pretrained: true
checkpoint: https://huggingface.co/wdika/REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
mode: test

Usage

You need to download the Stanford Knees 2019 dataset to effectively use this model. Check the StanfordKnees2019 page for more information.

Model Architecture

base

model:
  model_name: CIRIM
  recurrent_layer: IndRNN
  conv_filters:
    - 64
    - 64
    - 2
  conv_kernels:
    - 5
    - 3
    - 3
  conv_dilations:
    - 1
    - 2
    - 1
  conv_bias:
    - true
    - true
    - false
  recurrent_filters:
    - 64
    - 64
    - 0
  recurrent_kernels:
    - 1
    - 1
    - 0
  recurrent_dilations:
    - 1
    - 1
    - 0
  recurrent_bias:
    - true
    - true
    - false
  depth: 2
  time_steps: 8
  conv_dim: 2
  num_cascades: 5
  no_dc: true
  keep_prediction: true
  accumulate_predictions: true
  dimensionality: 2
  reconstruction_loss:
    wasserstein: 1.0

Training

base

  optim:
    name: adamw
    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 SENSE targets

12x: MSE = 0.001081 +/- 0.005786 NMSE = 0.03494 +/- 0.09865 PSNR = 32.77 +/- 7.234 SSIM = 0.7955 +/- 0.311

Limitations

This model was trained on the StanfordKnees2019 batch0 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] Epperson K, Rt R, Sawyer AM, et al. Creation of Fully Sampled MR Data Repository for Compressed SENSEing of the Knee. SMRT Conference 2013;2013:1