Comparison of anatomically informed class solution template trajectories with patient-specific trajectories for stereotactic radiosurgery and radiotherapy.

Class solution template trajectories are used clinically for efficiency, safety, and reproducibility. The aim was to develop class solutions for single cranial metastases radiotherapy/radiosurgery based on intracranial target positioning and compare to patient-specific trajectories in the context of 4π optimization. Template trajectories were constructed based on the open-source Montreal Neurological Institute (MNI) average brain. The MNI brain was populated with evenly spaced spherical target volumes (2 cm diameter, N = 243) and organs-at-risk (OARs) were identified. Template trajectories were generated for six anatomical regions (frontal, medial, and posterior, each with laterality dependence) based on previously published 4π optimization methods. Volumetric modulated arc therapy (VMAT) treatment plans generated using anatomically informed template 4π trajectories and patientspecific 4π trajectories were compared against VMAT plans from a standard four-arc template. Four-arc optimization techniques were compared to the standard VMAT template by placing three spherical targets in each of six anatomical regions of a test patient. This yielded 54 plans to compare various plan quality metrics. Increasing plan technique complexity, the total number of OAR maximum dose reductions compared to the standard arc template for the 6 anatomical classes was 4+/-2 (OFIXEDc) and 7+/-2 (OFIXEDi). In 65.6% of all cases, optimized fixed-couch positions outperformed the standard-arc template. Of the three comparisons, the most complex (OFIXEDi) showed the greatest statistical significance compared to the least complex (VMATi) across 12 plan quality metrics of maximum dose to each OAR, V12Gy, total plan Monitor Units, conformity index, and gradient index (p < 0.00417). In approximately 70% of all cases, 4π optimization methods outperformed the standard-arc template in terms of maximum dose reduction to OAR, by exclusively changing the arc geometry. We conclude that a tradeoff exists between complexity of a class solution methodology compared to patient-specific methods for arc selection, in the context of plan quality improvement.

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

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.