Clinical assessment of lesion detectability in dynamic whole-body PET imaging using compartmental and Patlak parametric mapping

Abstract

Continuous Bed Motion (CBM) acquisition mode enables the simultaneous estimation of compartmental and graphical analysis kinetic parameters in whole-body (WB) dynamic PET imaging. Lesion detectability is one of the important characteristics when developing novel image acquisition protocols. In this work, we used clinical dynamic WB 18F-FDG PET/CT studies to compare lesion detectability between Patlak graphical analysis and full compartmental modeling derived using a previously proposed methodology for hybrid macro- and micro-parametric imaging. The performance of these approaches in the context of lesion detection and false positive identification was compared to conventional static standardized uptake value (SUV) imaging. This study was conducted on 8 clinical whole-body 18F-FDG PET/CT studies injected with 3.5 MBq/kg of 18F-FDG. The total scan time including dynamic WB imaging lasted 80 minutes. An in-house developed MATLAB code was utilized to derive the micro- and macro-parametric maps. In total, 104 lesions were detected, among which 47 located in the bed position where all quantitative parameters were calculated, thus enabling comparative analysis. The evaluation encompassed visual interpretation performed by expert physicians as well as quantitative and statistical analysis. High correlation coefficients were observed between SUVmax and Kimax derived from generalized linear least square (GLLS) approach as well as Ki generated by Patlak graphical analysis. Moreover, three biopsy-proven malignant lesions located in the liver were not visible on static SUV images and Patlak maps while they were depicted on K1 and k2 maps. Our results demonstrate that full compartmental modeling has the potential to provide complementary information and in some cases more accurate diagnosis than conventional static SUV imaging and even Patlak graphical analysis.