Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac function, but individual cardiac cycles complicate automatic temporal comparison or sub-phase analysis. Accurate cardiac keyframe detection can eliminate this problem. However, automatic methods solely derive end-systole (ES) and end-diastole (ED) frames from left ventricular volume curves, which do not provide a deeper insight into myocardial motion. We propose a self-supervised deep learning method detecting five keyframes in short-axis (SAX) and four-chamber long-axis (4CH) cine CMR. Initially, dense deformable registration fields are derived from the images and used to compute a 1D motion descriptor, which provides valuable insights into global cardiac contraction and relaxation patterns. From these characteristic curves, keyframes are determined using a simple set of rules. The method was independently evaluated for both views using three public, multicentre, multidisease datasets. M&Ms-2 (n=360) dataset was used for training and evaluation, and M&Ms (n=345) and ACDC (n=100) datasets for repeatability control. Furthermore, generalisability to patients with rare congenital heart defects was tested using the German Competence Network (GCN) dataset. Our self-supervised approach achieved improved detection accuracy by 30% - 51% for SAX and 11% - 47% for 4CH in ED and ES, as measured by cyclic frame difference (cFD), compared with the volume-based approach. We can detect ED and ES, as well as three additional keyframes throughout the cardiac cycle with a mean cFD below 1.31 frames for SAX and 1.73 for LAX. Our approach enables temporally aligned inter- and intra-patient analysis of cardiac dynamics, irrespective of cycle or phase lengths. GitHub repository: https://github.com/Cardio-AI/cmr-multi-view-phase-detection.git

翻译：心血管磁共振成像是评估心脏功能的金标准，但个体心动周期的差异使得自动时间对比或亚相位分析变得复杂。准确的心脏关键帧检测可消除此问题。然而，现有自动方法仅从左心室容积曲线推导收缩末期与舒张末期帧，无法深入揭示心肌运动模式。本文提出一种自监督深度学习方法，用于在短轴切面与四腔心长轴切面电影磁共振图像中分别检测五个关键帧。该方法首先从图像序列中提取密集可变形配准场，进而计算一维运动描述符，该描述符能有效反映心脏整体收缩与舒张模式。基于这些特征曲线，通过一套简洁规则确定关键帧。我们使用三个公开的多中心多疾病数据集对两种切面视图进行独立评估：采用M&Ms-2数据集进行训练与评估，并利用M&Ms与ACDC数据集进行可重复性验证。此外，通过德国心脏网络数据集测试了该方法对罕见先天性心脏病患者的泛化能力。相较于基于容积的方法，本自监督方法在收缩末期与舒张末期的检测精度（以循环帧差度量）在短轴切面提升30%-51%，在四腔心长轴切面提升11%-47%。该方法不仅能检测收缩末期与舒张末期，还能在整个心动周期中检测三个附加关键帧，其平均循环帧差在短轴切面低于1.31帧，在长轴切面低于1.73帧。本方法实现了跨患者与患者内心脏动力学的时间对齐分析，且不受心动周期长度或相位时长影响。项目代码库：https://github.com/Cardio-AI/cmr-multi-view-phase-detection.git