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Proceedings Paper

High-resolution atheroma mixture-modeled MRI
Author(s): Justin D. Pearlman; Vivek V. Sukhatme
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Paper Abstract

Introduction: In chemically sensitive MRI, similar signals from the atheroma that blocks arteries and from perivascular fat, due to partial volume effects, can result in a mixed signal. Methods: MRI of pure samples and of blood vessels containing 10 different ratios of atheroma and perivascular fat were acquired by inversion recovery MRI. Mixture modeling deduced component signals, and distribution analysis enabled conversion of dynamic range to enhanced resolution. Imaging was repeated at high resolution (long acquisition) for validation of resolution enhancement. Monte Carlo methods were applied to examine error propagation. Quantitation of atheroma content corresponds accurately to measured samples (r equals .997). Results: Mixture modeling correctly identified components with rms error less than 5%. Multiresolution imaging confirmed that redistribution converts dynamic range to enhanced spatial resolution. The resolution enhancement agrees well with direct high-resolution acquisition, achieving 256x improvement in effective in-plane resolution. Conclusion. Mixture modeling correctly identifies atheroma vs. perivascular fat signal components, and resolution-enhancement converts the results to high spatial resolution maps of the components. The resolution-enhanced images agree well with the true high-resolution acquisition. They are faster to acquire, more practical, and provide better tissue characterization, including quantitation of the atheroma lipid burden in the vessel.

Paper Details

Date Published: 14 May 1998
PDF: 10 pages
Proc. SPIE 3298, Visual Data Exploration and Analysis V, (14 May 1998); doi: 10.1117/12.309545
Show Author Affiliations
Justin D. Pearlman, Beth Israel Deaconess Medical Ctr./Harvard Medical School (United States)
Vivek V. Sukhatme, Beth Israel Deaconess Medical Ctr./Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 3298:
Visual Data Exploration and Analysis V
Robert F. Erbacher; Alex Pang, Editor(s)

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