Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4079-4085. doi: 10.1109/EMBC46164.2021.9630270.
The dark-rim artifact (DRA) remains an important challenge in the routine clinical use of first-pass perfusion (FPP) cardiac magnetic resonance imaging (cMRI). The DRA mimics the appearance of perfusion defects in the subendocardial wall and reduces the accuracy of diagnosis in patients with suspected ischemic heart disease. The main causes for DRA are known to be Gibbs ringing and bulk motion of the heart. The goal of this work is to propose a deep-learning-enabled automatic approach for the detection of motion-induced DRAs in FPP cMRI datasets. To this end, we propose a new algorithm that can detect the DRA in individual time frames by analyzing multiple reconstructions of the same time frame (k-space data) with varying temporal windows. In addition to DRA detection, our approach is also capable of suppressing the extent and severity of DRAs as a byproduct of the same reconstruction-analysis process. In this proof-of-concept study, our proposed method showed a good performance for automatic detection of subendocardial DRAs in stress perfusion cMRI studies of patients with suspected ischemic heart disease. To the best of our knowledge, this is the first approach that performs deep-learning-enabled detection and suppression of DRAs in cMRI.Clinical Relevance- Our approach enables clinicians to provide a more accurate diagnosis of ischemic heart disease by detecting and suppressing subendocardial dark-rim artifacts in first-pass perfusion cMRI datasets.
暗边伪影（DRA）仍然是首过灌注（FPP）心脏磁共振成像（cMRI）临床常规应用中的一个重要挑战。DRA 模仿了心内膜下壁灌注缺损的外观，降低了疑似缺血性心脏病患者的诊断准确性。已知 DRA 的主要原因是 Gibbs 环和心脏的整体运动。这项工作的目标是提出一种基于深度学习的自动方法，用于检测 FPP cMRI 数据集中由运动引起的 DRA。为此，我们提出了一种新的算法，该算法可以通过分析同一时间帧的多个重建（k 空间数据）来检测单个时间帧中的 DRA，这些重建具有不同的时间窗口。除了 DRA 检测之外，我们的方法还能够抑制 DRA 的程度和严重程度，这是同一重建分析过程的副产品。在这项概念验证研究中，我们提出的方法在疑似缺血性心脏病患者应激灌注 cMRI 研究中自动检测心内膜下 DRA 方面表现出良好的性能。据我们所知，这是第一种在 cMRI 中进行深度学习支持的 DRA 检测和抑制的方法。临床意义-我们的方法能够通过在首过灌注 cMRI 数据集中检测和抑制心内膜下暗边伪影，为临床医生提供更准确的缺血性心脏病诊断。
