TY - GEN
T1 - Parametric myocardial perfusion PET imaging using physiological clustering
AU - Mohy-Ud-Din, Hassan
AU - Karakatsanis, Nikolaos A.
AU - Lodge, Martin A.
AU - Tang, Jing
AU - Rahmim, Arman
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Coronary artery disease
KW - Coronary artery stenosis
KW - Coronary flow reserve
KW - K-means clustering
KW - Myocardial perfusion
KW - PET
KW - Penalized least squares
KW - Physiological clustering
KW - Spectral clustering
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U2 - 10.1117/12.2043947
DO - 10.1117/12.2043947
M3 - Conference contribution
AN - SCOPUS:84901794804
SN - 9780819498311
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2014
PB - SPIE
T2 - Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging
Y2 - 16 February 2014 through 18 February 2014
ER -