats["min"], b0map_stats["max"]], gold_standard_b0, rtol=0.01, atol=0) def test_b0_offset_correction(self): @@ -214,6 +215,7 @@ def test_b0_offset_correction(self): # This assertion proves that there was offset correction performed assert (mapper.b0_map != b0_map_without_offset_correction).any() + # Delete the NIFTI test folder recursively if any of the unit tests failed if os.path.exists('test_output'): shutil.rmtree('test_output') diff --git a/ukat/mapping/tests/test_diffusion.py b/ukat/mapping/tests/test_diffusion.py index db88ad1c..d713bb8b 100644 --- a/ukat/mapping/tests/test_diffusion.py +++ b/ukat/mapping/tests/test_diffusion.py @@ -91,11 +91,13 @@ def test_fail_to_fit(self): def test_negative_signal(self): gold_standard_adc = [0.000833, 0.000998, 0.0, 0.004852] gold_standard_adc_err = [7.819414e-05, 1.222237e-04, 0.0, 9.935775e-04] + gold_standard_adc_r2 = [0.398594, 0.434274, -0.03715, 0.999107] negateive_pixel_array = self.pixel_array.copy() negateive_pixel_array[:30, :, :, :] -= 40000 mapper = ADC(negateive_pixel_array, self.affine, self.bvals) adc_stats = arraystats.ArrayStats(mapper.adc).calculate() adc_err_stats = arraystats.ArrayStats(mapper.adc_err).calculate() + adc_r2_stats = arraystats.ArrayStats(mapper.r2).calculate() assert np.sum(mapper.adc[:30, :, :]) == 0 npt.assert_allclose([adc_stats['mean']['3D'], adc_stats['std']['3D'], adc_stats['min']['3D'], adc_stats['max']['3D']], @@ -105,6 +107,11 @@ def test_negative_signal(self): adc_err_stats['min']['3D'], adc_err_stats['max']['3D']], gold_standard_adc_err, rtol=5e-3, atol=1e-7) + npt.assert_allclose([adc_r2_stats['mean']['3D'], + adc_r2_stats['std']['3D'], + adc_r2_stats['min']['3D'], + adc_r2_stats['max']['3D']], + gold_standard_adc_r2, rtol=5e-3, atol=1e-7) def test_real_data(self): # Gold standard statistics @@ -154,10 +161,13 @@ def test_to_nifti(self): mapper.to_nifti(output_directory='test_output', base_file_name='adc_test', maps='all') output_files = os.listdir('test_output') - assert len(output_files) == 3 + assert len(output_files) == 6 assert 'adc_test_adc_map.nii.gz' in output_files assert 'adc_test_adc_err.nii.gz' in output_files assert 'adc_test_mask.nii.gz' in output_files + assert 'adc_test_r2.nii.gz' in output_files + assert 'adc_test_s0_map.nii.gz' in output_files + assert 'adc_test_s0_err.nii.gz' in output_files for f in os.listdir('test_output'): os.remove(os.path.join('test_output', f)) diff --git a/ukat/mapping/tests/test_t1.py b/ukat/mapping/tests/test_t1.py index 407f6608..c860b674 100644 --- a/ukat/mapping/tests/test_t1.py +++ b/ukat/mapping/tests/test_t1.py @@ -96,16 +96,26 @@ def test_two_param_fit(self): # Multithread mapper = T1(signal_array, self.t, self.affine, multithread=True) assert mapper.shape == signal_array.shape[:-1] - assert mapper.t1_map.mean() - self.t1 < 0.00001 - assert mapper.m0_map.mean() - self.m0 < 0.00001 - assert mapper.r1_map().mean() - 1 / self.t1 < 0.00001 + npt.assert_almost_equal(mapper.t1_map.mean(), self.t1) + npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r1_map().mean(), 1 / self.t1) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Single Threaded mapper = T1(signal_array, self.t, self.affine, multithread=False) assert mapper.shape == signal_array.shape[:-1] - assert mapper.t1_map.mean() - self.t1 < 0.00001 - assert mapper.m0_map.mean() - self.m0 < 0.00001 - assert mapper.r1_map().mean() - 1 / self.t1 < 0.00001 + npt.assert_almost_equal(mapper.t1_map.mean(), self.t1) + npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r1_map().mean(), 1 / self.t1) + npt.assert_almost_equal(mapper.r2.mean(), 1) + + # Auto Threaded + mapper = T1(signal_array, self.t, self.affine, multithread='auto') + assert mapper.shape == signal_array.shape[:-1] + npt.assert_almost_equal(mapper.t1_map.mean(), self.t1) + npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r1_map().mean(), 