The GammaPod breast treatment device has been introduced to provide stereotactic radiation therapy to the breast to patients in the prone position. The GammaPod, using a stereotactic coordinate system, dynamically delivers dose to the target by rotating 25 non-overlapping Co-60 beams while the patient’s breast is translated continuously in three axes on the couch during delivery. From simulation to treatment, the patient’s breast is immobilized using mild negative pressure (150 mm Hg below atmospheric pressure) through a device-specific dual-cup system with stereotactic fiducials. This technology can be used for boost, multi-fraction partial-breast steterotactic body radiotherapy (SBRT), or single-fraction stereotactic radiosurgery (SRS). This paper reports the commissioning of the system for clinical use. The GammaPod device has four major subsystems: mechanical, dosimetric, radiation safety, and safety interlocks. Detailed methods for testing each subsystem have been identified and quantified. Mechanical systems include couch motion and accuracy along with couch sag. Dosimetric tests include absolute dose calibration, dose profiles, timer error, and plan verifications. Radiation safety includes room and wall surveys, along with device leakage measurements. Safety interlocks deal with power systems, immobilization, and treatment interrupts. The absolute dose rate of the 25 mm collimator was determined using TG-21 dosimetry protocol. The relative output factor for the 15 mm collimator was 0.94. The difference of the full-width-at-half-maximum of the single shot of the 25 mm collimator between the treatment planning system and the measurement was 0.2 mm. All interlocks were found to perform correctly, and the shield was within state and Nuclear Regulatory Commission limits. The items and techniques for commissioning the GammaPod have been developed and tested using the methods reported here.

Purpose: To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). Methods: The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discrete models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. Results: On phantom point clouds, their method achieved submillimeter reconstruction RMSE under different configurations, demonstrating quantitatively the faith of the proposed method in preserving local structural properties of the underlying surface in the presence of noise and missing measurements, and its robustness toward variations of such characteristics. On point clouds from the human subject, the proposed method successfully reconstructed all patient surfaces, filling regions where raw point coordinate readings were missing. Within two comparable regions of interest in the chest area, similar mean curvature distributions were acquired from both their reconstructed surface and CT surface, with mean and standard deviation of (µ recon = −2.7 × 10 −3 mm −1 ,σ recon = 7.0 × 10 −3 mm −1 ) and (µ CT = −2.5 × 10 −3 mm −1 ,σ CT = 5.3 × 10 −3 mm −1 ), respectively. The agreement of local geometry properties between the reconstructed surfaces and the CT surface demonstrated the ability of the proposed method in faithfully representing the underlying patient surface. Conclusions: The authors have integrat...

Purpose We develop and validate a motion model that uses real‐time surface photogrammetry acquired concurrently with four‐dimensional computed tomography (4DCT) to estimate respiration‐induced changes within the entire irradiated volume, over arbitrarily many respiratory cycles. Methods A research, couch‐mounted, VisionRT (VRT) system was used to acquire optical surface data (15 Hz, ROI = 15 × 20 cm2) from the thoraco‐abdominal surface of a consented lung SBRT patient, concurrently with their standard‐of‐care 4DCT. The end‐exhalation phase from the 4DCT was regarded as reference and for each remaining phase, deformation vector fields (DVFs) with respect to the reference phase were computed. To reduce dimensionality, the first two principal components (PCs) of the matrix of nine DVFs were calculated. In parallel, ten phase‐averaged VRT surfaces were created. Surface DVFs and corresponding PCs were computed. A principal least squares regression was used to relate the PCs of surface DVF to those of volume DVFs, establishing a relationship between time‐varying surface and the underlying time‐varying volume. Proof‐of‐concept validation was performed during each treatment fraction by concurrently acquiring 30 s time series of real‐time surface data and “ground truth” kV fluoroscopic data (FL). A ray‐tracing algorithm was used to create a digitally reconstructed fluorograph (DRF), and motion trajectories of high‐contrast, soft‐tissue, anatomical features in the DRF were compared with those from kV FL. Results For five of the six fluoroscopic acquisition sessions, the model out‐performed 4DCT in predicting contour Dice coefficient with respect to fluoroscopy‐derived contours. Similarly, the model exhibited a marked improvement over 4DCT for patch positions on the diaphragm. Model patch position errors varied from 5 to −15 mm while 4DCT errors ranged between 5 and −22.4 mm. For one fluoroscopic acquisition, a marked change in the a priori internal–external correlation resulted in model errors comparable to those of 4DCT. Conclusions We described the development and a proof‐of‐concept validation for a volumetric motion model that uses surface photogrammetry to correlate the time‐varying thoraco‐abdominal surface to the time‐varying internal thoraco‐abdominal volume. These early results indicate that the proposed approach can result in a marked improvement over 4DCT. While limited by the duration of the fluoroscopic acquisitions as well as the resolution of the acquired images, the DRF‐based proof‐of‐concept technique developed here is model‐agnostic, and therefore, has the potential to be used as an in‐patient validation tool for other volumetric motion models.

