Estimating fetal brain motion with total-variation-based magnetic resonance image registration

• Published in: 2016 12th World Congress on Intelligent Control and Automation (WCICA) pp. 809 - 813
• Main Authors: Luming Chen, Hefang Zhang, Shibin Wu, Shaode Yu, Yaoqin Xie
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Image registration; Magnetic resonance imaging; Motion estimation; Optimization; Visual perception; Visualization
• DOI: 10.1109/WCICA.2016.7578230

Fetal magnetic resonance imaging has been widely used for brain malformation assessment, brain growth estimation and related disease diagnosis. However, how to estimate fetal brain motion is pending which hampers image interpretation. This paper presents an image registration method to address this problem. It takes total-variation regularization (TV) to model fetal brain motions and adopts a penalty toward piece-wise convergence. Based on four clinical cases, the proposed method was verified from perceived visual perception and landmark errors, and also compared to nonlinear Levenberg-Marquart least square optimization (L2). Visual perception indicates that TV-based registration outperforms L2-based method with less tissue difference, and landmark errors show that the landmark displacement decreases from 4.86±1.76mm to 0.82±0.51mm with L2-based method and to 0.42±0.57mm with TV-based method. One-way ANOVA verifies that both L2- and TV-based methods significantly reduce landmark errors (p<;0.0001), while TV-based method outperforms L2-based method (p<;0.0065). This paper provides a feasible solution to estimate fetal brain motions and benefits brain development study and disease diagnosis.