Development and application of three-dimensional point cloud radiotherapy real-time monitoring system based on depth camera / 中华放射肿瘤学杂志

Objective:To develop the real-time radiotherapy monitoring system of three-dimensional (3D) point cloud by using depth camera and verify its feasibility.Methods:Taking the depth camera coordinate system as the world coordinate system, the conversion relationship between the simulation CT coordinate system and the world coordinate system was obtained from the calibration module. The patient's simulation CT point cloud was transformed into the world coordinate system through the above relationship, and registered with the patient's surface point cloud obtained in real-time manner by the depth camera to calculate the six-dimensional (6D) error, and complete the positioning verification and fractional internal position error monitoring in radiotherapy. Mean and standard deviation of 6D calculation error, Hausdorff distance of point cloud after registration and the running time of each part of the program were calculated to verify the feasibility of the system. Fifteen real patients were selected to calculate the 6D error between the system and cone beam CT (CBCT).Results:In the phantom experiment, the errors of the system in the x, y and z axes were (1.292±0.880)mm, (1.963±1.115)mm, (1.496±1.045)mm, respectively, and the errors in the rotation, pitch and roll directions were 0.201°±0.181°, 0.286°±0.326°, 0.181°±0.192°, respectively. For real patients, the translational error of the system was within 2.6 mm, the rotational error was approximately 1°, and the program run at 1-2 frames/s. The precision and speed met the radiotherapy requirement. Conclusion:The 3D point cloud radiotherapy real-time monitoring system based on depth camera can automatically complete the positioning verification before radiotherapy, real-time monitoring of body position during radiotherapy, and provide error visual feedback, which has potential clinical application value.

Automatic extraction of point cloud on cartilage surface of intraoperative knee using FPFH-PointNet / 解剖学报

Objective The navigation system of robot-assisted knee arthroplasty uses a laser scanner to acquire intraoperative cartilage point clouds and align them with the preoperative model for automatic non-contact space registration. The intraoperative patient knee lesion point cloud contains a large number of irrelevant background point clouds of muscles, tendons, ligaments and surgical instruments. Manual removal of irrelevant point clouds takes up surgery time due to human-computer interaction, so in this study we proposed a novel method for automatic extraction of point clouds from the knee cartilage surface for fast and accurate intraoperative registration. Methods Due to the lack of adequate description of cartilage surface and geometric local information, PointNet cannot extract cartilage point clouds with high precision. In this paper, a fast point feature histogram(FPFH)-PointNet method combined with fast point feature histogram was proposed, which effectively described the appearance of cartilage point cloud and achieved the automatic and efficient segmentation of cartilage point cloud. Results The point clouds of distal femoral cartilage of 10 cadaveric knee specimens and 1 human leg model were scanned from different directions as data sets. The accuracy of cartilage point cloud segmentation by PointNet and FPFH-PointNet were 0.94 ±0.003 and 0.98 ±0, and mean intersection over union(mIOU) were 0.83 ±0.015 and 0.93 ±0.005, respectively. Compared with PointNet, FPFH-PointNet improved accuracy and mIOU by 4% and 10% respectively, while the elapsed time was only about 1.37 s. Conclusion FPFH-PointNet can accurately and automatically extract the knee cartilage point cloud, which meets the performance requirement for intraoperative navigation.

Simulation method of skull remodellingsurgeryfor infant with craniosynostosis / 生物医学工程学杂志

Craniofacial malformation caused by premature fusion of cranial suture of infants has a serious impact on their growth. The purpose of skull remodeling surgery for infants with craniosynostosis is to expand the skull and allow the brain to grow properly. There are no standardized treatments for skull remodeling surgery at the present, and the postoperative effect can be hardly assessed reasonably. Children with sagittal craniosynostosis were selected as the research objects. By analyzing the morphological characteristics of the patients, the point cloud registration of the skull distortion region with the ideal skull model was performed, and a plan of skull cutting and remodeling surgery was generated. A finite element model of the infant skull was used to predict the growth trend after remodeling surgery. Finally, an experimental study of surgery simulation was carried out with a child with a typical sagittal craniosynostosis. The evaluation results showed that the repositioning and stitching of bone plates effectively improved the morphology of the abnormal parts of the skull and had a normal growth trend. The child's preoperative cephalic index was 65.31%, and became 71.50% after 9 months' growth simulation. The simulation of the skull remodeling provides a reference for surgical plan design. The skull remodeling approach significantly improves postoperative effect, and it could be extended to the generation of cutting and remodeling plans and postoperative evaluations for treatment on other types of craniosynostosis.

Accuracy of 3D white light scanning of abutment teeth impressions: evaluation of trueness and precision

PURPOSE: This study aimed to evaluate the accuracy of digitizing dental impressions of abutment teeth using a white light scanner and to compare the findings among teeth types. MATERIALS AND METHODS: To assess precision, impressions of the canine, premolar, and molar prepared to receive all-ceramic crowns were repeatedly scanned to obtain five sets of 3-D data (STL files). Point clouds were compared and error sizes were measured (n=10 per type). Next, to evaluate trueness, impressions of teeth were rotated by 10degrees-20degrees and scanned. The obtained data were compared with the first set of data for precision assessment, and the error sizes were measured (n=5 per type). The Kruskal-Wallis test was performed to evaluate precision and trueness among three teeth types, and post-hoc comparisons were performed using the Mann-Whitney U test with Bonferroni correction (alpha=.05). RESULTS: Precision discrepancies for the canine, premolar, and molar were 3.7 microm, 3.2 microm, and 7.3 microm, respectively, indicating the poorest precision for the molar (P<.001). Trueness discrepancies for teeth types were 6.2 microm, 11.2 microm, and 21.8 microm, respectively, indicating the poorest trueness for the molar (P=.007). CONCLUSION: In respect to accuracy the molar showed the largest discrepancies compared with the canine and premolar. Digitizing of dental impressions of abutment teeth using a white light scanner was assessed to be a highly accurate method and provided discrepancy values in a clinically acceptable range. Further study is needed to improve digitizing performance of white light scanning in axial wall.