A knowledge-driven quasi-global registration of thoracic-abdominal CT and CBCT for image-guided interventions

Li Zhang; Christophe Chefd'hotel; Vincent Ordy; Jie Zheng; Xiang Deng; Benjamin Odry

doi:10.1117/12.2007747

12 March 2013 A knowledge-driven quasi-global registration of thoracic-abdominal CT and CBCT for image-guided interventions

Li Zhang, Christophe Chefd'hotel, Vincent Ordy, Jie Zheng, Xiang Deng, Benjamin Odry

Author Affiliations +

Proceedings Volume 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling; 867110 (2013) https://doi.org/10.1117/12.2007747
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Li Zhang, Christophe Chefd'hotel, Vincent Ordy, Jie Zheng, Xiang Deng, and Benjamin Odry "A knowledge-driven quasi-global registration of thoracic-abdominal CT and CBCT for image-guided interventions", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 867110 (12 March 2013); https://doi.org/10.1117/12.2007747

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