In-vivo cortical thickness estimation from high-resolution T1w MRI scans in healthy and mucopolysaccharidosis affected dogs

René Labounek, Khoi Mai, Bryon Mueller, N. Matthew Ellinwood, Patricia Dickson, Igor Nestrašil. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
July 2019, Article number 8856826, Pages 2848-2851. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019; Berlin; Germany; 23 July 2019 through 27 July 2019; Category numberCFP19EMB-ART; Code 152547 Read More


Cortical thickness measurement estimated from high-resolution anatomical MRI scans may serve as a marker of cortical atrophy in clinical research applications. Most of the working algorithms and pipelines are optimized for human in-vivo data analyses that offer robust and reproducible measures. As animal-models are widely utilized in many preclinical phases of clinical trials the need for an optimized automated MRI data analysis to yield reliable data is warranted. We present a processing pipeline optimized for cortical thickness estimation of canine brains in native and template spaces. Preliminary results of 5 healthy and 5 mucopolysaccharidosis (MPS) dogs demonstrate single-canine mean/median cortical thickness in range of 2.69-3.58mm in native space and 3.26-4.15mm in template space. Our MRI generated values exceed previous histological measurements (observed mean about 2mm) in limited literature reports. Randomly selected manual measures corroborated the ranges defined by estimated cortical thickness probability density functions. Geometric transformations between native and template spaces change absolute mean/median cortical thickness values, but do not change the data nature and properties since the Pearson correlation coefficients between different space estimates were 0.84 for mean values and 0.89 for median values. No significant difference in total cortical thickness between MPS and age-and gender-matched dogs was observed. © 2019 IEEE.

Full Text


Posted on January 27, 2020
Posted in: Lysosome, NeuroRestorative Therapy, Publications Authors: