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wdika/
QMRI_qCIRIM_AHEAD_gaussian2d_12x

Name: QMRI_qCIRIM_AHEAD_gaussian2d_12x
Availability: InStock

atommic

quantitative-mri-mapping

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Model Overview

quantitative Cascades of Independently Recurrent Inference Machines (qCIRIM) for 12x accelerated quantitative MRI mapping of R2*, S0, B0, phi maps 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

base

pretrained: true
checkpoint: https://huggingface.co/wdika/QMRI_qCIRIM_AHEAD_gaussian2d_12x/blob/main/QMRI_qCIRIM_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

base

model:
  model_name: qCIRIM
  use_reconstruction_module: false
  quantitative_module_recurrent_layer: IndRNN
  quantitative_module_conv_filters:
    - 64
    - 64
    - 4
  quantitative_module_conv_kernels:
    - 5
    - 3
    - 3
  quantitative_module_conv_dilations:
    - 1
    - 2
    - 1
  quantitative_module_conv_bias:
    - true
    - true
    - false
  quantitative_module_recurrent_filters:
    - 64
    - 64
    - 0
  quantitative_module_recurrent_kernels:
    - 1
    - 1
    - 0
  quantitative_module_recurrent_dilations:
    - 1
    - 1
    - 0
  quantitative_module_recurrent_bias:
    - true
    - true
    - false
  quantitative_module_depth: 2
  quantitative_module_time_steps: 8
  quantitative_module_conv_dim: 2
  quantitative_module_num_cascades: 5
  quantitative_module_no_dc: true
  quantitative_module_keep_prediction: true
  quantitative_module_accumulate_predictions: true
  quantitative_module_signal_forward_model_sequence: MEGRE
  quantitative_module_dimensionality: 2
  quantitative_maps_scaling_factor: 1e-3
  quantitative_maps_regularization_factors:
    - 150.0
    - 150.0
    - 1000.0
    - 150.0
  quantitative_loss:
    ssim: 1.0
  kspace_quantitative_loss: false
  total_quantitative_loss_weight: 1.0  # balance between reconstruction and quantitative loss
  quantitative_parameters_regularization_factors:
    - R2star: 1.0
    - S0: 1.0
    - B0: 1.0
    - phi: 1.0

Training

base

  optim:
    name: adam
    lr: 1e-4
    betas:
      - 0.9
      - 0.98
    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 qmri task, with --evaluation_type per_slice.

Results

Evaluation against R2*, S0, B0, phi targets

12x: MSE = 0.004702 +/- 0.02991 NMSE = 0.1239 +/- 0.3383 PSNR = 28.28 +/- 11.31 SSIM = 0.8814 +/- 0.1774

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.