ArticleJournal of mathematical biology2016
An inverse problem formulation for parameter estimation of a reaction-diffusion model of low grade gliomas.
Article in Journal of mathematical biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Inverse Problem Regularization for 3D Multi-Species Tumor Growth Models.International journal for numerical methods in biomedical engineering · 2025Article
- Ensemble Inversion for Brain Tumor Growth Models With Mass Effect.IEEE transactions on medical imaging · 2023Article
- CLAIRE-Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications.Journal of imaging · 2022Article
- Partial Differential Equation-Constrained Diffeomorphic Registration from Sum of Squared Differences to Normalized Cross-Correlation, Normalized Gradient Fields, and Mutual Information: A Unifying Framework.Sensors (Basel, Switzerland) · 2022Article
- Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging.IEEE transactions on bio-medical engineering · 2021Article
- Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan.IEEE transactions on medical imaging · 2021Article
- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.Annual review of biomedical engineering · 2020Review
- WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS.Inverse problems · 2020Article
- IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2020Article
- Simulation of glioblastoma growth using a 3D multispecies tumor model with mass effect.Journal of mathematical biology · 2019Article
- Coupling brain-tumor biophysical models and diffeomorphic image registration.Computer methods in applied mechanics and engineering · 2019Article
- An adjoint-based method for a linear mechanically-coupled tumor model: Application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging.Computational mechanics · 2019Article
- CLAIRE: A DISTRIBUTED-MEMORY SOLVER FOR CONSTRAINED LARGE DEFORMATION DIFFEOMORPHIC IMAGE REGISTRATION.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2019Article
- A SEMI-LAGRANGIAN TWO-LEVEL PRECONDITIONED NEWTON-KRYLOV SOLVER FOR CONSTRAINED DIFFEOMORPHIC IMAGE REGISTRATION.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2017Article
- ConstrainedSIAM journal on imaging sciences · 2016Article
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Authors and funding
3 authors.
Funding
Abstract
We present a numerical scheme for solving a parameter estimation problem for a model of low-grade glioma growth. Our goal is to estimate the spatial distribution of tumor concentration, as well as the magnitude of anisotropic tumor diffusion. We use a constrained optimization formulation with a reaction-diffusion model that results in a system of nonlinear partial differential equations. In our formulation, we estimate the parameters using partially observed, noisy tumor concentration data at two different time instances, along with white matter fiber directions derived from diffusion tensor imaging. The optimization problem is solved with a Gauss-Newton reduced space algorithm. We present the formulation and outline the numerical algorithms for solving the resulting equations. We test the method using a synthetic dataset and compute the reconstruction error for different noise levels and detection thresholds for monofocal and multifocal test cases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.