A clinically feasible method for the detection of potential collision in proton therapy
Purpose: Potential collision between the patient/couch and the gantry could delay the start of the treatment and reduce clinical efficiency. The ability to accurately detect possible collisions during the treatment planning phase is desired. Such collision detection should account for the specific proton gantry design, the treatment beam configuration, couch orientation, and the patient specific geometry. In this paper the authors developed an approach to detect possible patient-machine collisions using patient treatment plan data. Methods: The geometry of the machine and the patient was reconstructed relative to the isocenter of the proton treatment room. The surface contour of the gantry was first captured from the proton computer aided design and reconstructed to account for specific gantry rotation, snout position, collimator rotation, and range compensator dimensions based on the patient treatment plan data. The patient body and couch contours were captured from the patient's CT DICOM structure file. They were reconstructed relative to the isocenter taking into account treatment couch rotation. For potential collision that occurs at body portions where no CT images exist, scout images are used to construct the body contour. A software program was developed using a ray casting algorithm that was applied to detect collisions by determining if any of the patient and couch contour points fall into the spatial polygons formed by the proton gantry surfaces. Results: Twenty-four patient plans with or without potential collisions were retrospectively identified and analyzed using the collision detection software. In addition, five collision cases were artificially generated using an anthropomorphic phantom. The program successfully detected the collisions in all cases. The calculation time for each case was within 20 s. The software program was implemented in the authors' clinic to detect patient-gantry or gantry-couch collisions in the treatment planning phase. Conclusions: The authors developed a fast and clinically feasible patient-specific collision detection program for proton therapy based on a ray casting algorithm. If incorporated during the treatment planning phase it may lead to improved clinical efficiency. This methodology could also be applied to patient collision detection in photon therapy.