ArticleJournal of neuroscience methods2025
Robust cortical thickness estimation in the presence of partial volumes using adaptive diffusion equation.
Article in Journal of neuroscience methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Detecting short-interval longitudinal cortical atrophy in neurodegenerative dementias via cluster scanning: A proof of concept.Imaging neuroscience (Cambridge, Mass.)Article
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7 authors.
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Abstract
backgroundAutomated 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
methodsIn 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.
resultsWe 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
methodsThe proposed method was compared with methods based on the Laplace equation, a linked distance metric, and the FreeSurfer software package.
conclusionWe 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.
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