Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer

Abstract

We compared the image quality and quantification parameters through bayesian penalized likelihood reconstruction algorithm (Q.Clear) and ordered subset expectation maximization (OSEM) algorithm for 2-[18F]FDG-PET/CT scans performed for response monitoring in patients with metastatic breast cancer in prospective setting. We included 37 metastatic breast cancer patients diagnosed and monitored with 2-[18F]FDG-PET/CT at Odense University Hospital (Denmark). A total of 100 scans were analyzed blinded toward Q.Clear and OSEM reconstruction algorithms regarding image quality parameters (noise, sharpness, contrast, diagnostic confidence, artefacts, and blotchy appearance) using a five-point scale. The hottest lesion was selected in scans with measurable disease, considering the same volume of interest in both reconstruction methods. SULpeak (g/mL) and SUVmax (g/mL) were compared for the same hottest lesion. There was no significant difference regarding noise, diagnostic confidence, and artefacts within reconstruction methods; Q.Clear had significantly better sharpness (p < 0.001) and contrast (p = 0.001) than the OSEM reconstruction, while the OSEM reconstruction had significantly less blotchy appearance compared with Q.Clear reconstruction (p < 0.001). Quantitative analysis on 75/100 scans indicated that Q.Clear reconstruction had significantly higher SULpeak (5.33 ± 2.8 vs. 4.85 ± 2.5, p < 0.001) and SUVmax (8.27 ± 4.8 vs. 6.90 ± 3.8, p < 0.001) compared with OSEM reconstruction. In conclusion, Q.Clear reconstruction revealed better sharpness, better contrast, higher SUVmax, and higher SULpeak, while OSEM reconstruction had less blotchy appearance.

The Pattern of Metastatic Breast Cancer: A Prospective Head-to-Head Comparison of [18F]FDG-PET/CT and CE-CT

“…Clear reconstruction methods were employed. While both methods have demonstrated nearly identical clinical accuracy [26], it could have been better to use the Q.Clear reconstruction algorithm in the evaluation of all scans with the potential for smaller lesion detection [27]. Finally, the research objectives differ from the lesion-based objectives listed on ClinicalTrials.gov due to the lack of a proper reference standard for all lesions.…”