M Fooladi - Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
H Sharini - Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
S Masjoodi - Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
E Khodamoradi - Radiology and Nuclear Medicine Department, School of Allied Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran

Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets. Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T۱ longitudinal relaxation time data of brain white matter, then the performance of three ANN-based classifiers have been investigated. Materials and Methods: The input features of ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural networks based on Akaike information criterion (ENN-AIC) were extracted in the form of QMTI and T۱ mean values from parametric maps. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria. Results: The results indicate that ENN-AIC-based classification method has achieved ۹۰% accuracy, ۹۲% sensitivity and ۸۶% precision compared to other ANN models. NPV, FPR and FDR values were found to be ۰.۹۳۳, ۰.۱۲۵ and ۰.۱۳۳, respectively, according to the proposed ENN-AIC model. A graphical representation of how to track actual data by the predictive values derived from ANN algorithms, was also presented.Conclusion: It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF.In addition, this research provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis.

Quantitative Magnetization Transfer Imaging, Relapsing Remitting Multiple Sclerosis, Artificial neural networks, Magnetic Resonance Imaging