Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease

Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).