---
language:
- en
license: apache-2.0
library_name: atommic
datasets:
- StanfordKnees2019
thumbnail: null
tags:
- image-reconstruction
- RIM
- ATOMMIC
- pytorch
model-index:
- name: REC_RIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM
  results: []

---


## Model Overview

Recurrent Inference Machines (RIM) 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_RIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_RIM_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: CIRIM
  recurrent_layer: GRU
  conv_filters:
    - 64
    - 64
    - 2
  conv_kernels:
    - 5
    - 3
    - 3
  conv_dilations:
    - 1
    - 2
    - 1
  conv_bias:
    - true
    - true
    - false
  recurrent_filters:
    - 64
    - 64
    - 0
  recurrent_kernels:
    - 1
    - 1
    - 0
  recurrent_dilations:
    - 1
    - 1
    - 0
  recurrent_bias:
    - true
    - true
    - false
  depth: 2
  time_steps: 8
  conv_dim: 2
  num_cascades: 1
  no_dc: true
  keep_prediction: true
  accumulate_predictions: true
  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 = 0.001278 +/- 0.006025 NMSE = 0.04409 +/- 0.1243 PSNR = 31.53 +/- 6.786 SSIM = 0.7692 +/- 0.3035


## 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