HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss

Ruru Xu, Caner Özer, Ilkay Oksuz
10/4/2024
eess.IVcs.CV

No official code found. Best implementation: Ruru-Xu/HyperCMR (3 stars, MIT, last commit 2024-10-20).

Abstract

Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR reconstruction. This paper presents HyperCMR, a novel framework designed to accelerate the reconstruction of multi-contrast cardiac magnetic resonance (CMR) images. HyperCMR enhances the existing PromptMR model by incorporating advanced loss functions, notably the innovative Eagle Loss, which is specifically designed to recover missing high-frequency information in undersampled k-space. Extensive experiments conducted on the CMRxRecon2024 challenge dataset demonstrate that HyperCMR consistently outperforms the baseline across multiple evaluation metrics, achieving superior SSIM and PSNR scores.

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Code Implementations(1)

Ruru-Xu/HyperCMRCommunity70%
MIT

[STACOM@MICCAI 2024] HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss (CMRxRecon2024 Challenge)

30Shell, PythonSep 17, 20242 years agoMIT

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Cite this paper

@article{xu2024hypercmr,
  title  = {HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss},
  author = {Ruru Xu and Caner Özer and Ilkay Oksuz},
  year   = {2024},
  eprint = {2410.03624},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.03624v1}
}

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