Gopal Subedi1, Todd Karasick2, Jimm Grimm3, Sheena Jain3, Jinyu Xue2, Qianyi Xu2, Yan Chen2, Sucha Asbell2, Niraj Pahlajani4, Tamara LaCouture2. 1. Bott Cancer Center, Holy Redeemer Hospital, Meadowbrook, Pennsylvania 19046 and Department of Radiation Oncology, MD Anderson Cancer Center at Cooper, Camden, New Jersey 08103. 2. Department of Radiation Oncology, MD Anderson Cancer Center at Cooper, Camden, New Jersey 08103. 3. Bott Cancer Center, Holy Redeemer Hospital, Meadowbrook, Pennsylvania 19046. 4. First Radiation Oncology Group, Jacksonville, Florida 32216.
Abstract
PURPOSE: The AAPM TG-135 report is a landmark recommendation for the quality assurance (QA) of image-guided robotic radiosurgery. The purpose of this paper is to present results pertaining to intentionally offsetting the phantom as recommended by TG-135 and to present data on targeting algorithm accuracy as a function of imager parameters in less than ideal circumstances, which had not been available at the time of publication of TG-135. METHODS: All tests in this study were performed at the Cooper University Hospital CyberKnife Center in Mt. Laurel, NJ. For intentional offsets, initial tests were performed on the Accuray-supplied anthropomorphic head and neck phantom, whereas for subsequent tests, the Accuray-supplied alignment quality assurance (AQA) phantom was used. To simulate the effects of imager parameters for larger patients, slabs of Blue Water (Standard Imaging, Inc., Middleton, WI) were added to attenuate the x-ray images in some of the tests. In conjunction with attenuated x-ray tests, the number of fiducials was varied by systematically deselecting them one at a time at the CyberKnife console. RESULTS: Tests using the AQA phantom verified that submillimeter alignments were consistently achieved even with intentional shifts and rotations of up to 10.0 mm and 1.0°, respectively. An analysis of 17 months of daily QA alignment tests showed that submillimeter alignments were achieved more than 99% of the time even with such intentional shifts and rotations of the phantom. When additional slabs of Blue Water were added to simulate patient attenuation of the x-ray images, targeting errors could be induced depending on imager parameters and the amount of Blue Water used. A series of consecutive tests showed that two helpful variables to ensure good accuracy of the system were (1) the fiducial extraction confidence level (FECL) system parameter and (2) the number of targeted fiducials. When fewer than four fiducials were used, the FECL reported by the CyberKnife was sometimes high even when a false lock occurred, so using multiple fiducials helped to ensure reliable targeting. CONCLUSIONS: Radiosurgery requires the highest degree of targeting accuracy, and in our experience, the CyberKnife has been able to maintain submillimeter accuracy consistently. It has been verified that our CyberKnife can correct for phantom shifts of up to 10.0 mm and rotations of up to 1.0°. It has also been discovered that false locks are more likely to occur with a single fiducial than with multiple fiducials. Although targeting accuracy can only be measured on a phantom, the insight gained from analyzing the QA tests can help us in devising better strategies for achieving the best treatment for our patients.
PURPOSE: The AAPM TG-135 report is a landmark recommendation for the quality assurance (QA) of image-guided robotic radiosurgery. The purpose of this paper is to present results pertaining to intentionally offsetting the phantom as recommended by TG-135 and to present data on targeting algorithm accuracy as a function of imager parameters in less than ideal circumstances, which had not been available at the time of publication of TG-135. METHODS: All tests in this study were performed at the Cooper University Hospital CyberKnife Center in Mt. Laurel, NJ. For intentional offsets, initial tests were performed on the Accuray-supplied anthropomorphic head and neck phantom, whereas for subsequent tests, the Accuray-supplied alignment quality assurance (AQA) phantom was used. To simulate the effects of imager parameters for larger patients, slabs of Blue Water (Standard Imaging, Inc., Middleton, WI) were added to attenuate the x-ray images in some of the tests. In conjunction with attenuated x-ray tests, the number of fiducials was varied by systematically deselecting them one at a time at the CyberKnife console. RESULTS: Tests using the AQA phantom verified that submillimeter alignments were consistently achieved even with intentional shifts and rotations of up to 10.0 mm and 1.0°, respectively. An analysis of 17 months of daily QA alignment tests showed that submillimeter alignments were achieved more than 99% of the time even with such intentional shifts and rotations of the phantom. When additional slabs of Blue Water were added to simulate patient attenuation of the x-ray images, targeting errors could be induced depending on imager parameters and the amount of Blue Water used. A series of consecutive tests showed that two helpful variables to ensure good accuracy of the system were (1) the fiducial extraction confidence level (FECL) system parameter and (2) the number of targeted fiducials. When fewer than four fiducials were used, the FECL reported by the CyberKnife was sometimes high even when a false lock occurred, so using multiple fiducials helped to ensure reliable targeting. CONCLUSIONS: Radiosurgery requires the highest degree of targeting accuracy, and in our experience, the CyberKnife has been able to maintain submillimeter accuracy consistently. It has been verified that our CyberKnife can correct for phantom shifts of up to 10.0 mm and rotations of up to 1.0°. It has also been discovered that false locks are more likely to occur with a single fiducial than with multiple fiducials. Although targeting accuracy can only be measured on a phantom, the insight gained from analyzing the QA tests can help us in devising better strategies for achieving the best treatment for our patients.
Authors: Anand Mahadevan; Bahman Emami; Jimm Grimm; Lawrence R Kleinberg; Kristin J Redmond; James S Welsh; Robert Rostock; Eric Kemmerer; Kenneth M Forster; Jason Stanford; Sunjay Shah; Sucha O Asbell; Tamara A LaCouture; Carla Scofield; Ian Butterwick; Jinyu Xue; Alexander Muacevic; John R Adler Journal: Front Oncol Date: 2021-02-09 Impact factor: 6.244
Authors: Sharmi Biswas; Irina Kapitanova; Sabrina Divekar; Jimm Grimm; Ian J Butterwick; Daniel Garren; Lawrence R Kleinberg; Kristin J Redmond; Michel Lacroix; Anand Mahadevan; Kenneth M Forster Journal: Technol Cancer Res Treat Date: 2021 Jan-Dec