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Unbiased Diffeomorphic Mapping of Longitudinal Data with Simultaneous Subject Specific Template Estimation

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Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics (GRAIL 2017, MICGen 2017, MFCA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10551))

Abstract

Longitudinal mapping techniques for neuroimage analysis in computational anatomy have an important potential for bias associated to the order of input scans. Geodesic trajectories which pass from a template onto a baseline image and then through each follow up image have been shown to overestimate atrophy rate in the entorhinal cortex, while the reverse is true for trajectories pass through the data in the opposite order. We propose a method to remove this source of bias by inserting a patient specific template into a time series at a specific point to be estimated from the data, and simultaneously producing a time varying mapping connecting each image in the series. We demonstrate the efficacy of this method using segmentations of the entorhinal and surrounding cortex in subjects with early Alzheimer’s disease.

Alzheimer’s Disease Neuroimaging Initiative—Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Notes

  1. 1.

    Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). For up-to-date information, see www.adni-info.org.

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Acknowledgements

This work was supported by the Kavli Foundation. This work was supported by NIH grant R01EB020062-01A1. The imaging core of the BIOCARD project is supported by NIH grant U19AG033655-09. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) [23], which is supported by National Science Foundation grant number ACI-1053575.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Authors and Affiliations

  1. Center for Imaging Science, Johns Hopkins University, Baltimore, MD, 21218, USA

    Daniel Tward & Michael Miller

  2. Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, 21218, USA

    Daniel Tward & Michael Miller

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA

    Michael Miller

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the Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Daniel Tward .

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Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. Imperial College London, London, United Kingdom

    Enzo Ferrante

  4. Inria Sophia, Sophia-Antipolis, France

    Xavier Pennec

  5. Harvard Medical School, Massachusetts Institute of Technology, Boston, Massachusetts, USA

    Adrian V. Dalca

  6. Imperial College London, London, United Kingdom

    Sarah Parisot

  7. University of Utah, Salt Lake City, Utah, USA

    Sarang Joshi

  8. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Nematollah K. Batmanghelich

  9. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Aristeidis Sotiras

  10. University of Copenhagen, Copenhagen, Denmark

    Mads Nielsen

  11. Cornell University, Ithaca, New York, USA

    Mert R. Sabuncu

  12. University of Utah, Salt Lake City, USA

    Tom Fletcher

  13. University of Indiana, Indianapolis, Indiana, USA

    Li Shen

  14. Brain and Spine Institute, Inria, Paris, France

    Stanley Durrleman

  15. University of Copenhagen, Copenhagen, Denmark

    Stefan Sommer

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Tward, D., Miller, M., the Alzheimer’s Disease Neuroimaging Initiative. (2017). Unbiased Diffeomorphic Mapping of Longitudinal Data with Simultaneous Subject Specific Template Estimation. In: Cardoso, M., et al. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics. GRAIL MICGen MFCA 2017 2017 2017. Lecture Notes in Computer Science(), vol 10551. Springer, Cham. https://doi.org/10.1007/978-3-319-67675-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-67675-3_12

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