Purpose Lung motion phantoms used to validate radiotherapy motion management strategies have fairly simplistic designs that do not adequately capture complex phenomena observed in human respiration such as external and internal deformation, variable hysteresis and variable correlation between different parts of the thoracic anatomy. These limitations make reliable evaluation of sophisticated motion management techniques quite challenging. In this work, we present the design and implementation of a programmable, externally and internally deformable lung motion phantom that allows for a reproducible change in external–internal and internal–internal correlation of embedded markers. Methods An in‐house–designed lung module, made from natural latex foam was inserted inside the outer shell of a commercially available lung phantom (RSD, Long Beach, CA, USA). Radiopaque markers were placed on the external surface and embedded into the lung module. Two independently programmable high‐precision linear motion actuators were used to generate primarily anterior–posterior (AP) and primarily superior–inferior (SI) motion in a reproducible fashion in order to enable (a) variable correlation between the displacement of interior volume and the exterior surface, (b) independent changes in the amplitude of the AP and SI motions, and (c) variable hysteresis. The ability of the phantom to produce complex and variable motion accurately and reproducibly was evaluated by programming the two actuators with mathematical and patient‐recorded lung tumor motion traces, and recording the trajectories of various markers using kV fluoroscopy. As an example application, the phantom was used to evaluate the performance of lung motion models constructed from kV fluoroscopy and 4DCT images. Results The phantom exhibited a high degree of reproducibility and marker motion ranges were reproducible to within 0.5 mm. Variable correlation was observed between the displacements of internal–internal and internal–external markers. The SI and AP components of motion of a specific marker had a correlation parameter that varied from −11 to 17. Monitoring a region of interest on the phantom's surface to estimate internal marker motion led to considerably lower uncertainties than when a single point was monitored. Conclusions We successfully designed and implemented a programmable, externally and internally deformable lung motion phantom that allows for a reproducible change in external–internal and internal–internal correlation of embedded markers.

Background and Purpose Respiratory gating is a frequently-used clinical motion-management strategy in lung radiotherapy. In conventional gating, the beam is turned on during a pre-determined window; typically, around end-exhalation (EE). In this work, we postulate that the optimal gating window for each beam will be dependent on a variety of patient-specific factors, such as tumor size and location, and the extent of relative tumor and organ motion. Material and Methods In order to create optimal gating treatment plans, we started from an optimized clinical plan, created a plan per respiratory phase using the same beam arrangements, and used an inverse planning optimization approach to determine the optimal gating window for each beam and optimal beam weights, i.e., monitor units (MUs). Two pieces of information were used for optimization: (i) the state of the anatomy at each phase, extracted from 4D CT scans, and (ii) the time spent in each state, estimated from a 2-minute monitoring of the patient’s breathing motion. We retrospectively studied 15 lung cancer patients clinically treated by hypofractionated conformal radiation therapy, where 45 – 60 Gy was administered over 3 – 15 fractions using 7 – 13 beams. Mean gross tumor volume and respiratory-induced tumor motion was 82.5 cc and 1.0 cm, respectively. Results Although patients spent most of their respiratory cycle in EE, our optimal gating plans used EE for only 34% of the beams. Using optimal gating, maximum and mean doses to esophagus, heart and spinal cord were reduced by an average of 15 – 26% and the beam-on times were reduced by an average of 23% compared to equivalent single-phase EE gated plans (p < 0.034, paired, two – tailed T – test). Conclusions We introduce a personalized respiratory-gating technique where inverse planning optimization is used to determine patient- and beam-specific gating phases towards enhancing dosimetric quality of radiotherapy treatment plans.