DeepHarmony: A deep learning approach to contrast harmonization across scanner changes

Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quanti...

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Bibliographic Details
Published in:	Magnetic resonance imaging Vol. 64; pp. 160 - 170
Main Authors:	Dewey, Blake E., Zhao, Can, Reinhold, Jacob C., Carass, Aaron, Fitzgerald, Kathryn C., Sotirchos, Elias S., Saidha, Shiv, Oh, Jiwon, Pham, Dzung L., Calabresi, Peter A., van Zijl, Peter C.M., Prince, Jerry L.
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier Inc 01.12.2019
Subjects:	Atrophy Brain - diagnostic imaging Brain - pathology Clinical Protocols Cohort Studies Contrast harmonization Deep learning Deep Learning - statistics & numerical data Humans Image Interpretation, Computer-Assisted - methods Longitudinal Studies Magnetic resonance imaging Magnetic Resonance Imaging - instrumentation Magnetic Resonance Imaging - methods Multiple Sclerosis - pathology Reproducibility of Results Deep learning Magnetic resonance imaging Contrast harmonization
ISSN:	0730-725X, 1873-5894, 1873-5894
Online Access:	Get full text
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