This folder contains code to train and validate an e2e-VarNet (https://arxiv.org/pdf/2004.06688.pdf) for accelerated MRI reconstruction. Accelerated MRI reconstruction is a compressed sensing task where the goal is to recover a ground-truth image from an under-sampled measurement. The under-sampled measurement is based on the frequency domain and is often called the
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Please see the dataset description for our dataset preparation.
We have already provided a model checkpoint varnet_mri_reconstruction.pt for a VarNet with 30,069,558 parameters. To obtain this checkpoint, we trained
a VarNet with the default hyper-parameters in train.py on our T2 subset of the brain dataset. The user can train their model on an arbitrary portion of the dataset.
The training dynamics for our checkpoint are depicted in the figure below.
Running train.py trains a VarNet. The default setup automatically detects a GPU for training; if not available, the CPU will be used.
# Run this to get a full list of training arguments
python ./train.py -h
# This is an example of calling train.py
python ./train.py
--data_path_train train_dir \
--data_path_val val_dir \
--exp varnet_mri_recon \
--exp_dir ./ \
--mask_type equispaced \
--num_epochs 50 \
--num_workers 0 \
--lr 0.00001
The notebook inference.ipynb contains an example to perform inference. The average SSIM score over the test subset is computed and then
one sample is picked for visualization.
Our checkpoint achieves 0.9650 SSIM on our test subset which is comparable to the original result reported on the
fastMRI public leaderboard (which is 0.9606 SSIM). Note that the results reported
on the leaderboard are for the unreleased test set. Moreover, the leaderboard model is trained on the validation set.
Data used in the preparation of this tutorial were obtained from the NYU fastMRI Initiative database (fastmri.med.nyu.edu).[citation of Knoll et al Radiol Artif Intell. 2020 Jan 29;2(1):e190007. doi: 10.1148/ryai.2020190007. (https://pubs.rsna.org/doi/10.1148/ryai.2020190007), and the arXiv paper: https://arxiv.org/abs/1811.08839] As such, NYU fastMRI investigators provided data but did not participate in analysis or writing of this tutorial. A listing of NYU fastMRI investigators, subject to updates, can be found at:fastmri.med.nyu.edu. The primary goal of fastMRI is to test whether machine learning can aid in the reconstruction of medical images.