Background: View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice. Purpose: Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible, annotation-free system for automatic CMR view planning. Methods: The system mines the spatial relationship, more specifically, locates the intersecting lines, between the target planes and source views, and trains deep networks to regress heatmaps defined by distances from the intersecting lines. The intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. As the spatial relationship is self-contained in properly stored data, the need for additional manual annotation is eliminated. In addition, the interplay of multiple target planes predicted in a source view is utilized in a stacked hourglass architecture to gradually improve the regression. Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. Results: The experiments include 181 CMR exams. Our system yields the mean angular difference and point-to-plane distance of 5.68 degrees and 3.12 mm, respectively. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-oriented scout.

翻译：摘要：背景：心脏磁共振（CMR）成像的视图规划在临床实践中仍是一项具有挑战性的任务。目的：现有的自动化方法要么依赖于临床常规中不常采集的额外容积图像，要么需要耗费人力对心脏结构标志进行手动标注。本研究提出一种临床兼容且无需标注的自动CMR视图规划系统。方法：该系统挖掘目标平面与源视图之间的空间关系（具体而言，定位两者间的交线），并训练深度网络回归由交线距离定义的热力图。这些交线是技术人员在图像采集时利用心脏标志点制定的规划线，并通过空间关系进行回顾性识别。由于空间关系已包含在规范存储的数据中，因此无需额外的手动标注。此外，我们在堆叠沙漏架构中利用源视图内多个目标平面预测的相互影响，逐步改进回归结果。进而提出多视图规划策略，整合目标平面的所有源视图预测热力图信息，实现全局最优规划，模拟熟练人类规划者的类似策略。结果：实验包含181例CMR检查。本系统的平均角度偏差和点到平面距离分别为5.68度和3.12毫米。在规划四个标准CMR平面时，其不仅优于现有方法（包括传统基于图谱和新型基于深度学习的方法），还能从身体导向的定位像中规划出首个以心脏解剖为导向的平面。