Consistent 4D cortical thickness measurement for longitudinal neuroimaging study.

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 01/2010; 13(Pt 2):133-42.
Source: PubMed


Accurate and reliable method for measuring the thickness of human cerebral cortex provides powerful tool for diagnosing and studying of a variety of neuro-degenerative and psychiatric disorders. In these studies, capturing the subtle longitudinal changes of cortical thickness during pathological or physiological development is of great importance. For this purpose, in this paper, we propose a 4D cortical thickness measuring method. Different from the existing temporal-independent methods, our method fully utilizes the 4D information given by temporal serial images. Therefore, it is much more resistant to noises from the imaging and pre-processing steps. The experiments on longitudinal image datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) show that our method significantly improves the longitudinal stability, i.e. temporal consistency, in cortical thickness measurement, which is crucial for longitudinal study. Power analysis of the correlation between cortical thickness and Mini-Mental-Status-Examination (MMSE) score demonstrated that our method generates statistically more significant results when comparing with the 3D temporal-independent thickness measuring methods.

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Available from: Zhong Xue, Aug 13, 2014
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    • "Han et al. (2006) describe a method to initialize follow-up surface reconstruction with surfaces constructed from the baseline scans. Li et al. (2010) register follow-up images to the baseline (rigidly and nonlinearly based on CLASSIC) and then keep the directions fixed across time along which they locally compute thickness in the cortex. Over the last few years, several authors focused more strongly on avoiding processing bias. "
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