Optimal marker placement in photogrammetry patient positioning system.
A photogrammetry-based patient positioning system has been used instead of the conventional laser alignment technique for patient set-up in external beam radiotherapy. It tracks skin affixed reflective markers with multiple infrared cameras. The three-dimensional (3D) positions of the markers provide reference information to determine the treatment plan isocenter location and hence provide the ability to position the lesion at the isocenter of the treatment linear accelerator. However, in current clinical practice for lung or liver lesion treatments, fiducial markers are usually randomly affixed onto the patients' chest and abdomen, so that the actual target registration error (TRE) of the internal lesions inside the body may be large, depending on the fiducial registration error (FRE). There exists an optimal marker configuration that can minimize the TRE. In this paper, we developed methods to design the patient-specific optimal configurations of the surface makers to minimize the TRE, given the patient's surface contour, the lesion position and the FRE. Floating genetic algorithm (GA) optimization was used to optimize the positions of the skin markers. The surface curve of the patient body was determined by an automatic segmentation algorithm from the planning CT. The method was evaluated using a body phantom implanted with a metal ball (a simulated target). By registering two CT scans using the surface markers and measuring the displacement of the target, the TRE was measured. The TRE was also measured by taking two orthogonal portal films after positioning the phantom using the photogrammetry based patient positioning system. A 50% reduction in TRE has been achieved by using the optimal configuration compared to the random configuration. This result demonstrates that the optimization of a fiducial configuration can result in improved tumor targeting ability.
Liu
Haisong
H
Department of Radiation Oncology, University of Rochester, Rochester, New York 14642, USA. haisong_liu@urmc.rochester.edu
Yu
Yan
Y
Schell
M C
MC
O'Dell
Walter G
WG
Ruo
Russell
R
Okunieff
Paul
P
D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007103:Immobilization; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D008297:Male; D009038:Motion; D055095:Optics and Photonics / Q000295:instrumentation; D019047:Phantoms, Imaging; D010780:Photogrammetry / Q000379:methods*; D011187:Posture; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D011786:Quality Control; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods