Abstract
Linear models such as factor analysis, independent component analysis (ICA), and nonnegative matrix factorization (NMF) were successfully applied to dynamic myocardial \(H_{2}^{15}O\) PET image data, showing that meaningful factor images and appropriate time activity curves were estimated for the quantification of myocardial blood flow. In this paper we apply the ensemble ICA to dynamic myocardial \(H_{2}^{15}O\) PET image data. The benefit of the ensemble ICA (or Bayesian ICA) in such a task is to decompose the image data into a linear sum of independent components as in ICA, with imposing the nonnegativity constraints on basis vectors as well as encoding variables, through the rectified Gaussian prior. We show that major cardiac components are separated successfully by the ensemble ICA method and blood flow could be estimated in 15 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 images, the image contrast between left ventricle and myocardium was 1:2.7 in average.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Lee, B.I., Lee, J.S., Lee, D.S., Choi, S. (2005). Myocardial Blood Flow Quantification in Dynamic PET: An Ensemble ICA Approach. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_113
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DOI: https://doi.org/10.1007/11550907_113
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