Search

Positron Emission Tomography (PET) imaging after 90 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 90 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 β 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 β = 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 β parameter lowered RC, COV and BV, while CNR was maximized at β = 4000; further increase resulted in oversmoothing. For quantification purposes, β = 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 β 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., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

The introduction of digital radiography has improved image acquisition. However, rejection of images remains a matter of concern. Reject analysis is part of the quality assurance program in radiology and helps identify potential errors or lack of training. A retrospective study was conducted at the radiology department of a university hospital. The reject rate was calculated both using the number of examinations, $r_n$, and the dose-area product, $r_d$. A reject rate $r_n$ of 3.3% for paediatric units and 4.5% for adults was found. The corresponding values of rd were 4.4 and 8.4%, respectively. The main rejection cause was patient motion, being 50.2% of rejected examinations in adults and 63.7% in children. The contribution of exposure errors was minor, as expected in digital radiography units. A discrepancy between reject rates $r_n$ and $r_d$ was observed, suggesting dosimetric quantities could be considered in reject analysis for further assessment of patient radiation burden., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)