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Abstract #4698

Improved Low Angular Resolution Diffusion Parametric Maps Using Deep Learning

Nontharat Tucksinapinunchai1, Doug P. VanderLaan2, Diana E. Peragine2, Malvina Skorska2, and Uten Yarach1
1Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chaing Mai, Thailand, 2Department of Psychology, University of Totonto Mississauga, Mississauga, ON, Canada

Synopsis

Keywords: Analysis/Processing, Neuro, Brain, White Matter

Motivation: The DTI technique is used to analyze and evaluate the white matter microstructure; however, the acquisition time is too long for clinical practice and large-scale research.

Goal(s): To reduce acquisition time by improving the low angular resolution diffusion parametric maps.

Approach: The deep-learning framework was used to improve the low angular resolution diffusion parametric maps and image quality measured with PSNR, and NRMSE.

Results: Our deep-learning framework improves low angular resolution diffusion parametric maps by effectively acquiring fiber information in FA map and enhancing overall image quality with increased PSNR and decreased NRMSE.

Impact: The reduced acquisition time and improved quality of the low angular resolution diffusion parametric maps obtained with our deep-learning framework may benefit to clinicians and researchers who study in white matter microstructure in routine clinical practice and large-scale research.

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