Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm

Positron Emission Tomography (PET) imaging after $$ Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for $$ Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the $$ penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with $$ = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing $$ parameter lowered RC, COV and BV, while CNR was maximized at $$ = 4000; further increase resulted in oversmoothing. For quantification purposes, $$ = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A $$ of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use.