1 / self.t1) + npt.assert_almost_equal(mapper.r2.mean(), 1) def test_three_param_fit(self): # Make the signal into a 4D array @@ -115,35 +125,39 @@ def test_three_param_fit(self): mapper = T1(signal_array, self.t, self.affine, parameters=3, multithread=True) assert mapper.shape == signal_array.shape[:-1] - assert mapper.t1_map.mean() - self.t1 < 0.00001 - assert mapper.m0_map.mean() - self.m0 < 0.00001 - assert mapper.eff_map.mean() - self.eff < 0.00005 - assert mapper.r1_map().mean() - 1 / self.t1 < 0.00001 + npt.assert_almost_equal(mapper.t1_map.mean(), self.t1) + npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.eff_map.mean(), self.eff) + npt.assert_almost_equal(mapper.r1_map().mean(), 1 / self.t1) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Single Threaded mapper = T1(signal_array, self.t, self.affine, parameters=3, multithread=False) assert mapper.shape == signal_array.shape[:-1] - assert mapper.t1_map.mean() - self.t1 < 0.00001 - assert mapper.m0_map.mean() - self.m0 < 0.00001 - assert mapper.eff_map.mean() - self.eff < 0.00005 - assert mapper.r1_map().mean() - 1 / self.t1 < 0.00001 + npt.assert_almost_equal(mapper.t1_map.mean(), self.t1) + npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.eff_map.mean(), self.eff) + npt.assert_almost_equal(mapper.r1_map().mean(), 1 / self.t1) + npt.assert_almost_equal(mapper.r2.mean(), 1) def test_tss(self): mapper = T1(self.correct_signal_two_param_tss, self.t, self.affine, tss=10) assert mapper.shape == self.correct_signal_two_param_tss.shape[:-1] - assert mapper.t1_map.mean() - self.t1 < 0.00001 - assert mapper.m0_map.mean() - self.m0 < 0.00001 - assert mapper.r1_map().mean() - 1 / self.t1 < 0.00001 + npt.assert_almost_equal(mapper.t1_map.mean(), self.t1) + npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r1_map().mean(), 1 / self.t1) + npt.assert_almost_equal(mapper.r2.mean(), 1) def test_tss_axis(self): signal_array = np.swapaxes(self.correct_signal_two_param_tss, 0, 1) mapper = T1(signal_array, self.t, self.affine, tss=10, tss_axis=0) - assert mapper.t1_map.mean() - self.t1 < 0.00001 - assert mapper.m0_map.mean() - self.m0 < 0.00001 - assert mapper.r1_map().mean() - 1 / self.t1 < 0.00001 + npt.assert_almost_equal(mapper.t1_map.mean(), self.t1) + npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r1_map().mean(), 1 / self.t1) + npt.assert_almost_equal(mapper.r2.mean(), 1) def test_failed_fit(self): # Make the signal, where the fitting is expected to fail, into 4D array @@ -154,20 +168,22 @@ def test_failed_fit(self): parameters=2, multithread=True) assert mapper_two_param.shape == signal_array.shape[:-1] # Voxels that fail to fit are set to zero - assert mapper_two_param.t1_map.mean() == 0.0 - assert mapper_two_param.t1_err.mean() == 0.0 - assert mapper_two_param.m0_map.mean() == 0.0 - assert mapper_two_param.m0_err.mean() == 0.0 + npt.assert_equal(mapper_two_param.t1_map.mean(), 0) + npt.assert_equal(mapper_two_param.t1_err.mean(), 0) + npt.assert_equal(mapper_two_param.m0_map.mean(), 0) + npt.assert_equal(mapper_two_param.m0_err.mean(), 0) + npt.assert_equal(mapper_two_param.r2.mean(), 0) # Fail to fit using the 3 parameter equation mapper_three_param = T1(signal_array, self.t, self.affine, parameters=3, multithread=True) assert mapper_three_param.shape == signal_array.shape[:-1] # Voxels that fail to fit are set to zero - assert mapper_three_param.t1_map.mean() == 0.0 - assert mapper_three_param.t1_err.mean() == 0.0 - assert mapper_three_param.m0_map.mean() == 0.0 - assert mapper_three_param.m0_err.mean() == 0.0 + npt.assert_equal(mapper_three_param.t1_map.mean(), 0) + npt.assert_equal(mapper_three_param.t1_err.mean(), 0) + npt.assert_equal(mapper_three_param.m0_map.mean(), 0) + npt.assert_equal(mapper_three_param.m0_err.mean(), 0) + npt.assert_equal(mapper_two_param.r2.mean(), 0) def test_mask(self): signal_array = np.tile(self.correct_signal_two_param, (10, 10, 3, 1)) @@ -177,16 +193,16 @@ def test_mask(self): mask[:5, ...] = False mapper = T1(signal_array, self.t, self.affine, mask=mask) assert mapper.shape == signal_array.shape[:-1] - assert mapper.t1_map[5:, ...].mean() - self.t1 < 0.00001 - assert mapper.t1_map[:5, ...].mean() < 0.00001 + npt.assert_almost_equal(mapper.t1_map[5:, ...].mean(), self.t1) + npt.assert_equal(mapper.t1_map[:5, ...].mean(), 0) # Int mask mask = np.ones(signal_array.shape[:-1]) mask[:5, ...] = 0 mapper = T1(signal_array, self.t, self.affine, mask=mask) assert mapper.shape == signal_array.shape[:-1] - assert mapper.t1_map[5:, ...].mean() - self.t1 < 0.00001 - assert mapper.t1_map[:5, ...].mean() < 0.00001 + npt.assert_almost_equal(mapper.t1_map[5:, ...].mean(), self.t1) + npt.assert_equal(mapper.t1_map[:5, ...].mean(), 0) def test_mismatched_raw_data_and_inversion_lengths(self): @@ -247,9 +263,10 @@ def test_tss_valid_axis(self): affine=self.affine, tss=1, tss_axis=2) def test_molli_2p_warning(self): + signal_array = np.tile(self.correct_signal_three_param, (10, 10, 3, 1)) with pytest.warns(UserWarning): - mapper = T1(pixel_array=np.zeros((5, 5, 5)), - inversion_list=np.linspace(0, 2000, 5), + mapper = T1(pixel_array=signal_array, + inversion_list=self.t, affine=self.affine, parameters=2, molli=True) def test_real_data(self): @@ -315,13 +332,14 @@ def test_to_nifti(self): mapper.to_nifti(output_directory='test_output', base_file_name='t1test', maps='all') output_files = os.listdir('test_output') - assert len(output_files) == 8 + assert len(output_files) == 9 assert 't1test_eff_err.nii.gz' in output_files assert 't1test_eff_map.nii.gz' in output_files assert 't1test_m0_err.nii.gz' in output_files assert 't1test_m0_map.nii.gz' in output_files assert 't1test_mask.nii.gz' in output_files assert 't1test_r1_map.nii.gz' in output_files + assert 't1test_r2.nii.gz' in output_files assert 't1test_t1_err.nii.gz' in output_files assert 't1test_t1_map.nii.gz' in output_files diff --git a/ukat/mapping/tests/test_t2.py b/ukat/mapping/tests/test_t2.py index 042841fb..fc3c52d9 100644 --- a/ukat/mapping/tests/test_t2.py +++ b/ukat/mapping/tests/test_t2.py @@ -40,19 +40,21 @@ def test_2p_exp_fit(self): assert mapper.shape == signal_array.shape[:-1] npt.assert_almost_equal(mapper.t2_map.mean(), self.t2) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) - npt.assert_almost_equal(mapper.r2_map().mean(), 1 / self.t2) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Single Threaded mapper = T2(signal_array, self.t, self.affine, multithread=False) assert mapper.shape == signal_array.shape[:-1] npt.assert_almost_equal(mapper.t2_map.mean(), self.t2) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Auto Threaded mapper = T2(signal_array, self.t, self.affine, multithread='auto') assert mapper.shape == signal_array.shape[:-1] npt.assert_almost_equal(mapper.t2_map.mean(), self.t2) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Fail to fit mapper = T2(signal_array[..., ::-1], self.t, self.affine, @@ -72,6 +74,7 @@ def test_3p_exp_fit(self): npt.assert_almost_equal(mapper.t2_map.mean(), self.t2) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.b_map.mean(), self.b) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Single Threaded mapper = T2(signal_array, self.t, self.affine, multithread=False, @@ -80,6 +83,7 @@ def test_3p_exp_fit(self): npt.assert_almost_equal(mapper.t2_map.mean(), self.t2) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.b_map.mean(), self.b) + npt.assert_almost_equal(mapper.r2.mean(), 1) def test_threshold_fit(self): # Make the signal into a 4D array @@ -91,6 +95,7 @@ def test_threshold_fit(self): assert mapper.shape == signal_array.shape[:-1] npt.assert_almost_equal(mapper.t2_map.mean(), self.t2) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Single