Abstract #0124
A Monte Carlo simulation framework for histology-informed diffusion MRI parameter estimation in cancer
Athanasios Grigoriou1,2, Anna Voronova1,2, Kinga Bernatowicz1, Sara Simonetti3,4, Garazi Serna3, Núria Roson5,6, Manuel Escobar5,6, Maria Vieito7,8, Paolo Nuciforo3, Rodrigo Toledo9, Elena Garralda10, Roser Sala-Llonch11,12, Marco Palombo13,14, Raquel Perez-Lopez1, and Francesco Grussu1
1Radiomics Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 2Department of Biomedicine, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain, 3Molecular Oncology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain, 4Prostate Cancer Translational Research Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 5Institut de Diagnòstic per la Imatge (IDI), Barcelona, Spain, 6Department of Radiology, Hospital Universitari Vall d’Hebron, Barcelona, Spain, 7GU, Sarcoma and Neuroncology Unit, Hospital Universitari Vall d’Hebron, Barcelona, Spain, 8Drug Development Unit, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 9Biomarkers and Clonal dynamics group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 10Early Clinical Drug Development Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 11Department of Biomedicine, Faculty of Medicine, Institute of Neurosciences, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, 12Centro de Investigación Biomédica en Red de Bioingenierı́a, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain, 13Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, 14School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
Synopsis
Keywords: Simulation/Validation, Microstructure, Monte-Carlo, Histology
Motivation: Analytical biophysical diffusion MRI (dMRI) models fail to capture the full complexity
of diffusion processes.
Goal(s): We propose a Monte Carlo (MC) simulation framework enabling the numerical implementation
of biophysical models with unprecedented fidelity to histology.
Approach: Our framework enables simulating diffusion within cancer environments reconstructed
from histology. It provides paired examples of dMRI signals and histological properties, which can be
used to build numerical microstructure parameter estimators.
Results: Our approach enables more accurate estimation of key properties such as cell size compared
to fitting of classical multi-compartment analytical models.
Impact: We propose a Monte Carlo (MC) simulation framework enabling the implementation of biophysicalmodels with unprecedented fidelity to histology. The framework improves microstructure inference compared to standard analytical fitting, and may provide more robust biomarkers in diseases such ascancer.
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