分割
医学
磁共振成像
血管病学
流离失所(心理学)
再现性
拉伤
编码(内存)
心脏磁共振
相(物质)
人工智能
生物医学工程
计算机视觉
放射科
心脏病学
计算机科学
内科学
物理
统计
心理治疗师
量子力学
数学
心理学
作者
Sona Ghadimi,Daniel A. Auger,Xue Feng,Changyu Sun,Craig H. Meyer,Kenneth C. Bilchick,Jie Cao,Andrew D. Scott,John N. Oshinski,Daniel B. Ennis,Frederick H. Epstein
标识
DOI:10.1186/s12968-021-00712-9
摘要
Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis.
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