How Do the More Recent Reconstruction Algorithms Affect the Interpretation Criteria of PET/CT Images?

• Published in: Nuclear medicine and molecular imaging Vol. 53; no. 3; pp. 216 - 222
• Main Authors: Matti, Antonella, Lima, Giacomo Maria, Pettinato, Cinzia, Pietrobon, Francesca, Martinelli, Felice, Fanti, Stefano
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-06-2019; Springer Nature B.V; 대한핵의학회
• Subjects: Algorithms; Background noise; Bayesian analysis; Cardiology; Computed tomography; Image quality; Image resolution; Imaging; Iterative algorithms; Medical imaging; Medicine; Medicine & Public Health; Nuclear Medicine; Oncology; Original; Original Article; Orthopedics; Positron emission; Quantitative analysis; Radiology; Reconstruction; Sensitivity; Signal to noise ratio; Tomography; 방사선과학; OSEM; Reconstruction algorithm; 18FDG-PET; Q.Clear; BPL reconstruction algorithm; PET/CT
• ISSN: 1869-3474; 1869-3482
• DOI: 10.1007/s13139-019-00594-x

Purpose
Recently, a new Bayesian Penalized Likelihood (BPL) Reconstruction Algorithm was introduced by GE Healthcare, Q.Clear; it promises to provide better PET image resolution compared to the widely used Ordered Subset Expectation Maximization (OSEM). The aim of this study is to compare the performance of these two algorithms on several types of findings, in terms of image quality, lesion detectability, sensitivity, and specificity.
Methods
Between September 6th 2017 and July 31st 2018, 663 whole body 18F-FDG PET/CT scans were performed at the Nuclear Medicine Department of S. Martino Hospital (Belluno, Italy). Based on the availability of clinical/radiological follow-up data, 240 scans were retrospectively reviewed. For each scan, a hypermetabolic finding was selected, reporting both for OSEM and Q.Clear: SUVmax and SUVmean values of the finding, the liver and the background close to the finding; size of the finding; percentage variations of SUVmax and SUVmean. Each finding was subsequently correlated with clinical and radiological follow-up, to define its benign/malignant nature.
Results
Overall, Q.Clear improved the SUV values in each scan, especially in small findings (< 10 mm), high SUVmax values (≥ 10), and medium/low backgrounds. Furthermore, Q.Clear amplifies the signal of hypermetabolic findings without modifying the background signal, which leads to an increase in signal-to-noise ratio, improving overall image quality. Finally, Q.Clear did not affect PET sensitivity or specificity, in terms of number of reported findings and characterization of their nature.
Conclusions
Q.Clear is an iterative algorithm that improves significantly the quality of PET images compared to OSEM, increasing the SUVmax of findings (in particular for small findings) and the signal-to-noise ratio. However, due to the intrinsic characteristics of this algorithm, it will be necessary to adapt and/or modify the current interpretative criteria based of quantitative evaluation, to avoid an overestimation of the disease burden.