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---
language:
- en
license: apache-2.0
library_name: atommic
datasets:
- CC359
thumbnail: null
tags:
- image-reconstruction
- UNet
- ATOMMIC
- pytorch
model-index:
- name: REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM
results: []
---
## Model Overview
UNet for 5x & 10x accelerated MRI Reconstruction on the CC359 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/CC359/conf).
### Automatically instantiate the model
```base
pretrained: true
checkpoint: https://huggingface.co/wdika/REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
mode: test
```
### Usage
You need to download the CC359 dataset to effectively use this model. Check the [CC359](https://github.com/wdika/atommic/blob/main/projects/REC/CC359/README.md) page for more information.
## Model Architecture
```base
model:
model_name: UNet
channels: 64
pooling_layers: 4
in_channels: 2
out_channels: 2
padding_size: 11
dropout: 0.0
normalize: true
norm_groups: 2
dimensionality: 2
reconstruction_loss:
l1: 0.1
ssim: 0.9
estimate_coil_sensitivity_maps_with_nn: true
```
## Training
```base
optim:
name: adamw
lr: 1e-4
betas:
- 0.9
- 0.999
weight_decay: 0.0
sched:
name: CosineAnnealing
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/CC359/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 RSS targets
------------------------------
5x: MSE = 0.001429 +/- 0.001373 NMSE = 0.02208 +/- 0.02319 PSNR = 28.85 +/- 4.169 SSIM = 0.8487 +/- 0.07037
10x: MSE = 0.002108 +/- 0.002 NMSE = 0.03273 +/- 0.03417 PSNR = 27.2 +/- 4.197 SSIM = 0.8095 +/- 0.09149
## Limitations
This model was trained on the CC359 using a UNet coil sensitivity maps estimation and might differ from the results reported on the challenge leaderboard.
## References
[1] [ATOMMIC](https://github.com/wdika/atommic)
[2] Beauferris, Y., Teuwen, J., Karkalousos, D., Moriakov, N., Caan, M., Yiasemis, G., Rodrigues, L., Lopes, A., Pedrini, H., Rittner, L., Dannecker, M., Studenyak, V., Gröger, F., Vyas, D., Faghih-Roohi, S., Kumar Jethi, A., Chandra Raju, J., Sivaprakasam, M., Lasby, M., … Souza, R. (2022). Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.919186
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