Automatic view plane prescription for cardiac magnetic resonance imaging
via supervision by spatial relationship between views
- URL: http://arxiv.org/abs/2309.12805v1
- Date: Fri, 22 Sep 2023 11:36:42 GMT
- Title: Automatic view plane prescription for cardiac magnetic resonance imaging
via supervision by spatial relationship between views
- Authors: Dong Wei, Yawen Huang, Donghuan Lu, Yuexiang Li, and Yefeng Zheng
- Abstract summary: This work presents a clinic-compatible, annotation-free system for automatic cardiac magnetic resonance (CMR) view planning.
The system mines the spatial relationship, more specifically, locates the intersecting lines, between the target planes and source views.
The interplay of multiple target planes predicted in a source view is utilized in a stacked hourglass architecture to gradually improve the regression.
- Score: 30.59897595586657
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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