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Abstract #3248

Moving Beyond Single-Model Assumptions in DCE-MRI: Probabilistic Nested Model Selection for Enhanced Tumor Microvasculature Analysis

Hassan Bagher-Ebadian1,2,3,4, Stephen L. Brown1,2,3, Mohammad M. Ghassemi5, Prabhu C. Acharya3, Indrin J. Chetty3,6, James R Ewing2,3,7, Benjamin Movsas1,2, and Kundan Thind1,2,4
1Radiation Oncology, Henry Ford Health, Detroit, MI, United States, 2Radiology, Michigan State University, East Lansing, MI, United States, 3Physics, Oakland University, Rochester, MI, United States, 4Oncology, Wayne State University, Detroit, MI, United States, 5Computer Science and Engineering, Michigan State University, East Lansing, MI, United States, 6Radiation Oncology, Cedars Sinai Medical Center, Los Angles, CA, United States, 7Neurology, Henry Ford Health, Detroit, MI, United States

Synopsis

Keywords: Data Processing, DSC & DCE Perfusion, Dynamic Contrast Enhanced MRI, Wavelet Analysis, Nested Model Selection, Radiation Induced Effect, Signal Coherence, Signal Time-Frequency Analysis, Physiological Tissue Characterization

Motivation: The current nested model selection (NMS) method for pharmacokinetic analysis of Dynamic Contrast Enhanced (DCE) MRI data assumes a single physiological model per voxel, potentially overlooking mixed-model scenarios in complex tissues like tumors.

Goal(s): To develop a probabilistic NMS (PNMS) technique that estimates voxel-wise probabilities of multiple physiological models for more accurate microvasculature parameter estimation in DCE-MRI.

Approach: Using DCE-MRI data from a rat glioblastoma model, we employed a Kohonen Self-Organizing Map (K-SOM) to estimate model probabilities and compared them to conventional NMS technique.

Results: PNMS provided comparable accuracy, with faster computation and more reliable permeability estimates.

Impact: Probabilistic nested model selection has the potential to improve the accuracy of DCE-based microvasculature parameter estimation. It enhanced DCE-MRI based tumor microenvironmental information assessment, that may translate to more precise diagnoses, targeted therapies, and informed clinical decision-making for cancer patients.

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Keywords