Optimization of the injected activity in dynamic 3D PET: a generalized approach using patient-specific NECs as demonstrated by a series of 15O-H2O scans.

摘要

The magnitude of the injected activity (A(0)) has a direct impact on the statistical quality of PET images. This study aimed to develop a generalized method for maximizing the statistical quality of dynamic PET images by optimizing A(0). METHODS: Patient-specific noise-equivalent counts (PS-NECs) were used as a metric of the statistical quality of each time frame of a dynamic PET image. Previous methodology developed to extrapolate the NEC as a function of A(0) was extended to dynamic PET, enabling the NEC to be extrapolated as a function of both A(0) and the time after injection. This method allowed A(0) to be optimized after a single scan (at a single A(0)), by maximizing the NEC within the time interval for which the parameter estimation is most sensitive. The extrapolation method was validated by a series of (15)O-H(2)O scans of the body acquired in 3-dimensional mode. Each patient (n = 6) underwent between 3 and 6 scans at 1 bed position. The injected activities were varied over a wide range (140-840 MBq). Noise-equivalent counting rate (NECR) versus A(0) curves and the optimal injected activities were calculated from each injection. RESULTS: PS-NECR versus A(0) curves as extrapolated from different injected activities were consistent (coefficient of variation, typically <5%). The optimal injected activities for an individual, as derived from these curves, were also consistent (maximum coefficient of variation, 4.3%). For abdominal (n = 4) and chest (n = 1) scans, we found optimal injected activities of (15)O-H(2)O in the range of 220-350 MBq for estimating blood perfusion (F) and 660-1,070 MBq for estimating the volume of distribution (V(T)). Higher optimal injected activities were found in the case of a pelvic scan (n = 1; 570 MBq for F and 1,530 MBq for V(T)). CONCLUSION: PS-NECs are a valid and generic method for optimizing the injected activity in PET, allowing scanning protocols to be improved after the collection of an initial, single dynamic dataset. This generic method can be used to estimate the optimal injected activity, which is specific to the patient, tracer, PET scanner, and body region being scanned.