PET Lesion Quantitation Noise Estimates from Sub-Scan Data

Abstract

The use of Positron Emission Tomography (PET) has been suggested as a tool for quantitative biological measurement to determine outcomes of therapy, diagnosis, and novel drugs, through measures of tumor change from repeated PET scans. However, inherent variability (due to technical and biological effects) results in different measurements even where there is no change in the tumor. This study evaluates the random component of variability due to the limited number of counts acquired in the scans. We have proposed using PET raw list mode data as a way to determine the variability associated with the scanner, including the nonlinear processes like the max standard uptake value (SUVmax) and iterative reconstruction processes. PET simulation (digitally simulated oval phantom), PET list mode whole body (WB) and tapering phantom (TP) data in addition to clinical data (prostate cancer patients) were used to divide a larger acquisition into sequentially smaller half scan durations to compare their variabilities with the ideal Poisson variability in a PET system. Poisson statistics predicts that variability decreases as 1/sqrt(n) of the number of counts (or scan duration).The WB phantom contained 21 spheres (six 3 cm, six 2 cm, nine 1 cm), the TP contained spheres (thirty-two 1 cm spheres and twenty-four 2 cm spheres) distributed over four levels. Simulated data was used as an ideal scenario with larger statistical power, and showed excellent agreement (<10%) with its Poisson calculation using 4 mmof smoothing. Through the use of simulation and phantom data variability among measurements using SUVmax have been shown. This data has demonstrated that maximum ROI methodology on iteratively reconstructed images retains the Poisson nature of PET coincidence counts in spite of the potential nonlinearities of both the reconstruction method and the ROI methodology.