Paper
20 March 2015 Measuring the lesion load of multiple sclerosis patients within the corticospinal tract
Jan Klein, Katrin Hanken, Jasna Koceva, Helmut Hildebrandt, Horst K. Hahn
Author Affiliations +
Abstract
In this paper we present a framework for reliable determination of the lesion load within the corticospinal tract (CST) of multiple sclerosis patients. The basis constitutes a probabilistic fiber tracking approach which checks possible parameter intervals on the fly using an anatomical brain atlas. By exploiting the range of those intervals, the algorithm is able to resolve fiber crossings and to determine the CST in its full entity although it can use a simple diffusion tensor model. Another advantage is its short running time, tracking the CST takes less than a minute. For segmenting the lesions we developed a semi-automatic approach. First, a trained classifier is applied to multimodal MRI data (T1/FLAIR) where the spectrum of lesions has been determined in advance by a clustering algorithm. This leads to an automatic detection of the lesions which can be manually corrected afterwards using a threshold-based approach. For evaluation we scanned 46 MS patients and 16 healthy controls. Fiber tracking has been performed using our novel fiber tracking and a standard defection based algorithm. Regression analysis of the old and new version of the algorithm showed a highly significant superiority of the new algorithm for disease duration. Additionally, a low correlation between old and new approach supports the observation that standard DTI fiber tracking is not always able to track and quantify the CST reliably.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jan Klein, Katrin Hanken, Jasna Koceva, Helmut Hildebrandt, and Horst K. Hahn "Measuring the lesion load of multiple sclerosis patients within the corticospinal tract", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130A (20 March 2015); https://doi.org/10.1117/12.2080765
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Cited by 2 scholarly publications and 1 patent.
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KEYWORDS
Image segmentation

Detection and tracking algorithms

Magnetic resonance imaging

Brain

Control systems

Reconstruction algorithms

Adaptive control

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