Improved image reconstruction of Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm.

Purpose: The aim of this study was to evaluate the use of a Bayesian penalized likelihood reconstruction algorithm (Q.Clear) for Zr-immunoPET image reconstruction and its potential to improve image quality and reduce the administered activity of Zr-immunoPET tracers.

Methods: Eight Zr-immunoPET whole-body PET/CT scans from three Zr-immunoPET clinical trials were selected for analysis. On average, patients were imaged 6.3 days (range 5.0-8.0 days) after administration of 69 MBq (range 65-76 MBq) of [Zr]Zr-DFO-daratumumab, [Zr]Zr-DFO-pertuzumab, or [Zr]Zr-DFO-trastuzumab. List-mode PET data was retrospectively reconstructed using Q.Clear with incremental β-values from 150 to 7200, as well as standard ordered-subset expectation maximization (OSEM) reconstruction (2-iterations, 16-subsets, a 6.4-mm Gaussian transaxial filter, "heavy" z-axis filtering and all manufacturers' corrections active). Reduced activities were simulated by discarding 50% and 75% of original counts in each list mode stream. All reconstructed PET images were scored for image quality and lesion detectability using a 5-point scale. SUV for normal liver and sites of disease and liver signal-to-noise ratio were measured.

Results: Q.Clear reconstructions with β = 3600 provided the highest scores for image quality. Images reconstructed with β-values of 3600 or 5200 using only 50% or 25% of the original counts provided comparable or better image quality scores than standard OSEM reconstruction images using 100% of counts.

Conclusion: The Bayesian penalized likelihood reconstruction algorithm Q.Clear improved the quality of Zr-immunoPET images. This could be used in future studies to improve image quality and/or decrease the administered activity of Zr-immunoPET tracers.