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. 2008 May:2008:1625-1628.
doi: 10.1109/ISBI.2008.4541324.

SEGMENTATION-FREE MEASURING OF CORTICAL THICKNESS FROM MRI

Iman Aganj  1 , Guillermo Sapiro  1 , Neelroop Parikshak  2 , Sarah K Madsen  2 , Paul M Thompson  2
Affiliations

SEGMENTATION-FREE MEASURING OF CORTICAL THICKNESS FROM MRI

Iman Aganj et al. Proc IEEE Int Symp Biomed Imaging. 2008 May.

Abstract

Estimating the thickness of cerebral cortex is one of the most essential measurements performed in MR brain imaging. In this work we present a new approach to measure the cortical thickness which is based on minimizing line integrals over the probability map of the gray matter in the MRI volume. Previous methods often perform a pre-segmentation of the gray matter before measuring the thickness. Considering the noise and the partial volume effects, there are underlying class probabilities allocated to each voxel that this hard classification ignores, a result of which is a loss of important available information. Following the introduction of the proposed framework, the performance of our method is demonstrated on both artificial volumes and real MRI data for normal and Alzheimer affected subjects.

Keywords: Cortical thickness measurement; gray matter density; magnetic resonance imaging; soft classification.

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Figures

Fig. 1
Fig. 1
Experimental results on an artificial probability map. Inner and outer surfaces of a paraboloid-shaped layer of GM are depicted. Line segments are chosen by the algorithm such that they give the smallest integrals (of the probability map) among all line segments passing through every selected test point, shown as small circles.
Fig. 2
Fig. 2
Common ways of measuring cortical thickness. (a) Coupled surface methods. (b) Closest point methods. (c) Laplace methods. (d) Largest enclosed sphere methods.
Fig. 3
Fig. 3
Computing line integrals passing through a point, and choosing the minimum integral value. (a) Binary probability map. (b) Continuous probability map.
Fig. 4
Fig. 4
(a) A sulcus that makes two sides of the gray matter layer close to each other. (b) How the algorithm might overestimate the thickness if no stopping criteria were to be used.
Fig. 5
Fig. 5
(a) A 2D slice of the volume presented in Fig. 1. (b) The same slice in the five-time-lower-resolution volume with additive Gaussian noise. (c) Binary classification of the low-resolution volume.
Fig. 6
Fig. 6
Experimental results on MRI data. All computations have been done in 3D. (a) A slice of the original volume. (b) The thickness map of the same slice. (blue thinner, red thicker)
Fig. 7
Fig. 7
3D mapping of the cortical thickness. (blue thinner, red thicker) such that gaps as narrow as one voxel are detected by the above stopping criteria.
See this image and copyright information in PMC

References

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