A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses

Along-Tract Parameterization of White Matter Microstructure using Medial Tractography Analysis (MeTA)

Diffusion MRI tractography is a noninvasive method to estimate the structural connectivity of white matter (WM) bundles (tracts) in the human brain, which can help us understand brain function and neurodegenerative diseases. Existing techniques for analyzing WM microstructure along the length of bundles often require registering all individuals into a common space that may ignore potentially key differences in the shape and alignment of the tracts. We propose the Medial Tractography Analysis (MeTA) method to reduce partial voluming and microstructural heterogeneity in dMRI metrics while retaining bundle shape and capturing the regional variation within bundles. We performed reliability, compatibility, and disease-based validations. MeTA showed moderate to good overall overlap for most bundles in a test-retest dataset and preserved regional compatibility when applied to a dataset of subjects scanned with both high and low angular resolution protocols. Diffusion tensor imaging (DTI) metrics along the length of MeTA bundles had strong associations with cognitive impairment in ADNI. MeTA may be a reliable approach to identify regional abnormalities in clinical populations across multiple diffusion acquisitions.</p