A novel, end-to-end framework for avoiding collisions between the patient’s body and gantry in proton therapy.
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Abstract
Administration of external radiation therapy via proton therapy systems carries a risk of occasional collisions between the patient’s body and gantry, which is increased by the snout placed near the patient for better dose distribution. Although treatment planning software (TPS) can simulate controlled collisions, the computed tomography (CT) data used for treatment planning are insufficient given that collisions can occur outside the CT imaging region. Thus, imaging the three-dimensional (3D) surface outside the CT range and combining the data with those obtained by CT are essential for avoiding collisions.To construct a prototype for 3D surface imaging and an end-to-end framework for preventing collisions between the patient’s body and the gantry.We obtained 3D surface data using a light sectioning method (LSM). By installing only cameras in front of the CT, we achieved LSM using the CT couch motion and preinstalled patient-positioning lasers. The camera image contained both sagittal and coronal lines, which are unnecessary for LSM and were removed by deep learning. We combined LSM 3D surface data and original CT data to create synthetic Digital Imaging and Communications in Medicine (DICOM) data. Subsequently, we compared the TPS snout auto-optimization using the original CT data with the synthetic DICOM data.The mean positional error for LSM of the arms and head was 0.7 ± 0.8 and 0.8 ± 0.8 mm for axial and sagittal imaging, respectively. The TPS snout auto-optimization indicated that the original CT data would cause collisions; however, the synthetic DICOM data prevented these collisions.The prototype system’s acquisition accuracy for 3D surface data was approximately 1 mm, which was sufficient for the collision simulation. The use of a TPS with collision avoidance can help optimize the snout position using synthetic DICOM data. Our proposed method requires no external software for collision simulation and can be integrated into the clinical workflow to improve treatment planning efficiency.© 2023 American Association of Physicists in Medicine.