Hugging Face's logo

wdika
/
REC_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM

PyTorch
English
atommic
image-reconstruction
RIM
ATOMMIC
Model card Files Community
REC_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM
File size: 4,086 Bytes 
b910e22
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
 
---
language:
- en
license: apache-2.0
library_name: atommic
datasets:
- CC359
thumbnail: null
tags:
- image-reconstruction
- RIM
- ATOMMIC
- pytorch
model-index:
- name: REC_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM
  results: []

---


## Model Overview

Recurrent Inference Machines (RIM) 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_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_RIM_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: 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:
    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 = 0.002022 +/- 0.002006 NMSE = 0.03154 +/- 0.03684 PSNR = 27.45 +/- 4.32 SSIM = 0.8336 +/- 0.07706

10x: MSE = 0.003063 +/- 0.002883 NMSE = 0.04949 +/- 0.06093 PSNR = 25.56 +/- 3.963 SSIM = 0.7881 +/- 0.09099


## 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