An efficient computational approach to characterize DSC-MRI signals arising from three-dimensional heterogeneous tissue structures.

Semmineh NB, Xu J, Boxerman JL, Delaney GW, Cleary PW, Gore JC, Quarles CC
PLoS One. 2014 9 (1): e84764

PMID: 24416281 · PMCID: PMC3885618 · DOI:10.1371/journal.pone.0084764

The systematic investigation of susceptibility-induced contrast in MRI is important to better interpret the influence of microvascular and microcellular morphology on DSC-MRI derived perfusion data. Recently, a novel computational approach called the Finite Perturber Method (FPM), which enables the study of susceptibility-induced contrast in MRI arising from arbitrary microvascular morphologies in 3D has been developed. However, the FPM has lower efficiency in simulating water diffusion especially for complex tissues. In this work, an improved computational approach that combines the FPM with a matrix-based finite difference method (FDM), which we call the Finite Perturber the Finite Difference Method (FPFDM), has been developed in order to efficiently investigate the influence of vascular and extravascular morphological features on susceptibility-induced transverse relaxation. The current work provides a framework for better interpreting how DSC-MRI data depend on various phenomena, including contrast agent leakage in cancerous tissues and water diffusion rates. In addition, we illustrate using simulated and micro-CT extracted tissue structures the improved FPFDM along with its potential applications and limitations.

MeSH Terms (10)

Algorithms Blood Vessels Brain Neoplasms Contrast Media Extravasation of Diagnostic and Therapeutic Materials Imaging, Three-Dimensional Kinetics Magnetic Resonance Imaging Models, Biological Reproducibility of Results

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