Development of Quantification Methods for the Myocardial Blood Flow Using Ensemble Independent Component Analysis for Dynamic H215O PET.
- Author:
Byeong Il LEE
1
;
Jae Sung LEE
;
Dong Soo LEE
;
Won Jun KANG
;
Jong Jin LEE
;
Soo Jin KIM
;
Seung Jin CHOI
;
June Key CHUNG
;
Myung Chul LEE
Author Information
1. Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Korea. dsl@plaza.snu.ac.kr
- Publication Type:Original Article
- Keywords:
Dynamic H215O PET;
ensemble ICA;
blood flow
- MeSH:
Angiography;
Bayes Theorem;
Constriction, Pathologic;
Coronary Angiography;
Heart Ventricles;
Humans;
Learning;
Myocardium;
Nuclear Medicine;
Perfusion;
Positron-Emission Tomography;
Regional Blood Flow;
Tomography, Emission-Computed, Single-Photon
- From:Korean Journal of Nuclear Medicine
2004;38(6):486-491
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: Factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial H215O PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. MATERIALS AND METHODS: Twenty subjects underwent H215O PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of 555~740 MBq H215O. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. RESULTS: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was 1.2 +/- 0.40 ml/min/g in rest, 1.85 +/- 1.12 ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1: 2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P< 0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in H215O PET. CONCLUSIONS: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.
