Jerry L. Prince received the B.S. degree from the University of Connecticut in 1979 and the S.M., E.E., and Ph.D. degrees in 1982, 1986, and 1988, respectively, from the Massachusetts Institute of Technology, all in electrical engineering and computer science. He worked at the Brigham and Women's Hospital, MIT Lincoln Laboratories, and The Analytic Sciences Corporation (TASC). He joined the faculty at the Johns Hopkins University in 1989, where he is currently William B. Kouwenhoven Professor in the Department of Electrical and Computer Engineering and holds joint appointments in the Departments of Radiology, Biomedical Engineering, Computer Science, and Applied Mathematics and Statistics. Dr. Prince is a Fellow of the IEEE, Fellow of the MICCAI Society, Fellow of the AIMBE, Senior Member of the SPIE, and a member of Sigma Xi. He also holds memberships in Tau Beta Pi, Eta Kappa Nu, and Phi Kappa Phi honor societies. He was an Associate Editor of IEEE Transactions on Image Processing from 1992-1995, an Associate Editor of IEEE Transactions on Medical Imaging from 2000-2004 and is currently a member of the Editorial Board of Medical Image Analysis. Dr. Prince received a 1993 National Science Foundation Presidential Faculty Fellows Award, was Maryland's 1997 Outstanding Young Engineer, and was awarded the MICCAI Society Enduring Impact Award in 2012. He is also co-founder of Sonavex, Inc. His current research interests are in image processing, computer vision, and machine learning with primary application to medical imaging; he has published over 500
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Method: The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons.
Result: The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.
Conclusions: A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration accuracy suitable to application in image-guided spine surgery across a broad range of anatomical sites and modes of deformation.
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