wdika/REC_JointICNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM

Model Overview

Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (JointICNet) for 4x & 8x accelerated MRI Reconstruction on the fastMRIBrainsMulticoil dataset.

ATOMMIC: Training

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.

pip install atommic['all']

How to Use this Model

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.

Automatically instantiate the model

pretrained: true
checkpoint: https://huggingface.co/wdika/REC_JointICNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_JointICNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
mode: test

Usage

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

Model Architecture

model:
  model_name: JointICNet
  num_iter: 2
  kspace_unet_num_filters: 16
  kspace_unet_num_pool_layers: 2
  kspace_unet_dropout_probability: 0.0
  kspace_unet_padding_size: 11
  kspace_unet_normalize: true
  imspace_unet_num_filters: 16
  imspace_unet_num_pool_layers: 2
  imspace_unet_dropout_probability: 0.0
  imspace_unet_padding_size: 11
  imspace_unet_normalize: true
  sens_unet_num_filters: 16
  sens_unet_num_pool_layers: 2
  sens_unet_dropout_probability: 0.0
  sens_unet_padding_size: 11
  sens_unet_normalize: true
  dimensionality: 2

Training

  optim:
    name: adam
    lr: 1e-4
    betas:
      - 0.9
      - 0.999
    weight_decay: 0.0
    sched:
        name: InverseSquareRootAnnealing
        min_lr: 0.0
        last_epoch: -1
        warmup_ratio: 0.1

trainer:
  strategy: ddp_find_unused_parameters_false
  accelerator: gpu
  devices: 1
  num_nodes: 1
  max_epochs: 20
  precision: 16-mixed
  enable_checkpointing: false
  logger: false
  log_every_n_steps: 50
  check_val_every_n_epoch: -1
  max_steps: -1

Performance

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.

Results

Evaluation against RSS targets

4x: MSE = 0.001774 +/- 0.004331 NMSE = 0.04376 +/- 0.08693 PSNR = 28.57 +/- 5.497 SSIM = 0.8318 +/- 0.1976

8x: MSE = 0.003421 +/- 0.005284 NMSE = 0.08763 +/- 0.1835 PSNR = 25.5 +/- 5.384 SSIM = 0.7719 +/- 0.2019

Limitations

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.

References

[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.