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Proceedings Paper

Deep learning and multi-contrast-based denoising for low-SNR Arterial Spin Labeling (ASL) MRI
Author(s): Enhao Gong; Jia Guo; Jiang Liu; Audrey Fan; John Pauly; Greg Zaharchuk
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Paper Abstract

Arterial Spin Labeling (ASL) is a popular non-invasive neuroimaging technique to use MRI for quantitatively Cerebral Blood Flow (CBF) mapping. However, ASL usually suffers from poor signal quality and repeated measurements are typically acquired to improve signal quality through averaging at the cost of long scan time. In this work, a deep learning algorithm is proposed to leverage both convolutional neural network (CNN) based image enhancement as well as combining complementary/mutual information from multiple tissue contrasts in ASL acquisition. Both quantitative and qualitative evaluation demonstrate the performance and stability of the proposed algorithm and its superiority over conventional denoising algorithms and standard deep learning based denoising. The results demonstrate the feasibility of efficient and high-quality ASL measurements from average-free fast acquisition which will enable broader clinical application of ASL.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130M (10 March 2020); doi: 10.1117/12.2549765
Show Author Affiliations
Enhao Gong, Stanford Univ. (United States)
Subtle Medical Inc. (United States)
Jia Guo, Stanford Univ. (United States)
Univ. of California (United States)
Jiang Liu, Stanford Univ. (United States)
Audrey Fan, Stanford Univ. (United States)
John Hopkins Univ. (United States)
John Pauly, Stanford Univ. (United States)
Greg Zaharchuk, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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