title: Evaluating volumetric brain registration performance using structural connectivity information.
abstract: In this paper, we propose a pipeline for evaluating the performance of brain image registration methods. Our aim is to compare how well the algorithms align subtle functional/anatomical boundaries that are not easily detectable in T1- or T2-weighted magnetic resonance images (MRI). In order to achieve this, we use structural connectivity information derived from diffusion-weighted MRI data. We demonstrate the approach by looking into how two competing registration algorithms perform at aligning fine-grained parcellations of subcortical structures. The results show that the proposed evaluation framework can offer new insights into the performance of registration algorithms in brain regions with highly varied structural connectivity profiles.
authors: University of Oxford, FMRIB Centre, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK. petrovic@fmrib.ox.ac.uk
authors: Aleksandar
authors: A
authors: Petrović
authors: Lilla
authors: L
authors: Zöllei
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D011336:Probability; D011699:Putamen / Q000033:anatomy & histology / Q000473:pathology*; D013382:Subtraction Technique; D013788:Thalamus / Q000033:anatomy & histology / Q000473:pathology*