Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

ABSTRACT:

Objective

Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods.

Methods

Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes

SUBMITTER: Ye Z 

PROVIDER: S-EPMC7261762 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature