Abstract
Accurate estimation of motion field in respiration-correlated 4DCT images, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported treatment planning. However, the lung motion estimation often suffers from the sliding motion. In this paper, a novel lung motion method based on the non-rigid registration of point clouds is proposed, and the tangent-plane distance is used to represent the distance term, which describes the difference between two point clouds. Local affine transformation model is used to express the non-rigid deformation of the lung motion. The final objective function is expressed in the Frobenius norm formation, and matrix optimization scheme is carried out to find out the optimal transformation parameters that minimize the objective function. A key advantage of our proposed method is that it alleviates the requirement that the source point cloud and the reference point cloud should be in one-to-one corresponding relationship, and the requirement is difficult to be satisfied in practical application. Furthermore, the proposed method takes the sliding motion of the lung into consideration and improves the registration accuracy by reducing the constraint of the motion along the tangent direction. Non-rigid registration experiments are carried out to validate the performance of the proposed method using popi-model data. The results demonstrate that the proposed method outperforms the traditional method with about 20% accuracy increase.
Highlights
Respiratory motion estimation is a vital problem in medical image processing [1,2]
The final objective function is expressed in the Frobenius norm formation, and a stochastic gradient descent strategy is used to find out the optimal transformation parameters
The point clouds are segmented from 4-D computed tomography (4DCT) images and they represent the statuses at different phases
Summary
The goal of the respiratory motion estimation is to acquire the time-sequenced motion fields along the lung surface It is a precondition for many applications in medical image analysis, such as image-guided interventions, quantitative evaluation of the motion and generating dynamic numerical phantom data for assessment [1,3,4]. Lung motion estimation based on non-rigid point cloud registration decision to publish, or preparation of the manuscript. Liu et al [14] propose a shape-correlated statistical model on dense image deformations for patient-specific respiratory motion estimation, and a point-based particle optimization algorithm was used to obtain the shape models of lungs with group-wise surface correspondences. We present a novel estimation method for lung motion based on the non-rigid registration of point clouds.
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