用深度学习加速心脏核磁成像，提升图像质量与诊断效率
An All-in-one Approach for Accelerated Cardiac MRI Reconstruction
- 基于Patch-GAN结构分步重建，适配多对比度、多视角的成像需求
- 在CMRxRecon2024挑战中，两项任务的SSIM分别达99.07和97.99
- 适合临床医生与影像算法研究者，提升扫描速度与患者体验
心血管磁共振（CMR）成像因无创性及良好对比度，成为多种心脏病诊断的金标准。然而，信号采集与图像重建耗时较长，导致成像时间延长时易产生伪影，影响诊断。通过深度学习驱动的图像重建，可仅采集高度欠采样的k空间数据（部分填充），并实现高质量临床可读图像重建。本文提出一种基于Patch-GAN结构的分步重建方法，兼容多对比度、多解剖视图及不同采集轨迹的CMR成像需求。在CMRxRecon2024挑战数据集上验证，第一项任务的结构相似性指数（SSIM）为99.07，第二项任务为97.99，优于先前方法。该方法可显著缩短扫描时间，获得高质图像，提升诊断准确性与患者体验。
原文摘要 · Abstract (English)
Cardiovascular magnetic resonance (CMR) imaging is the gold standard for diagnosing several heart diseases due to its non-invasive nature and proper contrast. MR imaging is time-consuming because of signal acquisition and image formation issues. Prolonging the imaging process can result in the appearance of artefacts in the final image, which can affect the diagnosis. It is possible to speed up CMR imaging using image reconstruction based on deep learning. For this purpose, the high-quality clinical interpretable images can be reconstructed by acquiring highly undersampled k-space data, that is only partially filled, and using a deep learning model. In this study, we proposed a stepwise reconstruction approach based on the Patch-GAN structure for highly undersampled k-space data compatible with the multi-contrast nature, various anatomical views and trajectories of CMR imaging. The proposed approach was validated using the CMRxRecon2024 challenge dataset and outperformed previous studies. The structural similarity index measure (SSIM) values for the first and second tasks of the challenge are 99.07 and 97.99, respectively. This approach can accelerate CMR imaging to obtain high-quality images, more accurate diagnosis and a pleasant patient experience.
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