UCSF

Three-dimensional brain imaging through cutting-edge MRI technology allows assessment of physical and chemical tissue properties at sub-millimeter resolution. In order to improve brain understanding as part of diagnostic tasks using MRI images, other imaging modalities to obtain deep cerebral structures and cytoarchitectural boundaries have been investigated. Under availability of postmortem samples, the fusion of MRI to brain histology supports more accurate description of neuroanatomical structures since it preserves microscopic entities and reveal fine anatomical details, unavailable otherwise. Nonetheless, histological processing causes severe tissue deformation and loss of the brain original 3D conformation, preventing direct comparisons between MRI and histology. This paper proposes an interactive computational pipeline designed to register multimodal brain data and enable direct histology-MRI correlation. Our main contribution is to develop schemes for brain data fusion, distortion corrections, using appropriate diffeomorphic mappings to align the 3D histological and MRI volumes. We describe our pipeline and preliminary developments of scalable processing schemes for highresolution images. Tests consider a postmortem human brain, and include qualitatively and quantitatively results, such as 3D visualizations and the Dice coefficient (DC) between brain structures. Preliminary results show promising DC values when comparing our scheme results to manually labeled neuroanatomical regions defined by a neurosurgeon on MRI and histology data sets. DC was computed for the left caudade gyrus (LC), right hippocampus (RH) and lateral ventricles (LV).