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PLoS One. 2014 Jan 8;9(1):e84764. doi: 10.1371/journal.pone.0084764. eCollection 2014.

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

Author information

1
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, United States of America.
2
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.
3
Department of Diagnostic Imaging, Rhode Island Hospital, Providence, Rhode Island, United States of America ; Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island, United States of America.
4
CSIRO Mathematical and Information Sciences, Clayton South, Victoria, Australia.
5
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.
6
Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America ; Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee, United States of America.

Abstract

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.

PMID:
24416281
PMCID:
PMC3885618
DOI:
10.1371/journal.pone.0084764
[Indexed for MEDLINE]
Free PMC Article

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