language:
- en
license: apache-2.0
library_name: atommic
datasets:
- StanfordKnees2019
thumbnail: null
tags:
- image-reconstruction
- RVN
- ATOMMIC
- pytorch
model-index:
- name: REC_RVN_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM
results: []
Model Overview
Recurrent Variational Network (RVN) for 12x accelerated MRI Reconstruction on the StanfordKnees2019 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_RVN_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_RVN_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
model:
model_name: RVN
in_channels: 2
recurrent_hidden_channels: 64
recurrent_num_layers: 4
num_steps: 8
no_parameter_sharing: true
learned_initializer: true
initializer_initialization: "SENSEe"
initializer_channels:
- 32
- 32
- 64
- 64
initializer_dilations:
- 1
- 1
- 2
- 4
initializer_multiscale: 1
accumulate_predictions: false
dimensionality: 2
reconstruction_loss:
wasserstein: 1.0
Training
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 = 0.001201 +/- 0.005875 NMSE = 0.04067 +/- 0.1203 PSNR = 31.96 +/- 6.899 SSIM = 0.7781 +/- 0.3002
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