Dataset Information

Long-term stability and mechanical characteristics of kV digital imaging system for proton radiotherapy.

ABSTRACT: To quantitatively evaluate the long-term image panel positioning stability and gantry angle dependence for gantry-mounted kV imaging systems.For patient setup digital imaging systems in isocentric rotating proton beam delivery facilities, physical crosshairs are commonly inserted into the snout to define the kV x-ray beam isocenter. Utilizing an automatic detection algorithm, the authors analyzed the crosshair center positions in 2744 patient setup kV images acquired with the four imagers in two treatment rooms from January 2012 to January 2013. The crosshair position was used as a surrogate for imaging panel position, and its long-term stability at the four cardinal angles and the panel flex dependency on gantry angle was investigated.The standard deviation of the panel position distributions was within 0.32 mm (with the range of variation less than ± 1.4 mm) in both the X-Z plane and Y direction. The mean panel inplane rotations were not more than 0.51° for the four panels at the cardinal angles, with standard deviations ≤ 0.26°. The panel position variations with gantry rotation due to gravity (flex) were within ± 4 mm, and were panel-specific.The authors demonstrated that the kV image panel positions in our proton treatment system were highly reproducible at the cardinal angles over 13 months and also that the panel positions can be correlated to gantry angles. This result indicates that the kV image panel positions are stable over time; the amount of panel sag is predictable during gantry rotation and the physical crosshair for kV imaging may eventually be removed, with the imaging beam isocenter position routinely verified by adequate quality assurance procedures and measurements.

SUBMITTER: Zhu M

PROVIDER: S-EPMC3978297 | biostudies-other | 2014 Apr

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10?s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9?mm, 0.6-1.5?mm, and 2.6-5.8?mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5?mm reduction in the 95th-percentile SI error for patients with??>10?mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude??<5?mm was 96.5% for markerless tumor tracking and 84.1% for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.

Dataset Information

Long-term stability and mechanical characteristics of kV digital imaging system for proton radiotherapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure