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...

Full description

Saved in:

Bibliographic Details
Published in:	Magnetic resonance imaging 2019-12, Vol.64, p.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:	Article
Language:	eng
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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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 quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.
ISSN:	0730-725X 1873-5894