HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 4 Oct 2024.
Builds on
6 of 11 references.
References · 6
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
- 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 232
- 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
- 2023Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstructioncited by 17
- 2024CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRIcited by 6
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.