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Proceedings Paper

A knowledge-driven quasi-global registration of thoracic-abdominal CT and CBCT for image-guided interventions
Author(s): Li Zhang; Christophe Chefd'hotel; Vincent Ordy; Jie Zheng; Xiang Deng; Benjamin Odry
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Paper Abstract

In this work, we have developed a novel knowledge-driven quasi-global method for fast and robust registration of thoracic-abdominal CT and cone beam CT (CBCT) scans. While the use of CBCT in operating rooms has become a common practice, there is an increasing demand on the registration of CBCT with pre-operative scans, in many cases, CT scans. One of the major challenges of thoracic-abdominal CT/CBCT registration is from various fields of view (FOVs) of the two imaging modalities. The proposed approach utilizes a priori knowledge of anatomy to generate 2D anatomy targeted projection (ATP) images that surrogate the original volumes. The use of lower dimension surrogate images can significantly reduce the computation cost of similarity evaluation during optimization and make it practically feasible to perform global optimization based registration for image-guided interventional procedures. Another a priori knowledge about the local optima distribution on energy curves is further used to effectively select multi-starting points for registration optimization. 20 clinical data sets were used to validate the method and the target registration error (TRE) and maximum registration error (MRE) were calculated to compare the performance of the knowledge-driven quasi-global registration against a typical local-search based registration. The local search based registration failed on 60% cases, with an average TRE of 22.9mm and MRE of 28.1mm; the knowledge-driven quasi-global registration achieved satisfactory results for all the 20 data sets, with an average TRE of 3.5mm, and MRE of 2.6mm. The average computation time for the knowledge-driven quasi-global registration is 8.7 seconds.

Paper Details

Date Published: 12 March 2013
PDF: 11 pages
Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867110 (12 March 2013); doi: 10.1117/12.2007747
Show Author Affiliations
Li Zhang, Siemens Corp. Technology (United States)
Christophe Chefd'hotel, Siemens Corp. Technology (United States)
Vincent Ordy, Siemens Corp. Technology (United States)
Jie Zheng, Siemens Ltd. China (China)
Xiang Deng, Siemens Ltd. China (China)
Benjamin Odry, Siemens Corp. Technology (United States)

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

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