Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results
NeuroImage, vol. 221, pp. 117128
Abstract
Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.
Authors 38
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Lipeng Ning corresponding
Brigham and Women's Hospital · Harvard University
Affiliation as printed
Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States. Electronic address: lning@bwh.harvard.edu
Brigham and Women's Hospital, Boston, United States
Harvard Medical School, Boston, United States
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Affiliation as printed
University College London, London, United Kingdom
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Affiliation as printed
University College London, London, United Kingdom
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University of Southern California
Affiliation as printed
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States
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Affiliation as printed
University College London, London, United Kingdom
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Affiliation as printed
New York University, New York, NY, United States
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Affiliation as printed
University College London, London, United Kingdom
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Affiliation as printed
University College London, London, United Kingdom
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Affiliation as printed
University College London, London, United Kingdom
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Affiliation as printed
University College London, London, United Kingdom
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Affiliation as printed
University College London, London, United Kingdom
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Harvard University · Boston Children's Hospital
Affiliation as printed
Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
Boston Children's Hospital, Boston, United States
Harvard Medical School, Boston, United States
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Harvard University · Boston Children's Hospital
Affiliation as printed
Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
Boston Children's Hospital, Boston, United States
Harvard Medical School, Boston, United States
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Harvard University · Boston Children's Hospital
Affiliation as printed
Boston Children's Hospital, Boston, United States; Harvard Medical School, Boston, United States
Boston Children's Hospital, Boston, United States
Harvard Medical School, Boston, United States
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Brigham and Women's Hospital · Harvard University
Affiliation as printed
Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States
Brigham and Women's Hospital, Boston, United States
Harvard Medical School, Boston, United States
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Brigham and Women's Hospital · Harvard University
Affiliation as printed
Brigham and Women's Hospital, Boston, United States; Harvard Medical School, Boston, United States
Brigham and Women's Hospital, Boston, United States
Harvard Medical School, Boston, United States
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Simon Koppers Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Leon Weninger Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Julia Ebert Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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Dorit Merhof Aachen
Affiliation as printed
RWTH Aachen University, Aachen, Germany
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University College London · University of Southern California
Affiliation as printed
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States
University College London, London, United Kingdom
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Affiliation as printed
Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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King's College London · KU Leuven
Affiliation as printed
Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
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Affiliation as printed
Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Affiliation as printed
Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Affiliation as printed
Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States
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Affiliation as printed
Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
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Affiliation as printed
Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
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Affiliation as printed
Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
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Affiliation as printed
Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
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Affiliation as printed
Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States; Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States
Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States
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Eindhoven University of Technology
Affiliation as printed
Eindhoven University of Technology, Eindhoven, Netherlands
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Eindhoven University of Technology
Affiliation as printed
Eindhoven University of Technology, Eindhoven, Netherlands
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Affiliation as printed
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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Affiliation as printed
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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Affiliation as printed
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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Cardiff University · Australian Catholic University
Affiliation as printed
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom; School of Psychology, Australian Catholic University, Melbourne, Australia
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
School of Psychology, Australian Catholic University, Melbourne, Australia
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Affiliation as printed
Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
Cited by 89 stored of 89
Cited by patents worldwide 2 (Lens.org)
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Harmonizing diffusion tensor images using machine learningUS11768265B2 2023-09-26 Active
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HARMONIZING DIFFUSION TENSOR IMAGES USING MACHINE LEARNINGWO2023056501A1 2023-04-13 Pending