Coronary magnetic resonance angiography (coronary MRA) is increasingly being considered as a clinically viable method to investigate coronary artery disease (CAD). Accurate determination of the trigger delay to place the acquisition window within the quiescent part of the cardiac cycle is critical for coronary MRA in order to reduce cardiac motion. This is currently reliant on operator-led decision making, which can negatively affect consistency of scan acquisition. Recently developed deep learning (DL) derived software may overcome these issues by automation of cardiac rest period detection. Thirty individuals (female, n = 10) were investigated using a 0.9 mm isotropic image-navigator (iNAV)-based motion-corrected coronary MRA sequence. Each individual was scanned three times utilising different strategies for determination of the optimal trigger delay: (1) the DL software, (2) an experienced operator decision, and (3) a previously utilised formula for determining the trigger delay. Methodologies were compared using custom-made analysis software to assess visible coronary vessel length and coronary vessel sharpness for the entire vessel length and the first 4 cm of each vessel. There was no difference in image quality between any of the methodologies for determination of the optimal trigger delay, as assessed by visible coronary vessel length, coronary vessel sharpness for each entire vessel and vessel sharpness for the first 4 cm of the left mainstem, left anterior descending or right coronary arteries. However, vessel length of the left circumflex was slightly greater using the formula method. The time taken to calculate the trigger delay was significantly lower for the DL-method as compared to the operator-led approach (106 ± 38.0 s vs 168 ± 39.2 s, p < 0.01, 95% CI of difference 25.5–98.1 s). Deep learning-derived automated software can effectively and efficiently determine the optimal trigger delay for acquisition of coronary MRA and thus may simplify workflow and improve reproducibility.

中文翻译：

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自动检测心脏休息期，用于基于图像的导航器冠状动脉磁共振血管造影的触发延迟计算

冠状动脉磁共振血管造影（冠状动脉 MRA）越来越被认为是研究冠状动脉疾病（CAD）的临床可行方法。准确确定触发延迟以将采集窗口置于心动周期的静态部分对于冠状动脉 MRA 减少心脏运动至关重要。目前这依赖于操作员主导的决策，这可能会对扫描采集的一致性产生负面影响。最近开发的深度学习 (DL) 衍生软件可以通过自动化心脏休息期检测来克服这些问题。使用基于 0.9 mm 各向同性图像导航器 (iNAV) 的运动校正冠状动脉 MRA 序列对 30 名个体（女性，n = 10）进行了研究。使用不同的策略对每个人进行三次扫描，以确定最佳触发延迟：(1) DL 软件，(2) 经验丰富的操作员决策，以及 (3) 先前使用的用于确定触发延迟的公式。使用定制分析软件对方法进行比较，以评估整个血管长度和每条血管前 4 厘米的可见冠状血管长度和冠状血管清晰度。通过可见冠状血管长度、每条整条血管的冠状血管清晰度以及左主干前 4 厘米、左前部的血管清晰度进行评估，确定最佳触发延迟的任何方法之间的图像质量没有差异。降支或右冠状动脉。然而，使用公式方法，左回旋支的血管长度稍长。与操作员主导的方法相比，DL 方法计算触发延迟所需的时间显着缩短（106 ± 38.0 s 与 168 ± 39.2 s，p < 0.01，差异 25.5–98.1 s 的 95% CI）。深度学习衍生的自动化软件可以有效且高效地确定冠状动脉 MRA 采集的最佳触发延迟，从而可以简化工作流程并提高可重复性。