HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss

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Ruru Xu, Caner Özer, Ilkay Öksüz

Published: 2024, Last Modified: 25 Jan 2026CMRxRecon/MBAS/STACOM@MICCAI 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
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.