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

被引:7
|
作者
Northway, Cassidy [1 ]
Lincoln, John David [1 ]
Little, Brian [2 ]
Syme, Alasdair [1 ,2 ,3 ,4 ]
Thomas, Christopher G. [1 ,2 ,3 ,4 ,5 ]
机构
[1] Dalhousie Univ, Dept Phys & Atmospher Sci, Halifax, NS, Canada
[2] Nova Scotia Hlth Author, Dept Med Phys, Halifax, NS, Canada
[3] Dalhousie Univ, Dept Radiat Oncol, Halifax, NS, Canada
[4] Beatrice Hunter Canc Res Inst, Halifax, NS, Canada
[5] Dalhousie Univ, Dept Radiol, Halifax, NS, Canada
关键词
collision detection; extracranial stereotactic; SBRT; noncoplanar VMAT; patient-specific; COUCH; AVOIDANCE; PREVENTION; SIMULATION; SOFTWARE; DELIVERY; MOTION;
D O I
10.1002/mp.15452
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose The 4 pi 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 4 pi 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 4 pi 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 4 pi SBRT.
引用
收藏
页码:1407 / 1416
页数:10
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