naa a22 4500 510808018 CHVBK 20180411083427.0 cr unu---uuuuu 180411e20130701xx s 000 0 eng 10.1007/s11548-013-0826-6 doi (NATIONALLICENCE)springer-10.1007/s11548-013-0826-6 Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations [Elektronische Daten] [Dante Nigris, D. Collins, Tal Arbel] Purpose: We present a novel approach for the registration of pre-operative magnetic resonance images to intra-operative ultrasound images for the context of image-guided neurosurgery. Method: Our technique relies on the maximization of gradient orientation alignment in a reduced set of high confidence locations of interest and allows for fast, accurate, and robust registration. Performance is compared with multiple state-of-the-art techniques including conventional intensity-based multi-modal registration strategies, as well as other context-specific approaches. All methods were evaluated on fourteen clinical neurosurgical cases with brain tumors, including low-grade and high-grade gliomas, from the publicly available MNI BITE dataset. Registration accuracy of each method is evaluated as the mean distance between homologous landmarks identified by two or three experts. We provide an analysis of the landmarks used and expose some of the limitations in validation brought forward by expert disagreement and uncertainty in identifying corresponding points. Results: The proposed approach yields a mean error of 2.57mm across all cases (the smallest among all evaluated techniques). Additionally, it is the only evaluated technique that resolves all cases with a mean distance of less than 1mm larger than the theoretical minimal mean distance when using a rigid transformation. Conclusion: Finally, our proposed method provides reduced processing times with an average registration time of 0.76s in a GPU-based implementation, thereby facilitating its integration into the operating room. CARS, 2013 Neurosurgery nationallicence Multi-modal registration nationallicence GPU nationallicence Ultrasound nationallicence Nigris Dante Centre for Intelligent Machines, McGill University, H3A 0E9, Montreal, QC, Canada aut Collins D. Montreal Neurological Institute, McConnell Brain Imaging Centre, McGill University, Room WB-314, 3801 University Street, H3A 2B4, Montreal, QC, Canada aut Arbel Tal Centre for Intelligent Machines, McGill University, H3A 0E9, Montreal, QC, Canada aut International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/4(2013-07-01), 649-661 1861-6410 8:4<649 2013 8 11548 https://doi.org/10.1007/s11548-013-0826-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-013-0826-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nigris Dante Centre for Intelligent Machines, McGill University, H3A 0E9, Montreal, QC, Canada aut NATIONALLICENCE 700 1- Collins D. Montreal Neurological Institute, McConnell Brain Imaging Centre, McGill University, Room WB-314, 3801 University Street, H3A 2B4, Montreal, QC, Canada aut NATIONALLICENCE 700 1- Arbel Tal Centre for Intelligent Machines, McGill University, H3A 0E9, Montreal, QC, Canada aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/4(2013-07-01), 649-661 1861-6410 8:4<649 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer