atommic image-reconstruction KIKINet ATOMMIC pytorch en dataset:CC359 license:apache-2.0 region:us
---
language:
- en
license: apache-2.0
library_name: atommic
datasets:
- CC359
thumbnail: null
tags:
- image-reconstruction
- KIKINet
- ATOMMIC
- pytorch
model-index:
- name: REC_KIKINet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM
  results: []

---


## Model Overview

KIKINet 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_KIKINet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_KIKINet_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: KIKINet
  num_iter: 2
  kspace_model_architecture: UNET
  kspace_in_channels: 2
  kspace_out_channels: 2
  kspace_unet_num_filters: 16
  kspace_unet_num_pool_layers: 2
  kspace_unet_dropout_probability: 0.0
  kspace_unet_padding_size: 11
  kspace_unet_normalize: true
  imspace_model_architecture: UNET
  imspace_in_channels: 2
  imspace_unet_num_filters: 16
  imspace_unet_num_pool_layers: 2
  imspace_unet_dropout_probability: 0.0
  imspace_unet_padding_size: 11
  imspace_unet_normalize: true
  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 = 0.003224 +/- 0.003526 NMSE = 0.04931 +/- 0.05484 PSNR = 25.43 +/- 4.157 SSIM = 0.7882 +/- 0.08686

10x: MSE = 0.004036 +/- 0.0038 NMSE = 0.06195 +/- 0.06049 PSNR = 24.37 +/- 3.88 SSIM = 0.7419 +/- 0.1053


## 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