Browsing by Author "Murphy, Martin"

Purpose: Cone-beam computed tomographic images (CBCTs) are increasingly used for setup correction, soft tissue targeting, and image-guided adaptive radiotherapy. However, CBCT image quality is limited by low contrast and imaging artifacts. This analysis investigates the detectability of soft tissue boundaries in CBCT by performing a multiple-observer segmentation study. Methods and Materials: In four prostate cancer patients prostate, bladder and rectum were repeatedly delineated by five observers on CBCTs and fan-beam CTs (FBCTs). A volumetric analysis of contouring variations was performed by calculating coefficients of variation (COV: standard deviation/average volume). The topographical distribution of contouring variations was analyzed using an average surface mesh-based method. Results: Observer- and patient-averaged COVs for FBCT/CBCT were 0.09/0.19 for prostate, 0.05/0.08 for bladder, and 0.09/0.08 for rectum. Contouring variations on FBCT were significantly smaller than on CBCT for prostate (p < 0.03) and bladder (p < 0.04), but not for rectum (p < 0.37; intermodality differences). Intraobserver variations from repeated contouring of the same image set were not significant for either FBCT or cBcr (p < 0.05). Average standard deviations of individual observers' contour differences from average surface meshes on FBCT vs. CBCT were 1.5 vs. 2.1 mm for prostate, 0.7 vs. 1.4 mm for bladder, and 1.3 vs. 1.5 mm for rectum. The topographical distribution of contouring variations was similar for FBCT and CBCT. Conclusion: Contouring variations were larger on CBCT than FBCT, except for rectum. Given the well-documented uncertainty in soft tissue contouring in the pelvis, improvement of CBCT image quality and establishment of well-defined soft tissue identification rules are desirable for image-guided radiotherapy. (C) 2010 Elsevier Inc.

Real-time tumor targeting involves the continuous realignment of the radiation beam with the tumor. Real-time tumor targeting offers several advantages such as improved accuracy of tumor treatment and reduced dose to surrounding tissue. Current limitations to this technique include mechanical motion constraints. The purpose of this study was to investigate an alternative treatment scenario using a moving average algorithm. The algorithm, using a suitable averaging period, accounts for variations in the average tumor position, but respiratory induced target position variations about this average are ignored during delivery and can be treated as a random error during planning. In order to test the method a comparison between five different treatment techniques was performed: (1) moving average algorithm, (2) real-time motion tracking, (3) respiration motion gating (at both inhale and exhale), (4) moving average gating (at both inhale and exhale) and (5) static beam delivery. Two data sets were used for the purpose of this analysis: (a) external respiratory-motion traces using different coaching techniques included 331 respiration motion traces from 24 lung-cancer patients acquired using three different breathing types [free breathing (FB), audio coaching (A) and audio-visual biofeedback (AV)]; (b) 3D tumor motion included implanted fiducial motion data for over 160 treatment fractions for 46 thoracic and abdominal cancer patients obtained from the Cyberknife Synchrony. The metrics used for comparison were the group systematic error (M), the standard deviation (SD) of the systematic error (Sigma) and the root mean square of the random error (sigma). Margins were calculated using the formula by Stroom [Int. J. Radiat. Oncol., Biol., Phys. 43(4), 905-919 (1999)]: 2 Sigma+0.7 sigma. The resultant calculations for implanted fiducial motion traces (all values in cm) show that M and Sigma are negligible for moving average algorithm, moving average gating, and real-time tracking (i.e., M and Sigma=0 cm) compared to static beam (M=0.02 cm and Sigma=0.16 cm) or gated beam delivery (M=-0.05 and 0.16 cm at both exhale and inhale, respectively, and Sigma=0.17 and 0.26 cm at both exhale and inhale, respectively). Moving average algorithm sigma=0.22 cm has a slightly lower random error than static beam delivery sigma=0.24 cm, though gating, moving average gating, and real-time tracking have much lower random error values for implanted fiducial motion. Similar trends were also observed for the results using the external respiratory motion data. Moving average algorithm delivery significantly reduces M and Sigma compared with static beam delivery. The moving average algorithm removes the nonstationary part of the respiration motion which is also achieved by AV, and thus the addition of the moving average algorithm shows little improvement with AV. Overall, a moving average algorithm shows margin reduction compared with gating and static beam delivery, and may have some mechanical advantages over real-time tracking when the beam is aligned with the target and patient compliance advantages over real-time tracking when the target is aligned to the beam. (C) 2008 American Association of Physicists in Medicine.