Threaded mapper = T2(signal_array, self.t, self.affine, multithread=False, @@ -98,6 +103,7 @@ def test_threshold_fit(self): assert mapper.shape == signal_array.shape[:-1] npt.assert_almost_equal(mapper.t2_map.mean(), self.t2) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) + npt.assert_almost_equal(mapper.r2.mean(), 1) def test_mask(self): signal_array = np.tile(self.correct_signal, (10, 10, 3, 1)) @@ -147,8 +153,8 @@ def test_real_data(self): 0.0, 568.160604] gold_standard_3p_exp = [9.881218e+01, 4.294529e+01, 3.489657e-02, 5.681606e+02] - gold_standard_thresh = [106.354968, 39.894933, - 0.0, 568.160591] + gold_standard_thresh = [106.351332, 39.904419, + 0.0, 568.160832] # 2p_exp method mapper = T2(image, te, self.affine) @@ -174,7 +180,7 @@ def test_real_data(self): def test_to_nifti(self): # Create a T2 map instance and test different export to NIFTI scenarios signal_array = np.tile(self.correct_signal, (10, 10, 3, 1)) - mapper = T2(signal_array, self.t, self.affine) + mapper = T2(signal_array, self.t, self.affine, method='3p_exp') if os.path.exists('test_output'): shutil.rmtree('test_output') @@ -184,7 +190,9 @@ def test_to_nifti(self): mapper.to_nifti(output_directory='test_output', base_file_name='t2test', maps='all') output_files = os.listdir('test_output') - assert len(output_files) == 6 + assert len(output_files) == 8 + assert 't2test_b_map.nii.gz' in output_files + assert 't2test_b_err.nii.gz' in output_files assert 't2test_m0_err.nii.gz' in output_files assert 't2test_m0_map.nii.gz' in output_files assert 't2test_mask.nii.gz' in output_files @@ -201,7 +209,7 @@ def test_to_nifti(self): output_files = os.listdir('test_output') assert len(output_files) == 0 - # Check that only t2 and r2 are saved. + # Check that only mask, t2 and r2 are saved. mapper.to_nifti(output_directory='test_output', base_file_name='t2test', maps=['mask', 't2', 'r2']) output_files = os.listdir('test_output') diff --git a/ukat/mapping/tests/test_t2_stimfit.py b/ukat/mapping/tests/test_t2_stimfit.py new file mode 100644 index 00000000..d6586de4 --- /dev/null +++ b/ukat/mapping/tests/test_t2_stimfit.py @@ -0,0 +1,275 @@ +import os +import shutil +import numpy as np +import numpy.testing as npt +import pytest +from ukat.data import fetch +from ukat.mapping.t2_stimfit import StimFitModel, T2StimFit, _epgsig +from ukat.utils import arraystats + + +class TestStimFitModel: + def test_invalid_model(self): + with pytest.raises(ValueError): + model = StimFitModel(mode='invalid_model') + + def test_invalid_comp(self): + with pytest.raises(ValueError): + model = StimFitModel(n_comp=0) + + with pytest.raises(ValueError): + model = StimFitModel(n_comp=4) + + with pytest.raises(ValueError): + model = StimFitModel(n_comp='one') + + def test_invalid_vendor(self): + with pytest.warns(UserWarning): + model = StimFitModel(ukrin_vendor='brucker') + + def test_mode_switching(self): + model = StimFitModel(mode='selective') + assert model.mode == 'selective' + assert model.opt['RFe']['angle'] == 90 + assert model.opt['Dz'] == [-0.5, 0.5] + + model = StimFitModel(mode='non_selective') + assert model.mode == 'non_selective' + assert model.opt['RFe']['angle'] == 90 + with pytest.raises(KeyError): + model.opt['Dz'] + + def test_n_comp_switching(self): + model = StimFitModel(n_comp=1) + assert model.n_comp == 1 + assert model.opt['lsq']['Ncomp'] == 1 + assert model.opt['lsq']['X0'][2] == 1 + + model = StimFitModel(n_comp=2) + assert model.n_comp == 2 + assert model.opt['lsq']['Ncomp'] == 2 + assert model.opt['lsq']['X0'][2] == 0.331 + + model = StimFitModel(n_comp=3) + assert model.n_comp == 3 + assert model.opt['lsq']['Ncomp'] == 3 + assert model.opt['lsq']['X0'][2] == 0.036 + + def test_vendor_switching(self): + model = StimFitModel(mode='selective', ukrin_vendor='ge') + assert