Parkinson's disease prediction using diffusion-based atlas approach

9 March 2010 Parkinson's disease prediction using diffusion-based atlas approach

Roxana Oana Teodorescu, Daniel Racoceanu, Nicolas Smit, Vladimir Ioan Cretu, Eng King Tan, Ling Ling Chan

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 762426 (2010) https://doi.org/10.1117/12.844068
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

We study Parkinson's disease (PD) using an automatic specialized diffusion-based atlas. A total of 47 subjects, among who 22 patients diagnosed clinically with PD and 25 control cases, underwent DTI imaging. The EPIs have lower resolution but provide essential anisotropy information for the fiber tracking process. The two volumes of interest (VOI) represented by the Substantia Nigra and the Putamen are detected on the EPI and FA respectively. We use the VOIs for the geometry-based registration. We fuse the anatomical detail detected on FA image for the putamen volume with the EPI. After 3D fibers growing on the two volumes, we compute the fiber density (FD) and the fiber volume (FV). Furthermore, we compare patients based on the extracted fibers and evaluate them according to Hohen&Yahr (H&Y) scale. This paper introduces the method used for automatic volume detection and evaluates the fiber growing method on these volumes. Our approach is important from the clinical standpoint, providing a new tool for the neurologists to evaluate and predict PD evolution. From the technical point of view, the fusion approach deals with the tensor based information (EPI) and the extraction of the anatomical detail (FA and EPI).

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Roxana Oana Teodorescu, Daniel Racoceanu, Nicolas Smit, Vladimir Ioan Cretu, Eng King Tan, and Ling Ling Chan "Parkinson's disease prediction using diffusion-based atlas approach", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762426 (9 March 2010); https://doi.org/10.1117/12.844068

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KEYWORDS

Brain

Image fusion

Parkinson's disease

Image registration

Anisotropy

Image compression

Neuroimaging

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