wdika/

REC_VSNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM

Variable-Splitting Net (VSNet) for 4x & 8x accelerated MRI Reconstruction on the fastMRIBrainsMulticoil dataset.

To train, fine-tune, or test the model you will need to install ATOMMIC. We recommend you install it after you've installed latest Pytorch version.

The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Corresponding configuration YAML files can be found here.

You need to download the fastMRI Brains dataset to effectively use this model. Check the fastMRIBrainsMulticoil page for more information.

To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use targets configuration files.

Evaluation can be performed using the evaluation script for the reconstruction task, with --evaluation_type per_slice.

4x: MSE = 0.001207 +/- 0.003744 NMSE = 0.0315 +/- 0.07854 PSNR = 30.37 +/- 5.336 SSIM = 0.8555 +/- 0.1964

8x: MSE = 0.002525 +/- 0.004475 NMSE = 0.06668 +/- 0.1296 PSNR = 26.88 +/- 5.433 SSIM = 0.7955 +/- 0.1969

This model was trained on the fastMRIBrainsMulticoil batch0 dataset using a UNet coil sensitivity maps estimation and Geometric Decomposition Coil-Compressions to 1-coil, and might differ from the results reported on the challenge leaderboard.

[1] ATOMMIC

[2] Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans Med Imaging. 2021 Sep;40(9):2306-2317. doi: 10.1109/TMI.2021.3075856. Epub 2021 Aug 31. PMID: 33929957; PMCID: PMC8428775.