model.vendor == 'ge' + assert model.opt['RFe']['tau'] == 2000 / 1e6 + + model = StimFitModel(mode='selective', ukrin_vendor='philips') + assert model.vendor == 'philips' + assert model.opt['RFe']['tau'] == 3820 / 1e6 + + model = StimFitModel(mode='selective', ukrin_vendor='siemens') + assert model.vendor == 'siemens' + assert model.opt['RFe']['tau'] == 3072 / 1e6 + + def test_set_rf(self): + model = StimFitModel(mode='selective', ukrin_vendor='ge') + npt.assert_almost_equal(model.opt['RFe']['RF'][-1], 2.58327955e-07) + npt.assert_almost_equal(model.opt['RFe']['alpha'][-1], 0.04164681, + decimal=5) + npt.assert_almost_equal(model.opt['RFr']['RF'][-1], -3.55622605e-05) + npt.assert_almost_equal(model.opt['RFr']['alpha'][-1], 1.97107315) + + model = StimFitModel(mode='selective', ukrin_vendor='philips') + npt.assert_almost_equal(model.opt['RFe']['RF'][-1], 3.59081850e-04) + npt.assert_almost_equal(model.opt['RFe']['alpha'][-1], 0.05002553, + decimal=5) + npt.assert_almost_equal(model.opt['RFr']['RF'][-1], 0.00473865) + npt.assert_almost_equal(model.opt['RFr']['alpha'][-1], 0.46764775, + decimal=5) + + model = StimFitModel(mode='selective', ukrin_vendor='siemens') + npt.assert_almost_equal(model.opt['RFe']['RF'][-1], 1.68182263e-07) + npt.assert_almost_equal(model.opt['RFe']['alpha'][-1], 0.07221162, + decimal=5) + npt.assert_almost_equal(model.opt['RFr']['RF'][-1], -3.71744163e-05) + npt.assert_almost_equal(model.opt['RFr']['alpha'][-1], 1.31133498) + + def test_getters(self): + model = StimFitModel(mode='selective', ukrin_vendor='ge') + assert len(model.get_opt()) == 11 + assert len(model.get_lsq()) == 6 + assert len(model.get_rfe()) == 7 + assert len(model.get_rfr()) == 8 + + +class TestT2StimFit: + image_ge, affine_ge, te_ge = fetch.t2_ge(1) + image_ge = image_ge[35:45, 50:65, 2:4, :] # Crop to speed up tests + image_philips, affine_philips, te_philips = fetch.t2_philips(2) + image_philips = image_philips[35:45, 50:65, 2:4, :] + image_siemens, affine_siemens, te_siemens = fetch.t2_siemens(1) + image_siemens = image_siemens[35:45, 40:55, 2:4, :] + + # selective + def test_selectiveness(self): + # Selective + model = StimFitModel(mode='selective', ukrin_vendor='ge') + mapper = T2StimFit(self.image_ge, self.affine_ge, model) + stats = arraystats.ArrayStats(mapper.t2_map).calculate() + npt.assert_allclose([stats["mean"]["3D"], stats["std"]["3D"], + stats["min"]["3D"], stats["max"]["3D"]], + [164.331581, 199.057747, 51.268116, 1455.551225], + rtol=1e-2, atol=0.25) + + # Non-selective + model = StimFitModel(mode='non_selective', ukrin_vendor='ge') + mapper = T2StimFit(self.image_ge, self.affine_ge, model) + stats = arraystats.ArrayStats(mapper.t2_map).calculate() + npt.assert_allclose([stats["mean"]["3D"], stats["std"]["3D"], + stats["min"]["3D"], stats["max"]["3D"]], + [165.994692, 203.583211, 51.827107, 1497.168001], + rtol=1e-2, atol=0.25) + + # n_comp + def test_n_comp(self): + # Two Components + model = StimFitModel(mode='selective', ukrin_vendor='ge', n_comp=2) + mapper = T2StimFit(self.image_ge[0, 14, :, :], self.affine_ge, model) + + npt.assert_allclose([mapper.t2_map[0, 0]], + [117.991529], + rtol=5e-2, atol=0.1) + + # Three Components + # Cant get this to be stable across operating systems so commented out. + + # model = StimFitModel(mode='selective', ukrin_vendor='ge', n_comp=3) + # mapper = T2StimFit(self.image_ge[0, 14, :, :], self.affine_ge, model) + # npt.assert_allclose([mapper.t2_map[0, 2]], + # [1245.291925], + # rtol=5e-2, atol=0.1) + + # vendor + def test_vendor(self): + # Philips + model = StimFitModel(mode='selective', ukrin_vendor='philips') + mapper = T2StimFit(self.image_philips, self.affine_philips, model) + stats = arraystats.ArrayStats(mapper.t2_map).calculate() + npt.assert_allclose([stats["mean"]["3D"], stats["std"]["3D"], + stats["min"]["3D"], stats["max"]["3D"]], + [281.52594, 