Yang Dong, Wu Pengxiang, Tan Chaowei, Pohl Kilian M, Axel Leon, Metaxas Dimitris
Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
Center for Health Sciences, SRI International, Menlo Park, CA 94025, USA.
Funct Imaging Model Heart. 2017 Jun;10263:481-492. doi: 10.1007/978-3-319-59448-4_46. Epub 2017 May 23.
The analysis of left ventricle (LV) wall motion is a critical step for understanding cardiac functioning mechanisms and clinical diagnosis of ventricular diseases. We present a novel approach for 3D motion modeling and analysis of LV wall in cardiac magnetic resonance imaging (MRI). First, a fully convolutional network (FCN) is deployed to initialize myocardium contours in 2D MR slices. Then, we propose an image registration algorithm to align MR slices in space and minimize the undesirable motion artifacts from inconsistent respiration. Finally, a 3D deformable model is applied to recover the shape and motion of myocardium wall. Utilizing the proposed approach, we can visually analyze 3D LV wall motion, evaluate cardiac global function, and diagnose ventricular diseases.
左心室(LV)壁运动分析是理解心脏功能机制和心室疾病临床诊断的关键步骤。我们提出了一种用于心脏磁共振成像(MRI)中左心室壁三维运动建模与分析的新方法。首先,部署一个全卷积网络(FCN)来初始化二维MR切片中的心肌轮廓。然后,我们提出一种图像配准算法,以在空间中对齐MR切片,并最大限度地减少因呼吸不一致而产生的不良运动伪影。最后,应用三维可变形模型来恢复心肌壁的形状和运动。利用所提出的方法,我们可以直观地分析左心室壁的三维运动,评估心脏整体功能,并诊断心室疾病。