Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising

Motohide Kawamura; Daiki Tamada; Satoshi Funayama; Marie-Luise Kromrey; Shintaro Ichikawa; Hiroshi Onishi; Utaroh Motosugi

doi:10.2463/mrms.tn.2019-0081

Technical Notes

Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising

Motohide Kawamura, Daiki Tamada, Satoshi Funayama, Marie-Luise Kromrey, Shintaro Ichikawa, Hiroshi Onishi, Utaroh Motosugi

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Keywords: magnetic resonance imaging, diffusion weighted imaging, deep learning, multi-shot echo-planar imaging

JOURNAL OPEN ACCESS

2021 Volume 20 Issue 1 Pages 99-105

DOI https://doi.org/10.2463/mrms.tn.2019-0081

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Abstract

To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.

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