596.832203, 36.470879, 3000.0], + rtol=1e-6, atol=1e-4) + + # Siemens + model = StimFitModel(mode='selective', ukrin_vendor='siemens') + mapper = T2StimFit(self.image_siemens, self.affine_siemens, model) + stats = arraystats.ArrayStats(mapper.t2_map).calculate() + npt.assert_allclose([stats["mean"]["3D"], stats["std"]["3D"], + stats["min"]["3D"], stats["max"]["3D"]], + [120.47096, 190.454984, 26.621704, 2999.999651], + rtol=1e-5, atol=1e-2) + + # mask + def test_mask(self): + mask = self.image_ge[..., 0] > 3000 + model = StimFitModel(mode='non_selective', ukrin_vendor='ge') + mapper = T2StimFit(self.image_ge, self.affine_ge, model, mask=mask) + stats = arraystats.ArrayStats(mapper.t2_map).calculate() + npt.assert_allclose([stats["mean"]["3D"], stats["std"]["3D"], + stats["min"]["3D"], stats["max"]["3D"]], + [156.693513, 207.797, 0.0, 1497.168001], + rtol=1e-2, atol=0.25) + + # threading + def test_st(self): + model = StimFitModel(mode='non_selective', ukrin_vendor='ge') + mapper = T2StimFit(self.image_ge, self.affine_ge, model, + multithread=False) + stats = arraystats.ArrayStats(mapper.t2_map).calculate() + npt.assert_allclose([stats["mean"]["3D"], stats["std"]["3D"], + stats["min"]["3D"], stats["max"]["3D"]], + [165.994692, 203.583211, 51.827107, 1497.168001], + rtol=1e-2, atol=0.25) + + # normalisation + def test_normalisation_warning(self): + with pytest.warns(UserWarning): + model = StimFitModel(mode='non_selective', ukrin_vendor='ge') + mapper = T2StimFit(self.image_ge * 2, self.affine_ge, model, + norm=False) + + def test_etl_signal_exception(self): + with pytest.raises(Exception): + model = StimFitModel(mode='non_selective', ukrin_vendor='ge') + mapper = T2StimFit(self.image_ge[..., :-2], self.affine_ge, model) + + # to_nifti + def test_to_nifti(self): + mask = self.image_ge[..., 0] > 3000 + model = StimFitModel(mode='non_selective', ukrin_vendor='ge') + mapper = T2StimFit(self.image_ge, self.affine_ge, model, mask=mask) + + if os.path.exists('test_output'): + shutil.rmtree('test_output') + os.makedirs('test_output', exist_ok=True) + + # Check all is saved. + mapper.to_nifti(output_directory='test_output', + base_file_name='t2stimfittest', maps='all') + output_files = os.listdir('test_output') + assert len(output_files) == 5 + assert 't2stimfittest_b1_map.nii.gz' in output_files + assert 't2stimfittest_m0_map.nii.gz' in output_files + assert 't2stimfittest_mask.nii.gz' in output_files + assert 't2stimfittest_r2_map.nii.gz' in output_files + assert 't2stimfittest_t2_map.nii.gz' in output_files + + for f in os.listdir('test_output'): + os.remove(os.path.join('test_output', f)) + + # Check that no files are saved. + mapper.to_nifti(output_directory='test_output', + base_file_name='t2stimfittest', maps=[]) + output_files = os.listdir('test_output') + assert len(output_files) == 0 + + # Check that only t2, mask and r2 are saved. + mapper.to_nifti(output_directory='test_output', + base_file_name='t2stimfittest', maps=['mask', 't2', + 'r2']) + output_files = os.listdir('test_output') + assert len(output_files) == 3 + assert 't2stimfittest_mask.nii.gz' in output_files + assert 't2stimfittest_t2_map.nii.gz' in output_files + assert 't2stimfittest_r2_map.nii.gz' in output_files + + for f in os.listdir('test_output'): + os.remove(os.path.join('test_output', f)) + + # Check that it fails when no maps are given + with pytest.raises(ValueError): + mapper.to_nifti(output_directory='test_output', + base_file_name='t2stimfittest', maps='') + + # Delete 'test_output' folder + shutil.rmtree('test_output') + + +class TestEpg: + t2 = 0.1 + b1 = 0.95 + + def test_selective(self): + model = StimFitModel(mode='selective', ukrin_vendor='ge') + sig = _epgsig(self.t2, self.b1, model.opt, model.mode) + npt.assert_allclose(sig, np.array([0.53193464, 