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Atlas-Powered Deep Learning (ADL) - Application to Diffusion Weighted MRI

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13431))

Abstract

Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion tensor registration to compute biomarker atlases and to estimate atlas reliability maps. We also use non-linear tensor registration to align the atlas to a subject and to estimate the error of this alignment. We use the biomarker atlas, atlas reliability map, and alignment error map, in addition to the dMRI signal, as inputs to a deep learning model for biomarker estimation. We use our framework to estimate fractional anisotropy and neurite orientation dispersion from down-sampled dMRI data on a test cohort of 70 newborn subjects. Results show that our method significantly outperforms standard estimation methods as well as recent deep learning techniques. Our method is also more robust to higher measurement down-sampling factors. Our study shows that the advantages of deep learning and atlases can be synergistically combined to achieve unprecedented biomarker estimation accuracy in dMRI.

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Acknowledgment

This study was supported in part by the National Institutes of Health (NIH) under grants R01EB031849, R01NS106030, and R01EB032366; and in part by the Office of the Director of the NIH under grant S10OD0250111.

The DHCP dataset is provided by the developing Human Connectome Project, KCL-Imperial-Oxford Consortium funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007–2013)/ERC Grant Agreement no. [319456]. We thank the families who supported this trial.

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Authors and Affiliations

  1. Computational Radiology Laboratory of the Department of Radiology at Boston Children’s Hospital, and Harvard Medical School, Boston, MA, USA

    Davood Karimi & Ali Gholipour

Authors
  1. Davood Karimi
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  2. Ali Gholipour
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Corresponding author

Correspondence to Davood Karimi .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Karimi, D., Gholipour, A. (2022). Atlas-Powered Deep Learning (ADL) - Application to Diffusion Weighted MRI. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-16431-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16430-9

  • Online ISBN: 978-3-031-16431-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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