Measurement of cortical thickness from MRI by minimum line integrals on soft-classified tissue.
Estimating the thickness of the cerebral cortex is a key step in many brain imaging studies, revealing valuable information on development or disease progression. In this work, we present a framework for measuring the cortical thickness, based on minimizing line integrals over the probability map of the gray matter in the MRI volume. We first prepare a probability map that contains the probability of each voxel belonging to the gray matter. Then, the thickness is basically defined for each voxel as the minimum line integral of the probability map on line segments centered at the point of interest. In contrast to our approach, previous methods often perform a binary-valued hard segmentation of the gray matter before measuring the cortical thickness. Because of image noise and partial volume effects, such a hard classification ignores the underlying tissue class probabilities assigned to each voxel, discarding potentially useful information. We describe our proposed method and demonstrate its performance on both artificial volumes and real 3D brain MRI data from subjects with Alzheimer's disease and healthy individuals.
Aganj
Iman
I
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA. iman@umn.edu
Sapiro
Guillermo
G
Parikshak
Neelroop
N
Madsen
Sarah K
SK
Thompson
Paul M
PM
D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001931:Brain Mapping; D016022:Case-Control Studies; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000473:pathology; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011336:Probability; D013223:Statistics as Topic