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

Abstract Purpose Q.Clear is a block sequential regularized expectation maximization (BSREM) penalized-likelihood reconstruction algorithm for PET. It tries to improve image quality by controlling noise amplification during image reconstruction. In this study, the noise properties of this BSREM were compared to the ordered-subset expectation maximization (OSEM) algorithm for both phantom and patient data acquired on a state-of-the-art PET/CT. Methods The NEMA IQ phantom and a whole-body patient study were acquired on a GE DMI 3-rings system in list mode and different datasets with varying noise levels were generated. Phantom data was evaluated using four different contrast ratios. These were reconstructed using BSREM with different β-factors of 300–3000 and with a clinical setting used for OSEM including point spread function (PSF) and time-of-flight (TOF) information. Contrast recovery (CR), background noise levels (coefficient of variation, COV), and contrast-to-noise ratio (CNR) were used to determine the performance in the phantom data. Findings based on the phantom data were compared with clinical data. For the patient study, the SUV ratio, metabolic active tumor volumes (MATVs), and the signal-to-noise ratio (SNR) were evaluated using the liver as the background region. Results Based on the phantom data for the same count statistics, BSREM resulted in higher CR and CNR and lower COV than OSEM. The CR of OSEM matches to the CR of BSREM with β = 750 at high count statistics for 8:1. A similar trend was observed for the ratios 6:1 and 4:1. A dependence on sphere size, counting statistics, and contrast ratio was confirmed by the CNR of the ratio 2:1. BSREM with β = 750 for 2.5 and 1.0 min acquisition has comparable COV to the 10 and 5.0 min acquisitions using OSEM. This resulted in a noise reduction by a factor of 2–4 when using BSREM instead of OSEM. For the patient data, a similar trend was observed, and SNR was reduced by at least a factor of 2 while preserving contrast. Conclusion The BSREM reconstruction algorithm allowed a noise reduction without a loss of contrast by a factor of 2–4 compared to OSEM reconstructions for all data evaluated. This reduction can be used to lower the injected dose or shorten the acquisition time.

Download Full-text

A clinical evaluation of the impact of the Bayesian penalized likelihood reconstruction algorithm on PET FDG metrics

Nuclear Medicine Communications ◽

10.1097/mnm.0000000000000729 ◽

2017 ◽

Vol 38 (11) ◽

pp. 979-984 ◽

Cited By ~ 3

Author(s):

Delphine Vallot ◽

Olivier Caselles ◽

Leonor Chaltiel ◽

Anthony Fernandez ◽

Erwan Gabiache ◽

...

Keyword(s):

Clinical Evaluation ◽

Penalized Likelihood ◽

Reconstruction Algorithm ◽

The Impact

Download Full-text

Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies

Nuclear Medicine Communications ◽

10.1097/mnm.0000000000000604 ◽

2017 ◽

Vol 38 (1) ◽

pp. 57-66 ◽

Cited By ~ 15

Author(s):

Bert-Ram Sah ◽

Paul Stolzmann ◽

Gaspar Delso ◽

Scott D. Wollenweber ◽

Martin Hüllner ◽

...

Keyword(s):

Clinical Evaluation ◽

Expectation Maximization ◽

Fdg Pet ◽

Reconstruction Algorithm ◽

Pet Ct ◽

18F Fdg ◽

Fdg Pet Ct ◽

Ct Studies

Download Full-text

The Impact of a Bayesian Penalized Likelihood Reconstruction Algorithm on the Evaluation of Indeterminate Pulmonary Nodules by Dual–Time Point 18F-FDG PET/CT

Clinical Nuclear Medicine ◽

10.1097/rlu.0000000000001713 ◽

2017 ◽

Vol 42 (7) ◽

pp. e352-e354 ◽

Cited By ~ 6

Author(s):

Tiago Sampaio Vieira ◽

Diogo Borges Faria ◽

Fernando Azevedo Silva ◽

Francisco Pimentel ◽

José Pereira de Oliveira

Keyword(s):

Fdg Pet ◽

Penalized Likelihood ◽

Pulmonary Nodules ◽

Reconstruction Algorithm ◽

Pet Ct ◽

18F Fdg ◽

Dual Time Point ◽

Fdg Pet Ct ◽

The Impact ◽

Time Point

Download Full-text

Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical 18F-FDG PET/CT

EJNMMI Physics ◽

10.1186/s40658-019-0262-y ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 3

Author(s):

Joost te Riet ◽

Sjoerd Rijnsdorp ◽

Mark J. Roef ◽

Albert J. Arends

Keyword(s):

Fdg Pet ◽

Penalized Likelihood ◽

Reconstruction Algorithm ◽

Pet Ct ◽

18F Fdg ◽

Fdg Pet Ct

Download Full-text

The clinical effectiveness of reconstructing 18F-sodium fluoride PET/CT bone using Bayesian penalized likelihood algorithm for evaluation of metastatic bone disease in obese patients

British Journal of Radiology ◽

10.1259/bjr.20210043 ◽

2021 ◽

Vol 94 (1120) ◽

pp. 20210043

Author(s):

Sharjeel Usmani ◽

Najeeb Ahmed ◽

Gopinath Gnanasegaran ◽

Rashid Rasheed ◽

Fahad Marafi ◽

...

Keyword(s):

Image Quality ◽

Sodium Fluoride ◽

Penalized Likelihood ◽

Clinical Effectiveness ◽

Reconstruction Algorithm ◽

Whole Body ◽

Obese Patients ◽

Positron Emission ◽

Pet Ct ◽

Clinical Cases

Objective: A new Bayesian penalized likelihood reconstruction algorithm for positron emission tomography (PET) (Q.Clear) is now in clinical use for fludeoxyglucose (FDG) PET/CT. However, experience with non-FDG tracers and in special patient populations is limited. This pilot study aims to compare Q.Clear to standard PET reconstructions for 18F sodium fluoride (18F-NaF) PET in obese patients. Methods: 30 whole body 18F-NaF PET/CT scans (10 patients with BMI 30–40 Kg/m2 and 20 patients with BMI >40 Kg/m2) and a NEMA image quality phantom scans were analyzed using ordered subset expectation maximization (OSEM) and Q.Clear reconstructions methods with B400, 600, 800 and 1000. The images were assessed for overall image quality (IQ), noise level, background soft tissue, and lesion detectability, contrast recovery (CR), background variability (BV) and contrast-to-noise ratio (CNR) for both algorithms. Results: CNR for clinical cases was higher for Q.Clear than OSEM (p < 0.05). Mean CNR for OSEM was (21.62 ± 8.9), and for Q.Clear B400 (31.82 ± 14.6), B600 (35.54 ± 14.9), B800 (39.81 ± 16.1), and B1000 (40.9 ± 17.8). As the β value increased the CNR increased in all clinical cases. B600 was the preferred β value for reconstruction in obese patients. The phantom study showed Q.Clear reconstructions gave lower CR and lower BV than OSEM. The CNR for all spheres was significantly higher for Q.Clear (independent of β) than OSEM (p < 0.05), suggesting superiority of Q.Clear. Conclusion: This pilot clinical study shows that Q.Clear reconstruction algorithm improves overall IQ of 18F-NaF PET in obese patients. Our clinical and phantom measurement results demonstrate improved CNR and reduced BV when using Q.Clear. A β value of 600 is preferred for reconstructing 18F-NaF PET/CT with Q.Clear in obese patients. Advances in knowledge: 18F-NaF PET/CT is less susceptible to artifacts induced by body habitus. Bayesian penalized likelihood reconstruction with18F-NaF PET improves overall IQ in obese patients.

Download Full-text