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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Veröffentlicht in:Magnetic resonance imaging Jg. 64; S. 160 - 170
Hauptverfasser: 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
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier Inc 01.12.2019
Schlagworte:
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-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2019.05.041