Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System

Abstract

Q.Clear, a Bayesian penalized-likelihood reconstruction algorithm for PET, was recently introduced by GE Healthcare on their PET scanners to improve clinical image quality and quantification. In this work, we determined the optimum penalization factor (beta) for clinical use of Q.Clear and compared Q.Clear with standard PET reconstructions. Methods: A National Electrical Manufacturers Association image-quality phantom was scanned on a time-of-flight PET/CT scanner and reconstructed using ordered-subset expectation maximization (OSEM), OSEM with point-spread function (PSF) modeling, and the Q.Clear algorithm (which also includes PSF modeling). Q.Clear was investigated for β (B) values of 100-1,000. Contrast recovery (CR) and background variability (BV) were measured from 3 repeated scans, reconstructed with the different algorithms. Fifteen oncology body 18 F-FDG PET/CT scans were reconstructed using OSEM, OSEM PSF, and Q.Clear using B values of 200, 300, 400, and 500. These were visually analyzed by 2 scorers and scored by rank against a panel of parameters (overall image quality; background liver, mediastinum, and marrow image quality; noise level; and lesion detectability). Results: As β is increased, the CR and BV decreases; Q.Clear generally gives a higher CR and lower BV than OSEM. For the smallest sphere reconstructed with Q.Clear B400, CR is 28.4% and BV 4.2%, with corresponding values for OSEM of 24.7% and 5.0%. For the largest hot sphere, Q.Clear B400 yields a CR of 75.2% and a BV of 3.8%, with corresponding values for OSEM of 64.4% and 4.0%. Scorer 1 and 2 ranked B400 as the preferred reconstruction in 13 of 15 (87%) and 10 of 15 (73%) cases. The least preferred reconstruction was OSEM PSF in all cases. In most cases, lesion detectability was highest ranked for B200, in 9 of 15 (67%) and 10 of 15 (73%), with OSEM PSF ranked lowest. Poor lesion detectability on OSEM PSF was seen in cases of mildly 18 F-FDG-avid mediastinal nodes in lung cancer and small liver metastases due to background noise. Conversely, OSEM PSF was ranked second highest for lesion detectability in most pulmonary nodule evaluation cases. The combined scores confirmed B400 to be the preferred reconstruction. Conclusion: Our phantom measurement results demonstrate improved CR and reduced BV when using Q.Clear instead of OSEM. A β value of 400 is recommended for oncology body PET/CT using Q.Clear.

Performance Characteristics of the Whole-Body Discovery IQ PET/CT System

“…Under these conditions, sphere contrast recovery coefficients remained at about constant levels whereas background variability increased. The introduction of Q.Clear improves image quality noticeably, increasing contrast recovery coefficients and decreasing background variability, consistent with results from the Discovery 960 scanner based on lutetium-yttrium oxyorthosilicate crystals (6). The image quality results suggest the need to optimize the reconstruction parameters, particularly the b parameter in Q.Clear, for specific clinical applications-such as low-count PET acquisitions-that have short acquisitions or administer a low activity.…”

“…The scanner also includes a new reconstruction algorithm, Q.Clear, which has been shown to significantly improve signal-to-noise ratio and SUV quantification in a lutetium-yttrium oxyorthosilicate PET/CT scanner (6), when compared with ordered-subsets expectation maximization.…”

18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer

“…A specific issue is related to reconstruction-dependent variations encountered with recently introduced advanced image reconstruction algorithms such as those incorporating the point spread function (PSF) in the system matrix [8], or Bayesian penalised likelihood (BPL) reconstruction [9]. These new image reconstruction schemes have been shown to produce SUV metrics significantly higher than conventional ordered subset expectation maximization (OSEM) algorithms [10].…”

Advances in clinical molecular imaging instrumentation

“…One example of a prior is the Relative Difference penalty [79] which penalises large relative differences between neighbouring voxel values, but with the option to have some edge preservation. This penalty forms the basis for the recent Q.Clear commercial implementation [80]. Just like other regularisation methods, MAP introduces extra parameters.…”