Program Information
Automatic Target-Based Patient Positioning Framework for Image-Guided Radiotherapy in Prostate Cancer Treatment
M Sasahara1*, H Arimura1 , Y Shibayama2 , T Hirose1 , S Ohga1 , Y Umezu2 , H Honda1 , T Sasaki1 , (1) Kyushu University, Fukuoka, Japan, (2) Kyushu University Hospital, Fukuoka, Japan
Presentations
SU-F-J-34 (Sunday, July 31, 2016) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose: Current image-guided radiotherapy (IGRT) procedure is bone-based patient positioning, followed by subjective manual correction using cone beam computed tomography (CBCT). This procedure might cause the misalignment of the patient positioning. Automatic target-based patient positioning systems achieve the better reproducibility of patient setup. Our aim of this study was to develop an automatic target-based patient positioning framework for IGRT with CBCT images in prostate cancer treatment.
Methods: Seventy-three CBCT images of 10 patients and 24 planning CT images with digital imaging and communications in medicine for radiotherapy (DICOM-RT) structures were used for this study. Our proposed framework started from the generation of probabilistic atlases of bone and prostate from 24 planning CT images and prostate contours, which were made in the treatment planning. Next, the gray-scale histograms of CBCT values within CTV regions in the planning CT images were obtained as the occurrence probability of the CBCT values. Then, CBCT images were registered to the atlases using a rigid registration with mutual information. Finally, prostate regions were estimated by applying the Bayesian inference to CBCT images with the probabilistic atlases and CBCT value occurrence probability. The proposed framework was evaluated by calculating the Euclidean distance of errors between two centroids of prostate regions determined by our method and ground truths of manual delineations by a radiation oncologist and a medical physicist on CBCT images for 10 patients.
Results: The average Euclidean distance between the centroids of extracted prostate regions determined by our proposed method and ground truths was 4.4 mm. The average errors for each direction were 1.8 mm in anteroposterior direction, 0.6 mm in lateral direction and 2.1 mm in craniocaudal direction.
Conclusion: Our proposed framework based on probabilistic atlases and Bayesian inference might be feasible to automatically determine prostate regions on CBCT images.
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