Simulated diffusion weighted images based on model-predicted tumor growth

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationSimulation and Synthesis in Medical Imaging - 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsNinon Burgos, David Svoboda, Jelmer M. Wolterink, Can Zhao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages32-40
Number of pages9
ISBN (Print)9783030595197
DOIs
StatePublished - 2020
Event5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12417 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period10/4/2010/4/20

Keywords

  • Biomathematical tumor growth model
  • Glioblastoma
  • Glioma
  • Simulated magnetic resonance imaging

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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