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---
language:
- en
license: apache-2.0
library_name: atommic
datasets:
- SKMTEA
thumbnail: null
tags:
- multitask-image-reconstruction-image-segmentation
- IDSLR
- ATOMMIC
- pytorch
model-index:
- name: MTL_IDSLR_SKMTEA_poisson2d_4x
results: []
---
## Model Overview
Image domain Deep Structured Low-Rank network (IDSLR) 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/MTL/rs/SKMTEA/conf).
### Automatically instantiate the model
```base
pretrained: true
checkpoint: https://huggingface.co/wdika/MTL_IDSLR_SKMTEA_poisson2d_4x/blob/main/MTL_IDSLR_SKMTEA_poisson2d_4x.atommic
mode: test
```
### Usage
You need to download the SKMTEA dataset to effectively use this model. Check the [SKMTEA](https://github.com/wdika/atommic/blob/main/projects/MTL/rs/SKMTEA/README.md) page for more information.
## Model Architecture
```base
model:
model_name: IDSLR
use_reconstruction_module: true
input_channels: 64 # coils * 2
reconstruction_module_output_channels: 64 # coils * 2
segmentation_module_output_channels: 4
channels: 64
num_pools: 2
padding_size: 11
drop_prob: 0.0
normalize: false
padding: true
norm_groups: 2
num_iters: 5
segmentation_loss:
dice: 1.0
dice_loss_include_background: true # always set to true if the background is removed
dice_loss_to_onehot_y: false
dice_loss_sigmoid: false
dice_loss_softmax: false
dice_loss_other_act: none
dice_loss_squared_pred: false
dice_loss_jaccard: false
dice_loss_flatten: false
dice_loss_reduction: mean_batch
dice_loss_smooth_nr: 1e-5
dice_loss_smooth_dr: 1e-5
dice_loss_batch: true
dice_metric_include_background: true # always set to true if the background is removed
dice_metric_to_onehot_y: false
dice_metric_sigmoid: false
dice_metric_softmax: false
dice_metric_other_act: none
dice_metric_squared_pred: false
dice_metric_jaccard: false
dice_metric_flatten: false
dice_metric_reduction: mean_batch
dice_metric_smooth_nr: 1e-5
dice_metric_smooth_dr: 1e-5
dice_metric_batch: true
segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
segmentation_activation: sigmoid
reconstruction_loss:
l1: 1.0
kspace_reconstruction_loss: false
total_reconstruction_loss_weight: 0.5
total_segmentation_loss_weight: 0.5
```
## Training
```base
optim:
name: adam
lr: 1e-4
betas:
- 0.9
- 0.98
weight_decay: 0.0
sched:
name: InverseSquareRootAnnealing
min_lr: 0.0
last_epoch: -1
warmup_ratio: 0.1
trainer:
strategy: ddp
accelerator: gpu
devices: 1
num_nodes: 1
max_epochs: 10
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/MTL/rs/SKMTEA/conf/targets) configuration files.
Evaluation can be performed using the reconstruction [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) and [segmentation](https://github.com/wdika/atommic/blob/main/tools/evaluation/segmentation.py) scripts for the reconstruction and the segmentation tasks, with --evaluation_type per_slice.
Results
-------
Evaluation against SENSE targets
--------------------------------
4x: MSE = 0.001198 +/- 0.002485 NMSE = 0.02524 +/- 0.07112 PSNR = 30.38 +/- 5.67 SSIM = 0.8364 +/- 0.1061 DICE = 0.8695 +/- 0.1342 F1 = 0.225 +/- 0.1936 HD95 = 8.724 +/- 3.298 IOU = 0.2124 +/- 0.1993
## Limitations
This model was trained on the SKM-TEA dataset for 4x accelerated MRI reconstruction and MRI segmentation with MultiTask Learning (MTL) of the axial plane.
## References
[1] [ATOMMIC](https://github.com/wdika/atommic)
[2] Desai AD, Schmidt AM, Rubin EB, et al. SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation. 2022 |