Robust cortical thickness estimation in the presence of partial volumes using adaptive diffusion equation.

Background: Automated estimation of cortical thickness in brain MRI is a critical step when investigating neuroanatomical population differences and changes associated with normal development and aging, as well as in neurodegenerative diseases such as Alzheimer's and Parkinson's. The limited spatial resolution of the scanner leads to partial volume effects, where each voxel in the scanned image may represent a mixture of more than one type of tissue. Due to the highly convoluted structure of the cortex, this can have a significant impact on the accuracy of thickness estimates, particularly if a hard intensity threshold is used to delineate cortical boundaries.

New Methods: In this paper, we describe a novel method based on an adaptive diffusion equation (ADE) that explicitly accounts for the presence of partial tissue volumes to estimate cortical thickness more accurately. The diffusivity term uses gray matter fractions to incorporate partial tissue volumes into the thickness calculation.

Results: We show that the proposed method is robust to the effects of finite voxel resolution and blurring. The method was validated through simulations, comparisons with histological measurements reported in the literature, and single- and multi-scanner test-retest studies.

Comparison With Existing Methods: The proposed method was compared with methods based on the Laplace equation, a linked distance metric, and the FreeSurfer software package.

Conclusion: We introduced a novel method (ADE) for estimating cortical thickness that is robust to variations in image resolution and scanner field strength. ADE yields accurate, histologically consistent thickness estimates and demonstrates superior consistency in multi-scanner test-retest studies.