Multimodal Whole Brain Registration: MRI and High Resolution Histology

Abstract

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) be-tween 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).

Magnetic resonance diffusion tensor imaging for the pedunculopontine nucleus: proof of concept and histological correlation

“…This method allowed the PPN outlines to be accurately depicted in T1 MR images. The registration quality control was demonstrated in a previous work from our group (Alegro et al 2015). To define the PPN stereotactic coordinates relative to the posterior comissure (PC) and Afshar’s coordinate system (Afshar et al 1978), we used SPM 12 (Neuroimaging Informatics Tools and Resources Clearinghouse, UCL, UK, 2014) to normalize the post-mortem MRI to the MNI space (ICBM152) and apply the transformations to our previously registered PPN masks.…”

“…A pipeline was developed by our group (Alegro et al 2015), which includes 2D and 3D registration, in three steps: (1) registering MRI, blockface and digitalized histology images; (2) bidimensional registration between histological images and blockface; (3) tridimensional registration between histological and MR images. Symmetric diffeomorphic registration method (SyN) (Avants et al 2008) and ANTs (Avants et al 2015) allows saving the transform that occurs during histological processing.…”

Deep Learning for Alzheimer’s Disease: Mapping Large-scale Histological Tau Protein for Neuroimaging Biomarker Validation

“…Histological sections were stored in 70% alcohol for further processing. Details for tissue processing celloidin embedding and cutting have been debrided by us elsewhere 1,[14][15][16] Immunohistochemical labeling of abnormal tau protein inclusions In Alzheimer's disease, tau protein becomes hyperphosphorylated and aggregated into inclusions. It is unclear which of these abnormal tau forms the tracers are binding to.…”

Tensor Image Registration Library: Automated Non-Linear Registration of Sparsely Sampled Histological Specimens to Post-Mortem MRI of the Whole Human Brain

“…However, given the size of the human brain, the acquisition of systematically sampled histology data requires bespoke slicing and stain automation hardware, none of which is readily available at most neuropathology facilities. This approach is therefore better suited to study the brains of small animals or, with substantial time and workforce commitment, a single human brain [35, 36].…”

“…Recently, Majka et al [46] released Possum, an open-source framework for reconstructing volumetric histology data, which is a great step toward standardising this aspect of stack-based MRI-histology registration. A recent preprint by Alegro et al [36, 47] reported an automated pipeline for serial histological sections. To the best of our knowledge, a similar software tool for slice-to-volume registration of small stand-alone histological images to MRI data is still not available to date.…”