0.48718256, 0.41393849, + 0.37639148, 0.32247532, 0.29132453, + 0.25050307, 0.22604609, 0.19430487, + 0.1755666]), + rtol=1e-5, atol=1e-5) + + def test_non_selective(self): + model = StimFitModel(mode='non_selective', ukrin_vendor='ge') + sig = _epgsig(self.t2, self.b1, model.opt, model.mode) + npt.assert_allclose(sig, np.array([0.87087025, 0.7713902, 0.6727603, + 0.59694589, 0.51965957, 0.46200336, + 0.40135138, 0.35760991, 0.30993556, + 0.27684428]), + rtol=1e-5, atol=1e-5) diff --git a/ukat/mapping/tests/test_t2star.py b/ukat/mapping/tests/test_t2star.py index e04568db..5f5501da 100644 --- a/ukat/mapping/tests/test_t2star.py +++ b/ukat/mapping/tests/test_t2star.py @@ -20,6 +20,7 @@ class TestT2Star: 1893.85093652, 1783.56164391, 1679.6950997, 1581.87727213, 1489.75591137, 1402.99928103]) affine = np.eye(4) + def test_two_param_eq(self): signal = two_param_eq(self.t, self.t2star, self.m0) npt.assert_allclose(signal, self.correct_signal, rtol=1e-6, atol=1e-8) @@ -36,6 +37,7 @@ def test_loglin_fit(self): assert np.isnan(mapper.t2star_err.mean()) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.r2star_map().mean(), 1 / self.t2star) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Single Threaded mapper = T2Star(signal_array, self.t, self.affine, method='loglin', @@ -45,6 +47,7 @@ def test_loglin_fit(self): assert np.isnan(mapper.t2star_err.mean()) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.r2star_map().mean(), 1 / self.t2star) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Auto Threaded mapper = T2Star(signal_array, self.t, self.affine, method='loglin', @@ -54,6 +57,7 @@ def test_loglin_fit(self): assert np.isnan(mapper.t2star_err.mean()) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.r2star_map().mean(), 1 / self.t2star) + npt.assert_almost_equal(mapper.r2.mean(), 1) def test_2p_exp_fit(self): # Make the signal into a 4D array @@ -67,6 +71,7 @@ def test_2p_exp_fit(self): npt.assert_almost_equal(mapper.t2star_err.mean(), 7.395706644238e-11) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.r2star_map().mean(), 1 / self.t2star) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Single Threaded mapper = T2Star(signal_array, self.t, self.affine, method='2p_exp', @@ -76,6 +81,7 @@ def test_2p_exp_fit(self): npt.assert_almost_equal(mapper.t2star_err.mean(), 7.395706644238e-11) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.r2star_map().mean(), 1 / self.t2star) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Auto Threaded mapper = T2Star(signal_array, self.t, self.affine, method='2p_exp', @@ -85,14 +91,16 @@ def test_2p_exp_fit(self): npt.assert_almost_equal(mapper.t2star_err.mean(), 7.395706644238e-11) npt.assert_almost_equal(mapper.m0_map.mean(), self.m0) npt.assert_almost_equal(mapper.r2star_map().mean(), 1 / self.t2star) + npt.assert_almost_equal(mapper.r2.mean(), 1) # Fail to fit mapper = T2Star(signal_array[..., ::-1], self.t, self.affine, method='2p_exp', multithread=True) assert mapper.shape == signal_array.shape[:-1] # Voxels that fail to fit are set to zero - npt.assert_almost_equal(mapper.t2star_map.mean(), 0.0) - npt.assert_almost_equal(mapper.t2star_err.mean(), 0.0) + npt.assert_almost_equal(mapper.t2star_map.mean(), 0) + npt.assert_almost_equal(mapper.t2star_err.mean(), 0) + npt.assert_almost_equal(mapper.r2.mean(), 0) def test_mask(self): signal_array = np.tile(self.correct_signal, (10, 10, 3, 1)) @@ -128,10 +136,11 @@ def test_to_nifti(self): mapper.to_nifti(output_directory='test_output', base_file_name='t2startest', maps='all') output_files = os.listdir('test_output') - assert len(output_files) == 6 + assert len(output_files) == 7 assert 't2startest_m0_err.nii.gz' in output_files assert 't2startest_m0_map.nii.gz' in output_files assert 't2startest_mask.nii.gz' in output_files + assert 't2startest_r2.nii.gz' in output_files