Merge pull request #3 from torchcvnn/nir_cinejense

Nir cinejense

README.md

@@ -3,3 +3,4 @@
 `torchcvnn/examples` is a repository showcasing examples of using [torchcvnn](https://www.github.com/torchcvnn/torchcvnn).
 
 - [Classification of MNIST in the Fourier space with complex valued CNNs](./mnist_conv/README.md)
+- [Complex valued Neural Implicit Representation for cardiac reconstruction](./nir_cinejense/README.md)

nir_cinejense/README.md

@@ -0,0 +1,82 @@
+# Neural Implicit Representation for cardiac reconstruction
+
+This example reproduces the code from [https://github.com/MDL-UzL/CineJENSE](https://github.com/MDL-UzL/CineJENSE) along the paper "CineJENSE: Simultaneous Cine MRI Image Reconstruction and Sensitivity Map Estimation using Neural Representations" by Ziad Al-Haj Hemidi, Nora Vogt, Lucile Quillien, Christian Weihsbach, Mattias P. Heinrich, and Julien Oster.
+
+It is showcasing NIR for cardiac reconstruction. In a few words : MRI is
+sampling in the Fourier space as bands. The longer the exam, the more you
+collect bands from the Fourier representation. The task is to estimate the
+non-observed bands from the observed bands, hopefully to get an exam as short as
+possible but still observing the heart as if it was observed for a longer
+period.
+
+For running this example, you need to download the data from the [CMRxRecon MICCAI 2023
+challenge](https://cmrxrecon.github.io/Home.html). See also the github of the
+challenge to access the data [https://github.com/CmrxRecon/CMRxRecon2024](https://github.com/CmrxRecon/CMRxRecon2024)
+
+The data directory is expected to follow the structure used by [torchcvnn](https://torchcvnn.github.io/torchcvnn/modules/datasets.html#torchcvnn.datasets.MICCAI2023).
+
+	rootdir/ChallengeData/MultiCoil/cine/TrainingSet/P{id}/
+								- cine_sax.mat
+								- cin_lax.mat
+	rootdir/ChallengeData/MultiCoil/cine/TrainingSet/AccFactor04/P{id}/
+								- cine_sax.mat
+								- cine_sax_mask.mat
+								- cin_lax.mat
+								- cine_lax_mask.mat
+
+
+The script supports the three acceleration factors and both the Short Axis (SAX) and
+Long Axis (LAX).
+
+If multiple patient data are provided, the script will sample one of them randomly.
+
+The installation is done in two steps 
+
+```bash
+python -m pip install -r requirements.txt
+python -m pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
+```
+
+The tinycudann installation may complain if the version of torch, installed during the first step is not using the cuda version for which you have installed the librairies. You may need to overwrite the installed torch version by installing the one supporting the right cuda version and listed on [https://pytorch.org/get-started/previous-versions/](https://pytorch.org/get-started/previous-versions).
+
+Once the dependencies are installed and you have the data available, you should be able to run the code : 
+
+```
+python nir_miccai2023.py --rootdir /path/to/the/data --acc_factor ACC10 --view SAX
+```
+
+## Examples
+
+The examples below have been produced on a GTX GeForce 3090, taking 2 minutes per slice.
+
+They were executed with numpy==1.26.4 tinycudann==1.7 torchcvnn==0.8.0 torch==2.0.1
+
+### Acceleration Factor 4
+
+- ACC4, SAX, patient P002, mean PSNR= $42.19$ (mean over the $12$ frames, for slice number $5$)
+
+![ACC4, SAX, P002](https://github.com/torchcvnn/examples/blob/nir_cinejense/nir_cinejense/gifs/acc4_sax_p002.gif?raw=true)
+
+- ACC4, LAX, patient P012, mean PSNR= $41.97$ (mean over the $12$ frames, for slice number $1$)
+
+![ACC4, LAX, P012](https://github.com/torchcvnn/examples/blob/nir_cinejense/nir_cinejense/gifs/acc4_lax_p012.gif?raw=true)
+
+### Acceleration Factor 8
+
+- ACC8, SAX, patient P063, mean PSNR= $34.59$ (mean over the $12$ frames, for slice number $5$)
+
+![ACC8, SAX, P063](https://github.com/torchcvnn/examples/blob/nir_cinejense/nir_cinejense/gifs/acc8_sax_p063.gif?raw=true)
+
+- ACC8, LAX, patient P001, mean PSNR= $36.48$ (mean over the $12$ frames, for slice number $1$)
+
+![ACC8, LAX, P001](https://github.com/torchcvnn/examples/blob/nir_cinejense/nir_cinejense/gifs/acc8_lax_p001.gif?raw=true)
+
+### Acceleration Factor 10
+
+- ACC10, SAX, patient P107, mean PSNR= $34.83$ (mean over the $12$ frames, for slice number $5$)
+
+![ACC10, SAX, P107](https://github.com/torchcvnn/examples/blob/nir_cinejense/nir_cinejense/gifs/acc10_sax_p107.gif?raw=true)
+
+- ACC10, LAX, patient P014, mean PSNR= $31.44$ (mean over the $12$ frames, for slice number $1$)
+
+![ACC10, LAX, P014](https://github.com/torchcvnn/examples/blob/nir_cinejense/nir_cinejense/gifs/acc10_lax_p014.gif?raw=true)