Quantification of myocardial perfusion in human subjects using 82Rb and wavelet-based noise reduction.

Abstract

Quantification of myocardial perfusion with 82Rb has been difficult to achieve because of the low signal-to-noise ratio of the dynamic data curves. This study evaluated the accuracy of flow estimates after the application of a novel multidimensional wavelet-based noise-reduction protocol. Myocardial perfusion was estimated using 82Rb and a two-compartment model from dynamic PET scans on 11 healthy volunteers at rest and after hyperemic stress with dipyridamole. Midventricular planes were divided into eight regions of interest, and a wavelet transform protocol was applied to images and time-activity curves. Flow estimates without and with the wavelet approach were compared with those obtained using H2(15)O. Over a wide flow range (0.45-2.75 mL/g/min), flow achieved with the wavelet approach correlated extremely closely with values obtained with H2(15)O (y = 1.03 x -0.12; n = 23 studies, r = 0.94, P < 0.001). If the wavelet noise-reduction technique was not used, the correlation was less strong (y = 1.11 x + 0.24; n = 23 studies, r = 0.79, P < 0.001). In addition, the wavelet approach reduced the regional variation from 75% to 12% and from 62% to 11% (P < 0.001 for each comparison) for resting and stress studies, respectively. The use of a wavelet protocol allows near-optimal noise reduction, markedly enhances the physiologic flow signal within the PET images, and enables accurate measurement of myocardial perfusion with 82Rb in human subjects over a wide range of flows.