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

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.

Digital Twins for Multiple Sclerosis

“…Using this platform to put imaging data in context with other multidimensional data offers unique possibilities of validation and implementation. Thus, in future, quantitative MRI will enable a detailed characterization of brain tissue by generating a large number of numerical results ( 150 ). More than a thousand parameters can be generated if a detailed segmentation of the brain is considered, making group studies complex and inefficient by parametric techniques of data analysis ( 150 ).…”

“…Thus, in future, quantitative MRI will enable a detailed characterization of brain tissue by generating a large number of numerical results ( 150 ). More than a thousand parameters can be generated if a detailed segmentation of the brain is considered, making group studies complex and inefficient by parametric techniques of data analysis ( 150 ). The large volume of MRI data can only be approached by AI, an essential tool of the DTMS ( 151 ).…”

Review of advanced computational approaches on multiple sclerosis segmentation and classification

“…7. This article reviewed more than 70 state- [54] non-local means (NLM) NA Brainweb database concept of 5D NLM is used to achieve higher accuracy with lower distortion Valverde et al 2014 [55] FAST and SPM8 NA OASIS database high accuracy WM lesion filling is achieved using FAST and SPM8 techniques Khotanlou and Afrasiabi 2011 [56] spatially constrained possibilistic fuzzy C means (SCPFCM) [37] 2016 differential evolution 81.68 Yeliz, K. and Şengül, H. [63] 2015 feed-forward back propagation 96.75 Esposito et al [40] 2010 evolutionary-fuzzy 88.71 Barry R. Greene et al [41] 2015 logistic regression model 96.90 Ayelet Akselrod-Ballin et al [44] 2009 decision forest 98.50 Simaa Hamid et al [45] 2016 grey level run length matrix 96.90 Yudong Zhang et al [47] 2016 SWE + KNN 97.94 Youngjin Yoo et al [52] 2016 CNN + Euclidean distance transform 72.90 Chang et al [64] 2018 deep CNN 94 Yijun Zhao et al [65] 2017 SVM 81 Punal M. Arabi et al [23] 2017 machine vision 90 Arman Eshaghi et al [66] 2016 random forest 80 Yu-Dong Zhang et al [53] 2018 CNN-PReLU-Dropout 98.23 Zhou and Shen [48] 2018 biogeography-based optimisation with GLCM 92.75 Washimkar and Chede [24] 2017 KNN 97 Rodrigo Antonio Pessini et al [67] 2018 KNN 95 Siar and Teshnehlab [68] 2019 CNN 96.88 Fooladi et al [38] 2018 ENN-AIC 90 Shui-Hua Wang et al [ of-the-art articles to provide broad knowledge of the concepts of MS, and analysed the techniques used for the segmentation and classification of MS diagnoses using MRI and images collected from different databases. Here, the challenges of the lesion segmentation and the classification problem were described and solutions identified.…”

Functional and structural brain connectivity in congenital deafness

“…Therefore, the hearing individuals did not perform the audiometric evaluation. It was assumed they had auditory thresholds within the normal range since they participated in other functional MRI studies as a control group (Siva Costa et al 2019, Pessini et al 2018, Rondinoni et al 2013).…”