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Proceedings Paper

A new deconvolution approach to perfusion imaging exploiting spatial correlation
Author(s): Burkay B. Orten; W. Clem Karl; Dushyant V. Sahani; Homer Pien
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Paper Abstract

The parts of the human body affected by a disease do not only undergo structural changes but also demonstrate significant physiological (functional) abnormalities. An important parameter that reveals the functional state of tissue is the flow of blood per unit tissue volume or perfusion, which can be obtained using dynamic imaging methods. One mathematical approach widely used for estimating perfusion from dynamic imaging data is based on a convolutional tissue-flow model. In these approaches, deconvolution of the observed data is necessary to obtain the important physiological parameters within a voxel. Although several alternatives have been proposed for deconvolution, all of them treat neighboring voxels independently and do not exploit the spatial correlation between voxels or the temporal correlation within a voxel over time. These simplistic approaches result in a noisy perfusion map with poorly defined region boundaries. In this paper, we propose a novel perfusion estimation method which incorporates spatial as well as temporal correlation into the deconvolution process. Performance of our method is compared to standard methods using independent voxel processing. Both simulated and real data experiments illustrate the potential of our method.

Paper Details

Date Published: 12 March 2008
PDF: 8 pages
Proc. SPIE 6916, Medical Imaging 2008: Physiology, Function, and Structure from Medical Images, 69160M (12 March 2008); doi: 10.1117/12.770425
Show Author Affiliations
Burkay B. Orten, Boston Univ. (United States)
W. Clem Karl, Boston Univ. (United States)
Dushyant V. Sahani, Massachusetts General Hospital (United States)
Homer Pien, Massachusetts General Hospital (United States)

Published in SPIE Proceedings Vol. 6916:
Medical Imaging 2008: Physiology, Function, and Structure from Medical Images
Xiaoping P. Hu; Anne V. Clough, Editor(s)

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