assert 't2startest_r2star_map.nii.gz' in output_files assert 't2startest_t2star_err.nii.gz' in output_files assert 't2startest_t2star_map.nii.gz' in output_files @@ -170,9 +179,10 @@ def test_to_nifti(self): mapper.to_nifti(output_directory='test_output', base_file_name='t2startest', maps='all') output_files = os.listdir('test_output') - assert len(output_files) == 4 + assert len(output_files) == 5 assert 't2startest_m0_map.nii.gz' in output_files assert 't2startest_mask.nii.gz' in output_files + assert 't2startest_r2.nii.gz' in output_files assert 't2startest_r2star_map.nii.gz' in output_files assert 't2startest_t2star_map.nii.gz' in output_files diff --git a/ukat/qa/tests/test_snr.py b/ukat/qa/tests/test_snr.py index 05b75fa7..2d4d0c56 100644 --- a/ukat/qa/tests/test_snr.py +++ b/ukat/qa/tests/test_snr.py @@ -23,7 +23,7 @@ def test_automatic_masking(self): npt.assert_allclose([noise_mask_stats['mean']['3D'], noise_mask_stats['std']['3D'], noise_mask_stats['min']['3D'], - noise_mask_stats[ 'max']['3D']], + noise_mask_stats['max']['3D']], gold_standard_noise_mask, rtol=1e-6, atol=1e-4) npt.assert_allclose(isnr_obj.isnr, 45.968827) npt.assert_allclose([isnr_map_stats['mean']['3D'], diff --git a/ukat/utils/arraystats.py b/ukat/utils/arraystats.py index f596de6a..acc2a1fd 100644 --- a/ukat/utils/arraystats.py +++ b/ukat/utils/arraystats.py @@ -11,6 +11,7 @@ NOT_CALCULATED_MSG = 'Not calculated. See ArrayStats.calculate().' + class ArrayStats(): """Class to calculate array statistics (optionally within a mask) @@ -236,8 +237,8 @@ def calculate(self): elif self.image_ndims == 3: n = { '2D': n2.transpose()[0], - '3D': n4, # n4 because {statistic}4 always returns the result - } # over the entire array, which here is 3D + '3D': n4, # n4 because {statistic}4 always returns the result + } # over the entire array, which here is 3D mean = { '2D': mean2.transpose()[0], '3D': mean4, diff --git a/ukat/utils/tests/test_ge.py b/ukat/utils/tests/test_ge.py index 26c9d983..b6a5aa68 100644 --- a/ukat/utils/tests/test_ge.py +++ b/ukat/utils/tests/test_ge.py @@ -1,5 +1,4 @@ import numpy as np -import pytest from ukat.utils.ge import scale_b1 diff --git a/ukat/utils/tests/test_tools.py b/ukat/utils/tests/test_tools.py index ac6dc185..587900b2 100644 --- a/ukat/utils/tests/test_tools.py +++ b/ukat/utils/tests/test_tools.py @@ -6,7 +6,6 @@ class TestConvertToPiRange: - # Gold Standard = [mean, std, minimum, maximum] # Input: {np.arange(12).reshape((2, 2, 3)) - 6 * np.ones((2, 2, 3))} gold_standard = [-7.401486830834377e-17, 1.9718077939258474, @@ -22,8 +21,8 @@ def test_pi_range_result(self): pi_range_calculated = tools.convert_to_pi_range(self.array) stats = arraystats.ArrayStats(pi_range_calculated).calculate() npt.assert_allclose([stats["mean"]["3D"], stats["std"]["3D"], - stats["min"]["3D"], stats["max"]["3D"]], - self.gold_standard, rtol=1e-6, atol=1e-4) + stats["min"]["3D"], stats["max"]["3D"]], + self.gold_standard, rtol=1e-6, atol=1e-4) def test_if_ranges(self): # Test for values > 3.2 @@ -54,7 +53,6 @@ def test_input_array_type_assertion(self): class TestResizeArray: - # Create arrays for testing array_2d = np.arange(100).reshape((10, 10)) array_3d = np.arange(500).reshape((10, 10, 5)) @@ -96,7 +94,6 @@ def test_input_array_type_assertion(self): class TestMaskSlices: - shape = (2, 2, 3) # Create mask where all pixels from all slices are True full_mask = np.full(shape, True) diff --git a/ukat/utils/tools.py b/ukat/utils/tools.py index 932fb3e6..476d4497 100644 --- a/ukat/utils/tools.py +++ b/ukat/utils/tools.py @@ -114,7 +114,7 @@ def mask_slices(shape, slices, mask=None): s_min = min(slices) s_max = max(slices) - if not(s_min >= 0 and s_max+1 <= shape[2]): + if not(s_min >= 0 and s_max + 1 <= shape[2]): msg = f"The elements of `slices` must be > 0 and <= {shape[2]-1}" raise ValueError(msg)