Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping

Abstract

Purpose Hybrid dynamic imaging allows not only the estimation of whole-body (WB) macroparametric maps but also the estimation of microparameters in the initial bed position targeting the blood pool region containing the pathology owing to the limited axial field of view of PET scanners. In this work, we assessed the capability of multipass WB 18F-FDG PET parametric imaging in terms of lesion detectability through qualitative and quantitative evaluation of simulation and clinical studies. Methods Simulation studies were conducted by generating data incorporating 3 liver and 3 lung lesions produced by 3 noise levels and 20 noise realizations for each noise level to estimate bias and lesion detection features. The total scan time for the clinical studies of 8 patients addressed for lung and liver lesions staging, including dynamic and static WB imaging, lasted 80 minutes. An in-house–developed MATLAB code was utilized to derive the microparametric and macroparametric maps. We compared lesion detectability and different image-derived PET metrics including the SUVs, Patlak-derived influx rate constant (K i) and distribution volume (V) and K1, k2, k3, blood volume (bv) microparameters, and K i estimated using the generalized linear least square approach. Results In total, 104 lesions were detected, among which 47 were located in the targeted blood pool bed position where all quantitative parameters were calculated, thus enabling comparative analysis across all parameters. The evaluation encompassed visual interpretation performed by an expert nuclear medicine specialist and quantitative analysis. High correlation coefficients were observed between SUVmax and K imax derived from the generalized linear least square approach, as well as K i generated by Patlak graphical analysis. Moreover, 3 contrast-enhanced CT-proven malignant lesions located in the liver and a biopsy-proven malignant liver lesion not visible on static SUV images and Patlak maps were clearly pinpointed on K1 and k2 maps. Conclusions Our results demonstrate that full compartmental modeling for the region containing the pathology has the potential of providing complementary information and, in some cases, more accurate diagnosis than conventional static SUV imaging, favorably comparing to Patlak graphical analysis.

Short-duration dynamic FDG PET imaging: Optimization and clinical application

“…In our study, full kinetic analysis can be performed for a single bed while whole-body kinetic analysis can be performed by applying the Patlak model in the rest of the bed positions. Combining Patlak and full compartmental analysis in a predefined pathology-focused single-bed FOV was reported recently in a scanner with larger axial FOV and continuous bed motion [29][30][31]. We add that our clinical study was performed for the ECAT EXACT HR + scanner, and different performances for shortened protocols might be obtained on newer, higher-sensitivity scanners.…”

Generation of parametric Ki images for FDG PET using two 5‐min scans

“…K i has been reported to outperform SUV using receiver operating characteristic analysis, in differentiating benign from malignant solitary pulmonary nodules, 6,7 and also in measuring response to breast cancer therapy using patient studies 8 and a virtual clinical trial 9 . K i can also complement SUV to enhance quantification and lesion detectability as it has been demonstrated with whole‐body imaging in clinical oncology studies 10–13 . However, full kinetic modeling of FDG using the dynamic images requires a long (≥60 min) acquisition protocol and sequential arterial blood sampling (or image‐derived blood activity) to derive an input function starting from the time of injection.…”

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques

“…Alternatively, dedicated protocols for whole-body dynamic imaging have been devised to enable the estimation of macro-or microkinetic parameters through graphical Patlak analysis or higher-order kinetic modeling techniques (39). These protocols have not only improved lesion detectability but also enhanced the quantitative capabilities of PET (40). AI and ML/DL may make significant contributions in all of these areas, as discussed in the next section.…”