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

Journal: Magnetic resonance imaging
Published Date: Jul 10, 2019

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.

Authors

  • Blake E Dewey
    Department 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 Zhao
    Ethnic Medical School, Chengdu University of Traditional Chinese Medicine, Chengdu 611131, China.
  • Jacob C Reinhold
    Department of Electrical and Computer Engineering, The Johns Hopkins University, 105 Barton Hall, 3400 N. Charles St., Baltimore, MD 21218, USA.
  • Aaron Carass
    Department of Computer Science, The Johns Hopkins University, United States; Department of Electrical and Computer Engineering, The Johns Hopkins University, United States.
  • Kathryn C Fitzgerald
    Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Elias S Sotirchos
    Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Shiv Saidha
    Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Jiwon Oh
    Division of Neurology, St. Michael's Hospital, Department of Medicine, University of Toronto, Toronto, ON, Canada.
  • Dzung L Pham
    Clinical Center, National Institutes of Health, Bethesda MD 20814, USA.
  • Peter A Calabresi
    Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Peter C M van Zijl
    Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA.
  • Jerry L Prince
    Department of Electrical and Computer Engineering, The Johns Hopkins University, United States.

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