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Simulated Diffusion Weighted Images Based on Model-Predicted Tumor Growth

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Simulation and Synthesis in Medical Imaging (SASHIMI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12417))

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Abstract

Non-invasive magnetic resonance imaging (MRI) is the primary imaging modality for visualizing brain tumor growth and treatment response. While standard MRIs are central to clinical decision making, advanced quantitative imaging sequences like diffusion weighted imaging (DWI) are increasingly relied on. Deciding the best way to interpret DWIs, particularly in the context of treatment, is still an area of intense research. With DWI being indicative of tissue structure, it is important to establish the link between DWI and brain tumor mathematical growth models, which could help researchers and clinicians better understand the tumor’s microenvironmental landscape. Our goal was to demonstrate the potential for creating a DWI patient-specific untreated virtual imaging control (UVICs), which represents an individual tumor’s untreated growth and could be compared with actual patient DWIs. We generated a DWI UVIC by combining a patient-specific mathematical model of tumor growth with a multi-compartmental MRI signal equation. GBM growth was mathematically modeled using the Proliferation-Invasion-Hypoxia-Necrosis-Angiogenesis-Edema (PIHNA-E) model, which simulated tumor as being comprised of multiple cellular phenotypes interacting with vasculature, angiogenic factors, and extracellular fluid. The model’s output consisted of spatial volume fraction maps for each microenvironmental species. The volume fraction maps and corresponding T2 and apparent diffusion coefficient (ADC) values from literature were incorporated into a multi-compartmental signal equation to simulate DWI images. Simulated DWIs were created at multiple b-values and then used to calculate ADC maps. We found that the regional ADC values of simulated tumors were comparable to literature values.

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Acknowledgements

The authors would like to thank Drs. Savannah C. Partridge and Paul E. Kinahan for many helpful discussions. Further, we acknowledge the following funding sources that made our work possible: John M. Nasseff, Sr. Career Development Award in Neurologic Surgery Research, Moffitt PSOC U54CA193489, Diversity Supplement 3U54CA193489-04S3, Mayo U01 U01CA220378, and MIT PSOC U54CA210180.

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Authors and Affiliations

  1. Mayo Clinic, Phoenix, AZ, 85054, USA

    Pamela R. Jackson, Andrea Hawkins-Daarud & Kristin R. Swanson

Authors
  1. Pamela R. Jackson
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  2. Andrea Hawkins-Daarud
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  3. Kristin R. Swanson
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Corresponding author

Correspondence to Pamela R. Jackson .

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Editors and Affiliations

  1. CNRS – Paris Brain Institute, Paris, France

    Ninon Burgos

  2. Masaryk University, Brno, Czech Republic

    David Svoboda

  3. University of Twente, Enschede, The Netherlands

    Jelmer M. Wolterink

  4. Johns Hopkins University, Bethesda, MD, USA

    Can Zhao

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Jackson, P.R., Hawkins-Daarud, A., Swanson, K.R. (2020). Simulated Diffusion Weighted Images Based on Model-Predicted Tumor Growth. In: Burgos, N., Svoboda, D., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2020. Lecture Notes in Computer Science(), vol 12417. Springer, Cham. https://doi.org/10.1007/978-3-030-59520-3_4

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  • DOI: https://doi.org/10.1007/978-3-030-59520-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59519-7

  • Online ISBN: 978-3-030-59520-3

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