Dense deformation field estimation for brain intraoperative images registration

Mathieu S. De Craene; Aloys du Bois d'Aische; Ion-Florin Talos; Matthieu Ferrant; Peter McL. Black; Ferenc Jolesz; Ron Kikinis; Benoit Macq; Simon K. Warfield

doi:10.1117/12.534064

12 May 2004 Dense deformation field estimation for brain intraoperative images registration

Mathieu S. De Craene, Aloys du Bois d'Aische, Ion-Florin Talos, Matthieu Ferrant, Peter McL. Black, Ferenc Jolesz, Ron Kikinis, Benoit Macq, Simon K. Warfield

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.534064
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

A new fast non rigid registration algorithm is presented. The algorithm estimates a dense deformation field by optimizing a criterion that measures image similarity by mutual information and regularizes with a linear elastic energy term. The optimal deformation field is found using a Simultaneous Perturbation Stochastic Approximation to the gradient. The implementation is parallelized for symmetric multi-processor architectures. This algorithm was applied to capture non-rigid brain deformations that occur during neurosurgery. Segmentation of the intra-operative data is not required but preoperative segmentation of the brain allows the algorithm to be robust to artifacts due to the craniotomy.

Citation Download Citation

Mathieu S. De Craene, Aloys du Bois d'Aische, Ion-Florin Talos, Matthieu Ferrant, Peter McL. Black, Ferenc Jolesz, Ron Kikinis, Benoit Macq, and Simon K. Warfield "Dense deformation field estimation for brain intraoperative images registration", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.534064

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