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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