Corpus ID: 235417574

Ensemble inversion for brain tumor growth models with mass effect

@article{Subramanian2021EnsembleIF,
  title={Ensemble inversion for brain tumor growth models with mass effect},
  author={Shashank Subramanian and Klaudius Scheufele and Naveen Himthani and Christos Davatzikos and George Biros},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.06016}
}
We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single… Expand

References

SHOWING 1-10 OF 26 REFERENCES
Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect
TLDR
A novel inversion scheme is introduced that uses multiple brain atlases as proxies for the healthy precancer patient brain resulting in robust and reliable parameter estimation and provides both global and local quantitative measures of tumor infiltration and mass effect. Expand
Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan
TLDR
A fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers is presented. Expand
Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain
TLDR
This article proposes a framework for modeling glioma growth and the subsequent mechanical impact on the surrounding brain tissue (mass-effect) in a medical imaging context and shows that model parameters can be estimated via optimization based on imaging data, using efficient solution algorithms on regular grids. Expand
WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS.
TLDR
A biophysically motivated regularization on the structure and magnitude of the tumor initial condition is introduced and the resulting optimization problem is solved using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Expand
An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects
TLDR
The two main goals are to improve the deformable registration from images of brain tumor patients to a common stereotactic space, thereby assisting in the construction of statistical anatomical atlases and to develop predictive capabilities for glioma growth, after the model parameters are estimated for a given patient. Expand
GLISTR: Glioma Image Segmentation and Registration
We present a generative approach for simultaneously registering a probabilistic atlas of a healthy population to brain magnetic resonance (MR) scans showing glioma and segmenting the scans into tumorExpand
Quantification of glioblastoma mass effect by lateral ventricle displacement
Mass effect has demonstrated prognostic significance for glioblastoma, but is poorly quantified. Here we define and characterize a novel neuroimaging parameter, lateral ventricle displacement (LVd),Expand
Simulation of glioblastoma growth using a 3D multispecies tumor model with mass effect
TLDR
A multispecies reaction–advection–diffusion partial differential equation coupled with linear elasticity for modeling tumor growth aims to capture the phenomenological features of glioblastoma multiforme observed in magnetic resonance imaging (MRI) scans. Expand
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
TLDR
This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks. Expand
Personalized Radiotherapy Planning for Glioma Using Multimodal Bayesian Model Calibration
TLDR
The proposed integration of data and models provides a robust, non-invasive tool to assist personalized RT planning and relies on data from a single time point and thus is applicable to standard clinical settings. Expand
...
1
2
3
...