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Abstract
Accurate coregistration of computed tomography (CT) and magnetic resonance (MR) imaging can provide clinically relevant and complementary information and can serve to facilitate multiple clinical tasks including surgical and radiation treatment planning, and generating a virtual Positron Emission Tomography (PET)/MR for the sites that do not have a PET/MR system available. Despite the long-standing interest in multimodality co-registration, a robust, routine clinical solution remains an unmet need. Part of the challenge may be the use of mutual information (MI) maximization and local phase difference (LPD) as similarity metrics, which have limited robustness, efficiency, and are difficult to optimize. Accordingly, we propose registering MR to CT by mapping the MR to a synthetic CT intermediate (sCT) and further using it in a sCT-CT deformable image registration (DIR) that minimizes the sum of squared differences. The resultant deformation field of a sCT-CT DIR is applied to the MRI to register it with the CT. Twenty-five sets of abdominopelvic imaging data are used for evaluation. The proposed method is compared to standard MI- and LPD-based methods, and the multimodality DIR provided by a state of the art, commercially available FDA-cleared clinical software package. The results are compared using global similarity metrics, Modified Hausdorff Distance, and Dice Similarity Index on six structures. Further, four physicians visually assessed and scored registered images for their registration accuracy. As evident from both quantitative and qualitative evaluation, the proposed method achieved registration accuracy superior to LPD- and MI-based methods and can refine the results of the commercial package DIR when using its results as a starting point. Supported by these, this manuscript concludes the proposed registration method is more robust, accurate, and efficient than the MI- and LPD-based methods.

Affiliations

Jin Uk Heo 1 2, Feifei Zhou 1, Robert Jones 1 3, Jiamin Zheng 4, Xin Song 4, Pengjiang Qian 4, Atallah Baydoun 2 5, Melanie S Traughber 6, Jung-Wen Kuo 1, Rose Al Helo 3, Cheryl Thompson 7, Norbert Avril 1 3, Daniel DeVincent 3, Harold Hunt 3, Amit Gupta 1 3, Navid Faraji 1 3, Michael Z Kharouta 8, Arash Kardan 1 3, David Bitonte 1 3, Christian B Langmack 8, Aaron Nelson 9, Alexandria Kruzer 9, Min Yao 6, Jennifer Dorth 8 10, John Nakayama 11, Steven E Waggoner 12, Tithi Biswas 8 10, Eleanor Harris 8 10, Susan Sandstrom 8, Bryan J Traughber 6, Raymond F Muzic Jr 1 2 3
1Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
2Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
3Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
4School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China.
5Department of Internal Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio, USA.
6Department of Radiation Oncology, Penn State University, Hershey, Pennsylvania, USA.
7Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, USA.
8Department of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
9MIM Software Inc., Cleveland, Ohio, USA.
10Department of Radiation Oncology, Case Western Reserve University, Cleveland, Ohio, USA.
11Department of Obstetrics and Gynecology, Allegheny Health Network, Pittsburgh, Pennsylvania, USA.
12Department of Obstetrics and Gynecology, Cleveland Clinic, Cleveland, Ohio, USA.