  View planning for the acquisition of cardiac magnetic resonance imaging (CMR)
requires acquaintance with the cardiac anatomy and remains a challenging task
in clinical practice. 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 and annotation-free system for automatic CMR view
planning. The system mines the spatial relationship -- more specifically,
locates and exploits the intersecting lines -- between the source and target
views, and trains deep networks to regress heatmaps defined by these
intersecting lines. As the spatial relationship is self-contained in properly
stored data, e.g., in the DICOM format, the need for manual annotation is
eliminated. Then, a multi-view planning strategy is proposed to aggregate
information from the predicted heatmaps for all the source views of a target
view, for a globally optimal prescription. The multi-view aggregation mimics
the similar strategy practiced by skilled human prescribers. Experimental
results on 181 clinical CMR exams show that our system achieves superior
accuracy to existing approaches including conventional atlas-based and newer
deep learning based ones, in prescribing four standard CMR views. The mean
angle difference and point-to-plane distance evaluated against the ground truth
planes are 5.98 degrees and 3.48 mm, respectively.
