Development and clinical implementation of eclipse scripting-based automated patient-specific collision avoidance software.

Increased use of Linac-based stereotactic radiosurgery (SRS), which requires highly noncoplanar gantry trajectories, necessitates the development of efficient and accurate methods of collision detection during the treatment planning process. This work outlines the development and clinical implementation of a patient-specific computed tomography (CT) contour-based solution that utilizes Eclipse Scripting to ensure maximum integration with clinical workflow. The collision detection application uses triangle mesh structures of the gantry and couch, in addition to the body contour of the patient taken during CT simulation, to virtually simulate patient treatments. Collision detection is performed using Binary Tree Hierarchy detection methods. Algorithm accuracy was first validated for simple cuboidal geometry using a calibration phantom and then extended to an anthropomorphic phantom simulation by comparing the measured minimum distance between structures to the predicted minimum distance for all allowable orientations. The collision space was tested at couch angles every 15° from 90 to 270 with the gantry incremented by 5° through the maximum trajectory. Receiver operating characteristic curve analysis was used to assess algorithm sensitivity and accuracy for predicting collision events. Following extensive validation, the application was implemented clinically for all SRS patients. The application was able to predict minimum distances between structures to within 3 cm. A safety margin of 1.5 cm was sufficient to achieve 100% sensitivity for all test cases. Accuracy obtained was 94.2% with the 5 cm clinical safety margin with 100% true positive collision detection. A total of 88 noncoplanar SRS patients have been currently tested using the application with one collision detected and no undetected collisions occurring. The average time for collision testing per patient was 2 min 58 s. A collision detection application utilizing patient CT contours was developed and successfully clinically implemented. This application allows collisions to be detected early during the planning process, avoiding patient delays and unnecessary resource utilization if detected during delivery.

Mann TD ，Ploquin NP ，Gill WR ，Thind KS ... -  《Journal of Applied Clinical Medical Physics》

The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery.

Yu VY ，Tran A ，Nguyen D ，Cao M ，Ruan D ，Low DA ，Sheng K ... -  《-》

Design of a 3D patient-specific collision avoidance virtual framework for half-gantry proton therapy system.

This study presents a comprehensive collision avoidance framework based on three-dimension (3D) computer-aided design (CAD) modeling, a graphical user interface (GUI) as peripheral to the radiation treatment planning (RTP) environment, and patient-specific plan parameters for intensity-modulated proton therapy (IMPT). A stand-alone software application was developed leveraging the Varian scripting application programming interface (API) for RTP database object accessibility. The Collision Avoider software models the Hitachi ProBeat-V half gantry design and the Kuka robotic couch with triangle mesh structures. Patient-specific plan parameters are displayed in the collision avoidance software for potential proximity evaluation. The external surfaces of the patients and the immobilization devices are contoured based on computed tomography (CT) images. A "table junction-to-CT-origin" (JCT) measurement is made for every patient at the time of CT simulation to accurately provide reference location of the patient contours to the treatment couch. Collision evaluations were performed virtually with the program during treatment planning to prevent four major types of collisional events: collisions between the gantry head and the treatment couch, gantry head and the patient's body, gantry head and the robotic arm, and collisions between the gantry head and the immobilization devices. The Collision Avoider software was able to accurately model the proton treatment delivery system and the robotic couch position. Commonly employed clinical beam configuration and JCT values were investigated. Brain and head and neck patients require more complex gantry and patient positioning system configurations. Physical measurements were performed to validate 3D CAD model geometry. Twelve clinical proton treatment plans were used to validate the accuracy of the software. The software can predict all four types of collisional events in our clinic since its full implementation in 2020. A highly efficient patient-specific collision prevention program for scanning proton therapy has been successfully implemented. The graphical program has provided accurate collision detection since its inception at our institution.

Dougherty JM ，Whitaker TJ ，Mundy DW ，Tryggestad EJ ，Beltran CJ ... -  《Journal of Applied Clinical Medical Physics》

Patient-specific collision zones for 4π trajectory optimized radiation therapy.

The 4π methodology determines optimized noncoplanar subarcs for stereotactic radiation therapy that minimize dose to organs-at-risk. Every combination of treatment angle is examined, but some angles are not appropriate as a collision would occur between the gantry and the couch or the gantry and the patient. Those combinations of couch and gantry angles are referred to as collision zones. A major barrier to applying 4π to stereotactic body radiation therapy (SBRT) is the unknown shape of the collision zones, which are significant as patients take up a large volume within the 4π sphere. This study presents a system that determines patient-specific collision zones, without additional clinical steps, to enable safe and deliverable noncoplanar treatment trajectories for SBRT patients. To augment patient's computed tomography (CT) scan, full body scans of patients in treatment position were acquired using an optical scanner. A library of a priori scans (N = 25) was created. Based on the patients' treatment position and their body dimensions, a library scan is selected and registered to the CT scan of the patient. Next, a model of the couch and immobilization equipment is added to the patient model. This results in a patient model that is then aligned with a model of the treatment LINAC in a "virtual treatment room," where both components can be rotated to test for collisions. To test the collision detection algorithm, an end-to-end test was performed using a cranial phantom. The registration algorithm was tested by comparing the registered patient collision zones to those generated by using the patient's matching scan. The collision detection algorithm was found to have a 97.80% accuracy, a 99.99% sensitivity, and a 99.99% negative predictive value (NPV). Analysis of the registration algorithm determined that a 6 cm buffer was required to achieve a 99.65% mean sensitivity, where a sensitivity of unity is considered to be a requirement for safe treatment delivery. With a 6 cm buffer, the mean accuracy was 86.70% and the mean NPV was 99.33%. Our method of determining patient-specific collision zones can be accomplished with minimal user intervention based on an a priori library of body surface scans, thus enabling the safe application of 4π SBRT.

Northway C ，Lincoln JD ，Little B ，Syme A ，Thomas CG ... -  《-》

Collision prediction for intracranial stereotactic radiosurgery planning: An easy-to-implement analytical solution.

Gantry collision is a concern in linac-based stereotactic radiosurgery (SRS). Without collision screening, the planner may compromise optimal planning, unnecessary re-planning delays can occur, and incomplete treatments may be delivered. To address these concerns, we developed a software for collision prediction based on simple machine measurements. Three types of collision were identified; gantry-couch mount, gantry-couch and gantry-patient. Trigonometric formulas to calculate the distance from each potential point of collision to the gantry rotation axis were generated. For each point, collision occurs when that distance is greater than the gantry head to gantry rotational axis distance. The colliding arc for each point is calculated. A computer code incorporating these formulas was generated. The inputs required are the couch coordinates relative to the isocenter, the patient dimensions, and the presence or absence of a circular SRS collimator. The software outputs the collision-free gantry angles, and for each point, the shortest distance to the gantry or the colliding sector when collision is identified. The software was tested for accuracy on a TrueBEAM® machine equipped with BrainLab® accessories for 80 virtual isocenter-couch angle configurations with and without a circular collimator and a parallelepiped phantom. The software predicted the absence of collision for 19 configurations. The mean absolute error between the measured and predicted gantry angle of collision for the remaining 61 cases was 0.86 (0.01-2.49). This tool accurately predicted collisions for linac-based intracranial SRS and is easy to implement in any radiotherapy facility.

Felefly T ，Achkar S ，Khater N ，Sayah R ，Fares G ，Farah N ，El Barouky J ，Azoury F ，El Khoury C ，Roukoz C ，Nehme Nasr D ，Nasr E ... -  《-》