Voxel-based automated detection of focal cortical dysplasia lesions using diffusion tensor imaging and T2-weighted MRI data.

Voxel-based automated detection of focal cortical dysplasia lesions using diffusion tensor imaging and T2-weighted MRI data.
Epilepsy Behav. 2018 May 21;84:127-134
Authors: Wang Y, Zhou Y, Wang H, Cui J, Nguchu BA, Zhang X, Qiu B, Wang X, Zhu M
Abstract
The aim of this study was to automatically detect focal cortical dysplasia (FCD) lesions in patients with extratemporal lobe epilepsy by relying on diffusion tensor imaging (DTI) and T2-weighted magnetic resonance imaging (MRI) data. We implemented an automated classifier using voxel-based multimodal features to identify gray and white matter abnormalities of FCD in patient cohorts. In addition to the commonly used T2-weighted image intensity feature, DTI-based features were also utilized. A Gaussian processes for machine learning (GPML) classifier was tested on 12 patients with FCD (8 with histologically confirmed FCD) scanned at 1.5 T and cross-validated using a leave-one-out strategy. Moreover, we compared the multimodal GPML paradigm's performance with that of single modal GPML and classical support vector machine (SVM). Our results demonstrated that the GPML performance on DTI-based features (mean AUC = 0.63) matches with the GPML performance on T2-weighted image intensity feature (mean AUC = 0.64). More promisingly, GPML yielded significantly improved performance (mean AUC = 0.76) when applying DTI-based features to multimodal paradigm. Based on the results, it can...

Source: Epilepsy and Behaviour - May 21, 2018 Category: Neurology Authors: Wang Y, Zhou Y, Wang H, Cui J, Nguchu BA, Zhang X, Qiu B, Wang X, Zhu M Tags: Epilepsy Behav Source Type: research