Bilateral Weighted Adaptive Local Similarity Measure for Registration in Neurosurgery
Image-guided neurosurgery involves the display of MRI-based preoperative plans in an intraoperative reference frame. Interventional MRI (iMRI) can serve as a reference for non-rigid registration based propagation of preoperative MRI. Structural MRI images exhibit spatially varying intensity relationships, which can be captured by a local similarity measure such as the local normalized correlation coefficient (LNCC). However, LNCC weights local neighborhoods using a static spatial kernel and includes voxels from beyond a tissue or resection boundary in a neighborhood centered inside the boundary. We modify LNCC to use locally adaptive weighting inspired by bilateral filtering and evaluate it extensively in a numerical phantom study, a clinical iMRI study and a segmentation propagation study. The modified measure enables increased registration accuracy near tissue and resection boundaries.
KeywordsNon-rigid registration Similarity measure Neurosurgery
This work was part funded by the Wellcome Trust (WT101957, WT106882, 201080/Z/16/Z), the Engineering and Physical Sciences Research Council (EPSRC grants EP/N013220/1, EP/N022750/1, EP/N027078/1, NS/A000027/1) and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative). MK is supported by the UCL Doctoral Training Programme in Medical and Biomedical Imaging studentship funded by the EPSRC (EP/K502959/1). MM is supported by the UCL Leonard Wolfson Experimental Neurology Centre (PR/ylr/18575) and received further funding from Alzheimer’s Society (AS-PG-15-025). GPW is supported by MRC Clinician Scientist Fellowship (MR/M00841X/1). DS receives further funding from the EU-Horizon2020 project EndoVESPA (H2020-ICT-2015-688592).
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