HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss

TL;DR

HyperCMR is a novel framework that improves multi-contrast CMR image reconstruction speed and quality by integrating advanced loss functions, notably Eagle Loss, to recover high-frequency details in undersampled data, outperforming baseline models.

Contribution

The paper introduces HyperCMR, which enhances multi-contrast CMR reconstruction by incorporating Eagle Loss for better high-frequency detail recovery, advancing the state-of-the-art in this domain.

Findings

HyperCMR outperforms baseline models in SSIM and PSNR.

Eagle Loss effectively recovers missing high-frequency information.

HyperCMR demonstrates consistent improvements on the CMRxRecon2024 dataset.

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.