Evidence map›Paper›PMID 31301354›Full record

ArticleMagnetic resonance imaging2019

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

Blake E Dewey, Can Zhao, Jacob C Reinhold, Aaron Carass, Kathryn C Fitzgerald, Elias S Sotirchos, Shiv Saidha, Jiwon Oh, Dzung L Pham, Peter A Calabresi and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

94 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  16. The Role of Harmonization: A Systematic Analysis of Various Task-based Scenarios.Proceedings of SPIE--the International Society for Optical Engineering · 2025
    Article
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34 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Blake E DeweyDepartment of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Kirby Center for Functional Brain Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA. Electronic address: blake.dewey@jhu.edu.
Can ZhaoDepartment of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA.
Jacob C ReinholdDepartment of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA.
Aaron CarassDepartment of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA.
Kathryn C FitzgeraldDepartment of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Elias S SotirchosDepartment of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Shiv SaidhaDepartment of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jiwon OhDepartment of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Dzung L PhamDepartment of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA.
Peter A CalabresiDepartment of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Peter C M van ZijlKirby Center for Functional Brain Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jerry L PrinceDepartment of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, USA; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

TR&D 4: Algorithms for functional and anatomical brain analysisP41EB015909 · NIBIB · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI VAN ZIJL, PETER CM · 2012 to 2020
$13.1M
Imaging Neurodegeneration in Multiple SclerosisR01NS082347 · NINDS · JOHNS HOPKINS UNIVERSITY · PI PETER A CALABRESI, Shiv Saidha · 2013 to 2026
$7.0M
NIBIB NIH HHS P41 EB015909NINDS NIH HHS R01 NS082347
6 · The paper itself

Abstract

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.

Indexed as

AtrophyBrainClinical ProtocolsCohort StudiesDeep LearningHumansImage Interpretation, Computer-AssistedLongitudinal StudiesMagnetic Resonance ImagingMultiple SclerosisReproducibility of ResultsContrast harmonizationDeep learningMagnetic resonance imaging

Identifiers

PMID31301354
PMCPMC6874910

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.