Theoretical study of the benefit of long axial field-of-view PET on region of interest quantification

The aim of this study is to evaluate the benefit of long axial field-of-view (AFOV) PET scanners on region of interest (ROI) quantification. We simulated a series of PET scanners with an AFOV ranging from 22 cm to 220 cm. A theoretical framework was used to predict the contrast recovery coefficient (CRC) and the variance of ROI quantification in penalized maximum likelihood (ML) image reconstruction, in which the resolution and noise tradeoff was controlled by a regularization parameter with a quadratic penalty function. The characterization was based on the converged penalized ML reconstruction with an accurate system model. We examined quantification of a 2 mm ROI and 10 mm ROI in a clinically relevant scan range of 110 cm. Multiple bed positions with 50% overlap were used for scanners with shorter AFOV to provide a relatively uniform sensitivity across the 110 cm axial range. A uniform water cylinder of 20 cm in diameter and 230 cm in length was chosen to model the attenuation and background activity. We computed the variance reduction factor at fixed resolution. Effects of different detector capabilities, including TOF (time-of-flight) resolution (320 ps, 500 ps, and non-TOF) and DOI (depth-of-interaction) resolution (4 mm, 10 mm, and no DOI), were evaluated. The results show that at a normal activity level (370 MBq), the 220 cm AFOV scanner offers a ∼17-fold variance reduction for the 2 mm ROI and ∼26-fold variance reduction for the 10 mm ROI (both measured at CRC = 0.5) over the 22 cm AFOV scanner when both using detectors with 500 ps TOF resolution no DOI capability. The variance reduction factors of trues-only are higher than those of including scatters and randoms. Combining 320 ps TOF and 4 mm DOI, the 220 cm long scanner offers a ∼45-fold variance reduction over the 22 cm long reference scanner (500 ps TOF, no DOI) for imaging 2 mm and 10 mm ROIs. The variance reduction factors are higher at a lower activity level due to lower random fraction. In conclusion, our study demonstrates that a long AFOV scanner can greatly improve the quantitative accuracy of PET imaging compared to current state-of-the-art clinical PET scanners.