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[STACOM@MICCAI 2023] Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction (1st@CMRxRecon2023 Challenge)

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PromptMR

This repository contains the pytorch implementation of PromptMR, an unrolled model for multi-coil MRI reconstruction. See our paper Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction for more details.

paper PWC

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Method

Overview of PromptMR: an all-in-one unrolled model for MRI reconstruction. Adjacent inputs, depicted in image domain for visual clarity, provide neighboring k-space information for reconstruction. To accommodate different input varieties, the input-type adaptive visual prompt is integrated into each cascade of the unrolled architecture to guide the reconstruction process.

Overview of the PromptUnet: the denoiser in each cascade of PromptMR. The PromptBlocks can generate adaptively learned prompts across multiple levels, which integrate with decoder features in the UpBlocks to allow rich hierachical context learning.

Installation and Data Preparation

See INSTALL.md for installation instructions and data preparation required to run this codebase.

Training/Inference Codes & Pretrained models

CMRxRecon dataset

FastMRI multi-coil knee dataset

Citation

If you found this repository useful to you, please consider giving a star ⭐️ and citing our paper:

@article{xin2023fill,
  title={Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction},
  author={Xin, Bingyu and Ye, Meng and Axel, Leon and Metaxas, Dimitris N},
  journal={arXiv preprint arXiv:2309.13839},
  year={2023}
}

Acknowledgements

E2E-VarNet, HUMUS-Net, PromptIR, CMRxRecon

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[STACOM@MICCAI 2023] Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction (1st@CMRxRecon2023 Challenge)

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