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| Deep Cardiac MRI Reconstruction with ADMM (CMRxRecon Challenge 2023) | ||
| ===================================================================== | ||
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| This project contains necessary configuration files to reproduce experiments of 2nd top-ranking approach | ||
| to both tasks of CMRxRecon Challenge 2023 as presented in `Deep Cardiac MRI Reconstruction with ADMM | ||
| <https://arxiv.org/abs/2310.06628>`_. | ||
| This will also help you set up the training and inference data directories. | ||
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| Setting up data directory | ||
| ------------------------- | ||
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| This project aims to help you set up directories for training and validation | ||
| data using the specified directory structures necessary to run with DIRECT. | ||
| You will need to run this process twice: once for "Cine" data and once for "Mapping" data. | ||
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| Prerequisites | ||
| ~~~~~~~~~~~~~ | ||
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| Before you begin, make sure you have downloaded the CMRxRecon Challenge | ||
| data. Check `https://cmrxrecon.github.io/Challenge.html`_ for more information. | ||
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| Assumed Base Path Structure | ||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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| The script assumes that CMRxRecon data is organized according to the following directory structure: | ||
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| .. code-block:: plaintext | ||
| base_path | ||
| ├── MultiCoil | ||
| │ ├── Cine_or_Mapping | ||
| │ │ ├── TrainingSet | ||
| │ │ │ ├── FullSample | ||
| │ │ │ ├── AccFactor04 | ||
| │ │ │ ├── AccFactor08 | ||
| │ │ │ └── AccFactor10 | ||
| │ │ ├── ValidationSet | ||
| │ │ │ ├── AccFactor04 | ||
| │ │ │ ├── AccFactor08 | ||
| │ │ │ └── AccFactor10 | ||
| │ │ ├── TestSet | ||
| │ │ │ ├── AccFactor04 | ||
| │ │ │ ├── AccFactor08 | ||
| │ │ │ └── AccFactor10 | ||
| Symlinks Path Structure | ||
| ~~~~~~~~~~~~~~~~~~~~~~~ | ||
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| The script will create symbolic links (symlinks) in a target directory with the following structure: | ||
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| .. code-block:: plaintext | ||
| target_path | ||
| target_path | ||
| ├── MultiCoil | ||
| │ ├── training | ||
| │ │ ├── P001_T1map.h5 | ||
| │ │ ├── with_masks_P001_T1map.h5 | ||
| │ │ ├── P001_cine_sax.h5 | ||
| │ │ ├── with_masks_P001_cine_sax.h5 | ||
| │ ├── Cine_or_Mapping | ||
| │ │ ├── validation | ||
| │ │ │ ├── AccFactor04 | ||
| │ │ │ | ├── P001_<..>.h5 | ||
| │ │ │ ├── AccFactor08 | ||
| │ │ │ | ├── P001_<..>.h5 | ||
| │ │ │ └── AccFactor10 | ||
| │ │ │ | ├── P001_<..>.h5 | ||
| │ │ ├── test | ||
| │ │ │ ├── AccFactor04 | ||
| │ │ │ | ├── P001_<..>.h5 | ||
| │ │ │ ├── AccFactor08 | ||
| │ │ │ | ├── P001_<..>.h5 | ||
| │ │ │ └── AccFactor10 | ||
| │ │ │ | ├── P001_<..>.h5 | ||
| Create Symbolic Directories | ||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
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| The following creates files of fully sampled data with the respective masks, and creates | ||
| symbolic paths of data in single directories to be used with DIRECT. | ||
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| .. code-block:: bash | ||
| python3 create_data_dir.py --base_path path_to_base_data --target_path path_to_target_directory --data_type Cine | ||
| python3 create_data_dir.py --base_path path_to_base_data --target_path path_to_target_directory --data_type Mapping | ||
| Experiments | ||
| ----------- | ||
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| Configuration Files | ||
| ~~~~~~~~~~~~~~~~~~~ | ||
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| We provide configuration files for DIRECT for experiments presented in `Deep Cardiac MRI Reconstruction with ADMM | ||
| <https://arxiv.org/abs/2310.06628>`_ in the `configs folder <./configs>`_. | ||
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| Training | ||
| ~~~~~~~~ | ||
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| In `direct/` run the following command to begin training on the training data. | ||
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| .. code-block:: bash | ||
| direct train <output_folder> \ | ||
| --training-root <target_path>/MultiCoil/training/ \ | ||
| --validation-root <target_path>/MultiCoil/training/ \ | ||
| --cfg projects/CMRxRecon/configs/base_<name_of_experiment>.yaml \ | ||
| --num-gpus <number_of_gpus> \ | ||
| --num-workers <number_of_workers> | ||
| Note that for validation a subset of the training data is used since full validation data have not been released. | ||
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| Inference | ||
| ~~~~~~~~~ | ||
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| Note that inference is performed for a single dataset, therefore a single acceleration factor. | ||
| For example, the following entry for `inference` will perform predictions for acceleration factor of 4x | ||
| on validation data. Change `kspace_key: kspace_sub04` to `kspace_key: kspace_sub08` for 8x and | ||
| `kspace_key: kspace_sub10` for 10x. | ||
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| .. code-block:: yaml | ||
| inference: | ||
| batch_size: 8 | ||
| dataset: | ||
| name: CMRxRecon | ||
| kspace_key: kspace_sub04 | ||
| compute_mask: true | ||
| transforms: | ||
| cropping: | ||
| crop: null | ||
| sensitivity_map_estimation: | ||
| estimate_sensitivity_maps: true | ||
| normalization: | ||
| scaling_key: masked_kspace | ||
| scale_percentile: 0.99 | ||
| masking: null | ||
| text_description: inference-4x | ||
| crop: null | ||
| In `direct/` run the following command to perform inference, for instance on 4x: | ||
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| .. code-block:: bash | ||
| direct predict <output_directory> | ||
| --checkpoint <path_or_url_to_checkpoint> \ | ||
| --cfg projects/CMRxRecon/configs/base_<name_of_experiment>.yaml \ | ||
| --data-root <target_path>/MultiCoil/<Cine_or_Mapping>/validation/AccFactor<04_or_08_or_10> \ | ||
| --num-gpus <number_of_gpus> \ | ||
| --num-workers <number_of_workers> \ | ||
| [--other-flags] |
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