Abstract
PURPOSE: This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)-based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules. METHODS: A National Electrical Manufacturers Association (NEMA) image-quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub-centimeter (<10 mm, n = 7) group and medium size (10-30 mm, n = 16) group. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers. RESULTS: With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values (P < 0.001), although the sub-centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion-size groups before the PVC (P < 0.001 for all). After the PVC, SUVpeak measured from the sub-centimeter group was no significantly different among different beta values (P = 0.830). CONCLUSION: Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.