#6237. Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson’s disease

September 2026publication date
Proposal available till 18-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Science Applications;
Cognitive Neuroscience;
Neurology;
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Abstract:
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 scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results.
Keywords:
Convolutional neural network; Diffusion kurtosis imaging; Kurtosis fractional anisotropy; Mean diffusivity; Mean kurtosis; Parkinson’s disease

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