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Proceedings Paper

Fast radioactive seed localization in intraoperative cone beam CT for low-dose-rate prostate brachytherapy
Author(s): Yu-chi Hu; Jian-ping Xiong; Gilad Cohan; Marco Zaider; Gig Mageras; Michael Zelefsky
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Paper Abstract

A fast knowledge-based radioactive seed localization method for brachytherapy was developed to automatically localize radioactive seeds in an intraoperative volumetric cone beam CT (CBCT) so that corrections, if needed, can be made during prostate implant surgery. A transrectal ultrasound (TRUS) scan is acquired for intraoperative treatment planning. Planned seed positions are transferred to intraoperative CBCT following TRUS-to-CBCT registration using a reference CBCT scan of the TRUS probe as a template, in which the probe and its external fiducial markers are pre-segmented and their positions in TRUS are known. The transferred planned seeds and probe serve as an atlas to reduce the search space in CBCT. Candidate seed voxels are identified based on image intensity. Regions are grown from candidate voxels and overlay regions are merged. Region volume and intensity variance is checked against known seed volume and intensity profile. Regions meeting the above criteria are flagged as detected seeds; otherwise they are flagged as likely seeds and sorted by a score that is based on volume, intensity profile and distance to the closest planned seed. A graphical interface allows users to review and accept or reject likely seeds. Likely seeds with approximately twice the seed volume are automatically split. Five clinical cases are tested. Without any manual correction in seed detection, the method performed the localization in 5 seconds (excluding registration time) for a CBCT scan with 512×512×192 voxels. The average precision rate per case is 99% and the recall rate is 96% for a total of 416 seeds. All false negative seeds are found with 15 in likely seeds and 1 included in a detected seed. With the new method, updating of calculations of dose distribution during the procedure is possible and thus facilitating evaluation and improvement of treatment quality.

Paper Details

Date Published: 8 March 2013
PDF: 7 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867108 (8 March 2013); doi: 10.1117/12.2007957
Show Author Affiliations
Yu-chi Hu, Memorial Sloan-Kettering Cancer Ctr. (United States)
Jian-ping Xiong, Memorial Sloan-Kettering Cancer Ctr. (United States)
Gilad Cohan, Memorial Sloan-Kettering Cancer Ctr. (United States)
Marco Zaider, Memorial Sloan-Kettering Cancer Ctr. (United States)
Gig Mageras, Memorial Sloan-Kettering Cancer Ctr. (United States)
Michael Zelefsky, Memorial Sloan-Kettering Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 8671:
Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Ziv R. Yaniv, Editor(s)

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