用新损失函数提升心脏MRI快速成像的图像质量
HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss
- 引入鹰形损失函数，重点恢复欠采样数据中的高频信息
- 在CMRxRecon2024数据集上显著提升SSIM与PSNR指标
- 适合从事医学影像重建与深度学习加速成像的研究者
心脏磁共振成像（CMRI）的快速采集是一项关键任务。CMRxRecon2024挑战赛旨在推动多对比度CMR重建的前沿水平。本文提出HyperCMR框架，用于加速多对比度心脏磁共振图像的重建。该方法在现有PromptMR模型基础上，通过引入先进的损失函数，尤其是创新的Eagle Loss，专门用于恢复欠采样k空间中缺失的高频信息。在CMRxRecon2024挑战赛数据集上的大量实验表明，HyperCMR在多个评估指标上均持续优于基线模型，实现了更高的结构相似性（SSIM）和峰值信噪比（PSNR）。
原文摘要 · Abstract (English)
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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