Patient‐specific collision zones for 4π trajectory optimized radiation therapy. Issue 3 (21st January 2022)

• Record Type: Journal Article
• Authors: Northway, Cassidy; Lincoln, John David; Little, Brian; Syme, Alasdair; Thomas, Christopher G
• Is Part Of: Medical physics. Volume 49:Issue 3(2022)
• Page Start: 1407
• Page End: 1416
• Publication Date: 2022-01-21
• Subjects: collision detection -- extracranial stereotactic/SBRT -- noncoplanar VMAT -- patient‐specific; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15452
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: 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. Methods: 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. Results: 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%. Conclusions: 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.