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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: atommic |
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datasets: |
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- StanfordKnees2019 |
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thumbnail: null |
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tags: |
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- image-reconstruction |
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- KIKINet |
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- ATOMMIC |
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- pytorch |
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model-index: |
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- name: REC_KIKINet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM |
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results: [] |
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--- |
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## Model Overview |
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KIKINet for 12x accelerated MRI Reconstruction on the StanfordKnees2019 dataset. |
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## ATOMMIC: Training |
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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. |
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``` |
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pip install atommic['all'] |
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``` |
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## How to Use this Model |
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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. |
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Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/StanfordKnees2019/conf). |
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### Automatically instantiate the model |
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```base |
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pretrained: true |
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checkpoint: https://huggingface.co/wdika/REC_KIKINet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_KIKINet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic |
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mode: test |
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``` |
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### Usage |
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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. |
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## Model Architecture |
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```base |
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model: |
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model_name: KIKINet |
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num_iter: 2 |
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kspace_model_architecture: UNET |
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kspace_in_channels: 2 |
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kspace_out_channels: 2 |
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kspace_unet_num_filters: 16 |
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kspace_unet_num_pool_layers: 2 |
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kspace_unet_dropout_probability: 0.0 |
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kspace_unet_padding_size: 11 |
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kspace_unet_normalize: true |
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imspace_model_architecture: UNET |
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imspace_in_channels: 2 |
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imspace_unet_num_filters: 16 |
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imspace_unet_num_pool_layers: 2 |
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imspace_unet_dropout_probability: 0.0 |
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imspace_unet_padding_size: 11 |
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imspace_unet_normalize: true |
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dimensionality: 2 |
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reconstruction_loss: |
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wasserstein: 1.0 |
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``` |
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## Training |
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```base |
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optim: |
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name: adamw |
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lr: 1e-4 |
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betas: |
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- 0.9 |
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- 0.999 |
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weight_decay: 0.0 |
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sched: |
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name: InverseSquareRootAnnealing |
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min_lr: 0.0 |
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last_epoch: -1 |
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warmup_ratio: 0.1 |
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trainer: |
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strategy: ddp_find_unused_parameters_false |
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accelerator: gpu |
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devices: 1 |
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num_nodes: 1 |
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max_epochs: 20 |
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precision: 16-mixed |
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enable_checkpointing: false |
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logger: false |
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log_every_n_steps: 50 |
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check_val_every_n_epoch: -1 |
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max_steps: -1 |
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``` |
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## Performance |
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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. |
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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. |
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Results |
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------- |
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Evaluation against SENSE targets |
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12x: MSE = 0.0025 +/- 0.00671 NMSE = 0.1052 +/- 0.1784 PSNR = 27.33 +/- 5.552 SSIM = 0.6587 +/- 0.2413 |
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## Limitations |
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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. |
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## References |
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[1] [ATOMMIC](https://github.com/wdika/atommic) |
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[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 |
