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Cluster-Guided Multiscale Lung Modeling via Machine Learning

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Handbook of Materials Modeling

Abstract

Accurate prediction of airflow distribution and aerosol transport in the human lungs, which are difficult to be measured in vivo but important to understand the structure and function relationship, is challenging. It is because the interplay between them spans more than two orders of magnitude in dimension from the trachea to alveoli. This chapter reviews the techniques and strategies for modeling lungs both within and between subjects, viz., subject specificity versus generalization from individuals to populations, with both exhibiting multiscale characteristics. For “within-subject” modeling, a computed tomography (CT)-derived subject-specific computational fluid dynamics (CFD) lung model is presented. The pipeline for building such an imaging-based lung model is composed of image segmentation and processing, geometrical modeling labeled with anatomical information, image registration, three-dimensional (accurate) and one-dimensional (approximate) coupling techniques, and a high-fidelity turbulent flow model. The subject-specific model is essential in predicting local structural and functional interactions. For “between subjects” modeling, machine learning is employed to identify homogeneous subpopulations (clusters), among healthy and diseased populations, aiming to bridge individual and population scales. For this purpose, three major issues need to be addressed. They are intersubject variability (due to, e.g., gender, age, and height), inter-site variability (due to scanner and imaging protocol differences), and definition of quantitative CT imaging-based metrics at multiple scales (due to alterations at different disease stages) needed for clustering. The use of the cluster membership to select representative subjects for detailed CFD analysis enables an examination of the cluster-specific structural and functional relationships.

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Acknowledgments

This work was supported in part by NIH grants U01-HL114494, R01 HL094315, R01-HL112986, and S10-RR022421 as well as Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03034157). We also thank the San Diego Supercomputer Center (SDSC), the Texas Advanced Computing Center (TACC), and XSEDE sponsored by the National Science Foundation for the computer time.

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Authors and Affiliations

  1. Department of Mechanical Engineering, The University of Iowa, Iowa City, IA, USA

    Ching-Long Lin, Babak Haghighi & Jiwoong Choi

  2. Department of Radiology, The University of Iowa, Iowa City, IA, USA

    Ching-Long Lin & Eric A. Hoffman

  3. IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA, USA

    Ching-Long Lin, Babak Haghighi & Jiwoong Choi

  4. School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea

    Sanghun Choi

  5. Department of Biomedical Engineering, The University of Iowa, Iowa, IA, USA

    Eric A. Hoffman

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  1. Ching-Long Lin
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  2. Sanghun Choi
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  3. Babak Haghighi
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  4. Jiwoong Choi
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  5. Eric A. Hoffman
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Correspondence to Ching-Long Lin .

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Editors and Affiliations

  1. Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science & Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Sidney Yip

Section Editor information

  1. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States

    Ming Dao

  2. Division of Applied Mathematics, Brown University, Providence, RI, USA

    George E Karniadakis

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Lin, CL., Choi, S., Haghighi, B., Choi, J., Hoffman, E.A. (2018). Cluster-Guided Multiscale Lung Modeling via Machine Learning. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-50257-1_98-1

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  • DOI: https://doi.org/10.1007/978-3-319-50257-1_98-1

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