3D Measurement of Human Chest and Abdomen Surface Based on 3D Fourier Transform and Time Phase Unwrapping Monitoring respiratory movements is an effective way to improve radiotherapy treatments of thoracic and abdominal tumors, but the current approach is limited to measuring specific points in the chest and abdomen. In this paper, a dynamic threedimensional (3D) measurement approach of the human chest and abdomen surface is proposed, which can infer tumor movement more accurately, so the radiotherapy damage to the human body can be reduced. Firstly, color stripe patterns in the RGB color model are projected, then after color correction, the collected stripe image sequences are separated into the three RGB primary color stripe image sequences. Secondly, a fringe projection approach is used to extract the folded phase combined 3D Fourier transform with 3D Gaussian filtering. By the relationship between adjacent fringe images in the time sequence, Gaussian filter parameters with individual characteristics are designed and optimized to improve the accuracy of wrapped phase extraction. In addition, based on the difference between the fractional parts of the folded phase error, one remainder equation can be determined, which is used for timephase unwrapping. The simulation model and human experiments show that the proposed approach can obtain the 3D image sequences of the chest and abdomen surface in respiratory motion effectively and accurately with strong antiinterference ability.

Prediction of Liver Respiratory Motion Based on Machine Learning Hepatic respiratory movement has always been an important factor that affects the accuracy of liver interventional therapy. To improve the prediction accuracy of imageguided therapy, we proposed a liver breath prediction model that combines machine learning, surface point set sparse registration, and internal and external breath amplitude correlation. We used surface sparse point set registration to calculate the displacement vector field of the liver surface and the displacement vector field of a specified region of the abdominal surface. Using correlation analysis of the internal and external respiratory amplitudes, we selected the liver displacement vector field that is closest to the input respiratory signal as the optimal training data. A patientspecific model that combines local vector field optimization with abdominal surface similarity optimization was constructed by combining the liver surface and the abdominal surface after segmentation, and accurate motion prediction was realized based on principal component analysis (PCA). In an experiment on 7 patients, we adopted two experimental verification methods: (1) only one data collection stage and one crossvalidation stage were used, and (2) the experimental data that were collected in the first stage were used as the training data set, and the experimental data that were collected in the second stage were used as the test data set. The prediction errors of the two methods were 0.35 xc2xb1 0.08 mm and 0.96 xc2xb1 0.40 mm, respectively. In this experiment, we combined surface sparse point set registration with an internal and external breath amplitude correlation method, which substantially improved the runtime and accuracy of the experiment compared with the traditional PCA method.