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---
language:
- en
license: apache-2.0
library_name: atommic
datasets:
- StanfordKnees2019
thumbnail: null
tags:
- image-reconstruction
- VSNet
- ATOMMIC
- pytorch
model-index:
- name: REC_VSNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM
results: []
---
## Model Overview
Variable-Splitting Net (VSNet) 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](https://github.com/wdika/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](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf).
### Automatically instantiate the model
```base
pretrained: true
checkpoint: https://huggingface.co/wdika/REC_VSNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_VSNet_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](https://github.com/wdika/atommic/blob/main/projects/REC/StanfordKnees2019/README.md) page for more information.
## Model Architecture
```base
model:
model_name: VSNet
num_cascades: 10
imspace_model_architecture: CONV
imspace_in_channels: 2
imspace_out_channels: 2
imspace_conv_hidden_channels: 64
imspace_conv_n_convs: 4
imspace_conv_batchnorm: false
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](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf/targets) configuration files.
Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task, with --evaluation_type per_slice.
Results
-------
Evaluation against SENSE targets
--------------------------------
12x: MSE = 0.001976 +/- 0.005902 NMSE = 0.07433 +/- 0.1106 PSNR = 28.51 +/- 5.793 SSIM = 0.7084 +/- 0.289
## 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](https://github.com/wdika/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
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