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Related Experiment Video

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Translational Orthotopic Models of Glioblastoma Multiforme
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IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION.

Klaudius Scheufele1, Shashank Subramanian2, Andreas Mang3

  • 1Institut for Parallel and Distributed Systems, Universität Stuttgart, Universitätsstraße 38, 70569, Stuttgart, Germany.

SIAM Journal on Scientific Computing : a Publication of the Society for Industrial and Applied Mathematics
|October 19, 2020
PubMed
Summary

This study calibrates a glioblastoma tumor growth model using a single magnetic resonance imaging (MRI) scan by simulating a second snapshot. The novel method enhances accuracy and reliability in patient-specific tumor parameter estimation.

Keywords:
35K4049M1549M2065K1065N3565Y0592C50PDE-constrained optimizationPicard iterationbiophysical model calibrationimage registrationtumor progression inversion

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Area of Science:

  • Computational biology
  • Medical imaging
  • Biophysics

Background:

  • Biophysical tumor growth models often require multiple time-point data for accurate calibration.
  • Glioblastoma multiforme (GBM) necessitates precise modeling for effective treatment planning.
  • Magnetic Resonance Imaging (MRI) provides essential multiparametric data for tumor characterization.

Purpose of the Study:

  • To develop a novel formulation for calibrating a biophysical tumor growth model from a single-time snapshot, multiparametric MRI scan of a glioblastoma patient.
  • To create a two-snapshot scenario using an atlas as a substitute for a pretumor scan, enabling parameter extraction from limited patient data.
  • To enhance the accuracy and reliability of patient-specific tumor growth parameter estimation.

Main Methods:

  • Utilized a PDE-constrained optimization framework and a modified Picard-iteration-type solution strategy.
  • Combined image-registration and parameter estimation algorithms, simulating tumor growth in the patient brain domain.
  • Incorporated an L1 sparsity constraint on the initial tumor condition and solved sub-problems using a reduced space, inexact Gauss-Newton-Krylov/quasi-Newton method.

Main Results:

  • The novel scheme demonstrated more accurate and reliable reconstruction of tumor parameters compared to previous methods.
  • Simulating tumor growth directly in the patient brain domain yielded more meaningful patient-specific results.
  • The joint inversion scheme effectively integrated registration, parameter estimation, and sparsity constraints.

Conclusions:

  • The proposed method offers a robust approach for calibrating biophysical tumor growth models from single-time MRI scans.
  • This technique improves the accuracy of patient-specific glioblastoma modeling, potentially aiding in treatment strategy development.
  • The integration of atlas-based data and advanced computational methods provides a reliable framework for analyzing limited clinical data.