Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease.

ABSTRACT:

Objectives

The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson's disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD.

Methods

A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of sca

SUBMITTER: Sun J 

PROVIDER: S-EPMC8479023 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature