Three-dimensional (3D) morphology of microparts has an important influence on performance of microassembly system that mainly assembles microparts in millimetre and micron scale. Because 3D morphology of microparts cannot be accurately obtained by conventional microscopic vision system, a depth estimation method of surface of micropart in microassembly space based on microscopic vision tomographic scanning (MVTS) images is proposed in this paper. The proposed method uses the positions of pixels with the largest focus values in MVTS image to construct the isodepth contours of surface of micropart and obtains the depth values of micropart's surface at the positions of MVTS by assigning depth values to corresponding isodepth contours. The MVTS images are obtained by MVTS and pixels with the largest focus values in MVTS image are obtained by focus measurement of MVTS images of micropart in microassembly space. On these bases, 3D spatial interpolation method is applied to map depth value of space between adjacent isodepth contours and to obtain depth values of all surface of micropart. Simulation experiments are carried out to verify the proposed method by generating simulated MVTS image array from two simulation objects, and the influence parameters of the proposed method are analysed. In established experimental setup of microassembly that can realise MVTS, experimental verification for the proposed depth estimation method are carried out by using cone cavity and end jaws of microgripper. 3D morphologies of depth maps of cone cavity and end jaws of microgripper are registered with their respective CAD models using iterative nearest point registration algorithm to quantify accuracy of depth estimation. The research results show that 3D morphology of micropart can be obtained by the proposed method and has better accuracy than those by conventional shape from focus method. This method provides a new way to obtain the morphology of microparts and lays a foundation for improving
Purpose: Scatter correction in cone‐beam computed tomography (CBCT) has obvious effect on the removal of image noise, the cup artifact and the increase of image contrast. Several methods using a beam blocker for the estimation and subtraction of scatter have been proposed. However, the inconvenience of mechanics and propensity to residual artifacts limited the further evolution of basic and clinical research. Here, we propose a rotating collimator‐based approach, in conjunction with reconstruction based on a discrete Radon transform and Tchebichef moments algorithm, to correct scatter‐induced artifacts. Methods: A rotating‐collimator, comprising round tungsten alloy strips, was mounted on a linear actuator. The rotating‐collimator is divided into 6 portions equally. The round strips space is evenly spaced on each portion but staggered between different portions. A step motor connected to the rotating collimator drove the blocker to around x‐ray source during the CBCT acquisition. The CBCT reconstruction based on a discrete Radon transform and Tchebichef moments algorithm is performed. Experimental studies using water phantom and Catphan504 were carried out to evaluate the performance of the proposed scheme. Results: The proposed algorithm was tested on both the Monte Carlo simulation and actual experiments with the Catphan504 phantom. From the simulation result, the mean square error of the reconstruction error decreases from 16% to 1.18%, the cupping (τcup) from 14.005% to 0.66%, and the peak signal‐to‐noise ratio increase from 16.9594 to 31.45. From the actual experiments, the induced visual artifacts are significantly reduced. Conclusion: We conducted an experiment on CBCT imaging system with a rotating collimator to develop and optimize x‐ray scatter control and reduction technique. The proposed method is attractive in applications where a high CBCT image quality is critical, for example, dose calculation in adaptive radiation therapy. We want to thank Dr. Lei Xing and Dr. Yong Yang in the Stanford University School of Medicine for this work. This work was jointly supported by NSFC (61471226), Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201516), and China Postdoctoral Science Foundation (2015T80739, 2014M551949).
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