Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Korea.
Division of Bio-medical Science & Technology, KIST School, Korea University of Science and Technology, 02792, Seoul, Korea.
J Digit Imaging. 2021 Feb;34(1):190-203. doi: 10.1007/s10278-020-00409-y. Epub 2021 Jan 22.
The sliding motion along the boundaries of discontinuous regions has been actively studied in B-spline free-form deformation framework. This study focusses on the sliding motion for a velocity field-based 3D+t registration. The discontinuity of the tangent direction guides the deformation of the object region, and a separate control of two regions provides a better registration accuracy. The sliding motion under the velocity field-based transformation is conducted under the [Formula: see text]-Rényi entropy estimator using a minimum spanning tree (MST) topology. Moreover, a new topology changing method of the MST is proposed. The topology change is performed as follows: inserting random noise, constructing the MST, and removing random noise while preserving a local connection consistency of the MST. This random noise process (RNP) prevents the [Formula: see text]-Rényi entropy-based registration from degrading in sliding motion, because the RNP creates a small disturbance around special locations. Experiments were performed using two publicly available datasets: the DIR-Lab dataset, which consists of 4D pulmonary computed tomography (CT) images, and a benchmarking framework dataset for cardiac 3D ultrasound. For the 4D pulmonary CT images, RNP produced a significantly improved result for the original MST with sliding motion (p<0.05). For the cardiac 3D ultrasound dataset, only a discontinuity-based registration indicated activity of the RNP. In contrast, the single MST without sliding motion did not show any improvement. These experiments proved the effectiveness of the RNP for sliding motion.
在 B 样条自由变形框架中,对不连续区域边界的滑动运动进行了积极研究。本研究专注于基于速度场的 3D+t 配准的滑动运动。切向方向的不连续性指导物体区域的变形,并且两个区域的单独控制提供了更好的配准精度。基于速度场的变换下的滑动运动是在[公式:见文本]-Renyi 熵估计器下使用最小生成树(MST)拓扑结构进行的。此外,还提出了一种新的 MST 拓扑结构改变方法。拓扑结构的改变如下:插入随机噪声,构建 MST,并在保留 MST 的局部连接一致性的同时删除随机噪声。这种随机噪声过程(RNP)防止基于[公式:见文本]-Renyi 熵的配准在滑动运动中降级,因为 RNP 在特殊位置周围产生小干扰。使用两个公开可用的数据集进行了实验:DIR-Lab 数据集,其中包含 4D 肺部计算机断层扫描(CT)图像,以及心脏 3D 超声的基准框架数据集。对于 4D 肺部 CT 图像,RNP 产生了原始 MST 带有滑动运动的显著改进结果(p<0.05)。对于心脏 3D 超声数据集,只有基于不连续性的配准表明 RNP 有活动。相比之下,没有滑动运动的单个 MST 没有显示出任何改进。这些实验证明了 RNP 对滑动运动的有效性。