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一种用于从小鼠心脏电影磁共振成像估计三维心肌应变的图像配准框架。

An image registration framework to estimate 3D myocardial strains from cine cardiac MRI in mice.

作者信息

Keshavarzian Maziyar, Fugate Elizabeth, Chavan Saurabh, Chu Vy, Arif Mohammed, Lindquist Diana, Sadayappan Sakthivel, Avazmohammadi Reza

机构信息

Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA.

Department of Radiology, University of Cincinnati, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.

出版信息

Funct Imaging Model Heart. 2021 Jun;12738:273-284. doi: 10.1007/978-3-030-78710-3_27. Epub 2021 Jun 18.

Abstract

Accurate and efficient quantification of cardiac motion offers promising biomarkers for non-invasive diagnosis and prognosis of structural heart diseases. Cine cardiac magnetic resonance imaging remains one of the most advanced imaging tools to provide image acquisitions needed to assess and quantify in-vivo heart kinematics. The majority of cardiac motion studies are focused on human data, and there remains a need to develop and implement an image-registration pipeline to quantify full three-dimensional (3D) cardiac motion in mice where ideal image acquisition is challenged by the subject size and heart rate and the possibility of traditional tagged imaging is hampered. In this study, we used diffeomorphic image registration to estimate strains in the left ventricular wall in two wild-type mice and one diabetic mouse. Our pipeline resulted in a continuous and fully 3D strain map over one cardiac cycle. The estimation of 3D regional and transmural variations of strains is a critical step towards identifying mechanistic biomarkers for improved diagnosis and phenotyping of structural left heart diseases including heart failure with reduced or preserved ejection fraction.

摘要

准确且高效地量化心脏运动可为结构性心脏病的非侵入性诊断和预后提供有前景的生物标志物。心脏电影磁共振成像仍然是提供评估和量化体内心脏运动学所需图像采集的最先进成像工具之一。大多数心脏运动研究都集中在人体数据上,仍然需要开发和实施一种图像配准流程,以量化小鼠的全三维(3D)心脏运动,在小鼠中,理想的图像采集受到动物大小、心率的挑战,传统标记成像的可能性也受到阻碍。在本研究中,我们使用微分同胚图像配准来估计两只野生型小鼠和一只糖尿病小鼠左心室壁的应变。我们的流程在一个心动周期内生成了连续且完整的3D应变图。估计应变的3D区域和透壁变化是识别机械生物标志物的关键步骤,有助于改善包括射血分数降低或保留的心力衰竭在内的结构性左心疾病的诊断和表型分析。

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