title: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, United Kingdom Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom; and eugene.teoh@oncology.ox.ac.uk.
authors.forename: Eugene J
authors.initials: EJ
authors.lastname: Teoh
authors.affiliation: Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom; and Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, United Kingdom.
authors.forename: Daniel R
authors.initials: DR
authors.lastname: McGowan
authors.affiliation: Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, United Kingdom.
authors.forename: Ruth E
authors.initials: RE
authors.lastname: Macpherson
authors.affiliation: Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, United Kingdom.
authors.forename: Kevin M
authors.initials: KM
authors.lastname: Bradley
authors.affiliation: Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, United Kingdom Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, United Kingdom; and.
authors.forename: Fergus V
authors.initials: FV
authors.lastname: Gleeson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D000069550:Machine Learning; D064847:Multimodal Imaging / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*
keywords: Bayesian penalized likelihood; NEMA; image quality; image reconstruction; optimization; positron emission tomography