Generation of parametric image of regional myocardial blood flow using H(2)(15)O dynamic PET and a linear least-squares method

Abstract

Although a parametric image of myocardial blood flow (MBF) can be obtained from H(2)(15)O PET using factor and cluster analysis, this approach is limited when factor analysis fails to extract each cardiac component. In this study, a linear least-squares (LLS) method for estimating MBF and generating a MBF parametric image was developed to overcome this limitation. The computer simulation was performed to investigate the statistical properties of the LLS method, and MBF values obtained from the MBF parametric images in dogs were compared with those obtained using the conventional region of interest (ROI) and invasive microsphere methods. METHODS: A differential model equation for H(2)(15)O in the myocardium was modified to incorporate the partial-volume and spillover effect. The equation was integrated from time 0 to each PET sampling point to obtain a linearlized H(2)(15)O model equation. The LLS solution of this equation was estimated and used to calculate the MBF, the perfusable tissue fraction (PTF), and the arterial blood volume fraction (V(a)). A computer simulation was performed using the input function obtained from canine experiments and the tissue time-activity curves contaminated by various levels of Poisson noise. The parametric image of the MBF, PTF, and V(a) was constructed using the PET data from dogs (n = 7) at rest and after pharmacologic stress. The regional MBF from the parametric image was compared with those produced by the ROI method using a nonlinear least-squares (NLS) estimation and an invasive radiolabeled microsphere technique. RESULTS: The simulation study showed that the LLS method was better than the NLS method in terms of statistical reliability, and the parametric images of the MBF, PTF, and V(a) using the LLS method had good image quality and contrast. The regional MBF values using the parametric image showed a good correlation with those using the ROI method (y = 0.84x + 0.40; r = 0.99) and the microsphere technique (y = 0.95x + 0.29; r = 0.96). The computation time was approximately 10 s for the 32 x 32 x 6 x18 (pixel x pixel x plane x frame) matrix. CONCLUSION: A noninvasive, very fast, and accurate method for estimating the MBF and generating a MBF parametric image was developed using the LLS estimation technique and H(2)(15)O dynamic myocardial PET.