Study of the Effect of Reconstruction Parameters for Myocardial Perfusion Imaging in PET With a Novel Flow Phantom - Siekkinen, Reetta; Teuho, Jarmo; Smith, Nadia A. S.; Fenwick, Andrew; Kirjavainen, Anna K.; Koskensalo, Kalle; Saraste, Antti; Teräs, Mika (Frontiers in Physics, 2020)
Myocardial perfusion imaging (MPI) with positron emission tomography (PET) allows quantitative temporal measurements of the radioactive tracer distribution in tissue. The quantification for myocardial blood flow (MBF) is conducted with kinetic modeling of the image-derived time-activity curves (TACs) allowing derivation for MBF in units of mL/min per gram of tissue. The ordered-subset expectation maximization (OSEM) reconstruction algorithm with time-of-flight (TOF) and point spread function (PSF) modeling is now routinely employed in cardiac imaging. However, the varying counting statistics of the MPI measurements conducted with short-lived tracers present a challenge for the PET image reconstruction methods. Thus, the effect of the reconstruction methods on the flow quantification needs to be evaluated in a standardized manner. Recently, a novel PET flow phantom modeling the MBF has been developed for investigation of the standardization of the MBF measurements. In this study, the effect of the reconstruction parameters on the image-derived flow values against a known reference flow of the flow phantom was studied with [15O]H2O. The effects were studied by comparison of TACs and relative errors of the image-derived flow values with respect to the phantom-derived reference flow value using 5 repeated PET scans with fixed acquisition parameters using a digital Discovery MI PET/CT system. The reconstruction methods applied were OSEM using both TOF and PSF (OSEM-TOF-PSF) with several matrix sizes (128 x 128, 192 x 192, 256 x 256, 384 x 384), Gaussian filter sizes (4, 8 mm) and OSEM without TOF and PSF (OSEM), with TOF (OSEM-TOF) and with PSF (OSEM-PSF) in addition to recently introduced regularized reconstruction method based on Bayesian-penalized maximum likelihood (Q.Clear). Between repeated measurements, the image-derived flow values showed high repeatability with a SD less than 2 mL/min as well as high accuracy with the maximum error of 7% with respect to the reference flow for all reconstructions. Overall, reconstruction settings had only a small impact on the resulting flow values. In conclusion, due to the small differences detected, any of the implemented reconstruction algorithms on the system can be applied in MPI studies for accurate flow quantification.