刚性变换
图像配准
计算机视觉
运动学
人工智能
计算机科学
离群值
磁共振成像
运动(物理)
体积热力学
刚体
生物医学工程
医学
放射科
物理
图像(数学)
量子力学
经典力学
作者
Alena Uus,Tong Zhang,Laurence H. Jackson,Thomas A. Roberts,Mary Rutherford,Joseph V. Hajnal,Maria Deprez
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:39 (9): 2750-2759
被引量:73
标识
DOI:10.1109/tmi.2020.2974844
摘要
In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28-31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20-34 weeks GA range.
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