REC_VarNet_AHEAD_gaussian2d_12x

Model Overview

Variational Network (VarNet) for 12x accelerated MRI Reconstruction on the AHEAD 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_VarNet_AHEAD_gaussian2d_12x/blob/main/REC_VarNet_AHEAD_gaussian2d_12x.atommic
mode: test

Usage

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

Model Architecture

model:
  model_name: VN
  num_cascades: 8
  channels: 18
  pooling_layers: 4
  padding_size: 11
  normalize: true
  no_dc: false
  dimensionality: 2
  num_echoes: 4
  reconstruction_loss:
    ssim: 1.0

Training

  optim:
    name: adamw
    lr: 1e-4
    betas:
      - 0.9
      - 0.999
    weight_decay: 0.0
    sched:
        name: PolynomialHoldDecayAnnealing
        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 = 0.0006782 +/- 0.0008642 NMSE = 0.04725 +/- 0.06244 PSNR = 32.39 +/- 4.795 SSIM = 0.8935 +/- 0.05252

Limitations

This model was trained on very few subjects on the AHEAD dataset. It is not guaranteed to generalize to other datasets.

References

[1] ATOMMIC

[2] Alkemade A, Mulder MJ, Groot JM, et al. The Amsterdam Ultra-high field adult lifespan database (AHEAD): A freely available multimodal 7 Tesla submillimeter magnetic resonance imaging database. NeuroImage 2020;221.