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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Quantitative MRI data in Multiple Sclerosis patients: a pattern recognition study

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Author(s):
Rodrigo Antonio Pessini [1] ; Antonio Carlos dos Santos [2] ; Carlos Ernesto Garrido Salmon [3]
Total Authors: 3
Affiliation:
[1] University of São Paulo. Clinics Hospital, Ribeirão Preto Medical School. Center of Imaging Sciences and Medical Physics - Brasil
[2] University of São Paulo. Clinics Hospital, Ribeirão Preto Medical School. Center of Imaging Sciences and Medical Physics - Brasil
[3] University of São Paulo. Faculty of Philosophy, Sciences and Letters of Ribeirão Preto. Department of Physics - Brasil
Total Affiliations: 3
Document type: Journal article
Source: Res. Biomed. Eng.; v. 34, n. 2, p. 138-146, 2018-05-28.
Abstract

Abstract Introduction Multiple Sclerosis (MS) is a neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Quantitative Magnetic Resonance Imaging (qMRI) enables a detailed characterization of brain tissue, but generates a large number of numerical results. In this study, we elucidated the main qMRI techniques and the brain regions that allow the identification of MS patients from neuroimaging data and pattern recognition techniques. Methods The data came from the combination of computational tools of image processing and neuroimaging acquired in a 3 Tesla scanner using different techniques: Diffusion, T2 Relaxometry, Magnetization Transfer Ratio (MTR) and Structural Morphometry. Data from 126 brain regions of 203 healthy individuals and 124 MS patients were separated into two groups and processed in a data-mining program using the k-nearest-neighbor (KNN) algorithm. Results The most relevant anatomical structures in the classification procedure were: corpus callosum, precuneus, left cerebellum and fusiform. Among the quantitative techniques the most relevant was the MTR, being indicated for longitudinal studies of this disease. KNN with 5 neighbors and pre-selected attributes had a better performance with an area under the ROC curve (97.3%) and accuracy (95.7%). A restricted classification considering only brain regions previously reported in the literature as affected by MS brought slightly lower scores, area: 97.1% and accuracy: 93.2%. Conclusion The use of standard recognition techniques from quantitative neuroimaging techniques has confirmed that the white matter of the brain is the most affected tissue by MS following a global pattern with greater involvement of the left hemisphere. (AU)

FAPESP's process: 05/56447-7 - Research through images from high field magnetic resonance aimed at studies in humans
Grantee:João Pereira Leite
Support Opportunities: Inter-institutional Cooperation in Support of Brain Research (CINAPCE) - Thematic Grants