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Proceedings Paper

Parametric myocardial perfusion PET imaging using physiological clustering
Author(s): Hassan Mohy-ud-Din; Nikolaos A. Karakatsanis; Martin A. Lodge; Jing Tang; Arman Rahmim
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Paper Abstract

We propose a novel framework of robust kinetic parameter estimation applied to absolute ow quanti cation in dynamic PET imaging. Kinetic parameter estimation is formulated as a nonlinear least squares with spatial constraints problem (NLLS-SC) where the spatial constraints are computed from a physiologically driven clustering of dynamic images, and used to reduce noise contamination. An ideal clustering of dynamic images depends on the underlying physiology of functional regions, and in turn, physiological processes are quanti ed by kinetic parameter estimation. Physiologically driven clustering of dynamic images is performed using a clustering algorithm (e.g. K-means, Spectral Clustering etc) with Kinetic modeling in an iterative handshaking fashion. This gives a map of labels where each functionally homogenous cluster is represented by mean kinetics (cluster centroid). Parametric images are acquired by solving the NLLS-SC problem for each voxel which penalizes spatial variations from its mean kinetics. This substantially reduces noise in the estimation process for each voxel by utilizing kinetic information from physiologically similar voxels (cluster members). Resolution degradation is also substantially minimized as no spatial smoothing between heterogeneous functional regions is performed. The proposed framework is shown to improve the quantitative accuracy of Myocardial Perfusion (MP) PET imaging, and in turn, has the long-term potential to enhance capabilities of MP PET in the detection, staging and management of coronary artery disease.

Paper Details

Date Published: 13 March 2014
PDF: 11 pages
Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90380P (13 March 2014); doi: 10.1117/12.2043947
Show Author Affiliations
Hassan Mohy-ud-Din, Johns Hopkins Univ. (United States)
Nikolaos A. Karakatsanis, Johns Hopkins Univ. (United States)
Martin A. Lodge, Johns Hopkins Univ. (United States)
Jing Tang, Oakland Univ. (United States)
Arman Rahmim, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 9038:
Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

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