Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner
Open accessAbstract
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.
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PL reconstruction has the potential to systematically improve SNR compared to OSEM-based algorithms.
“Based on the potential of PL reconstruction to systematically improve SNR compared to OSEM-based algorithms [108,113,168]”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [168]
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References 30 · Cited by 51
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- Authors
- Caribé, P. R. R. V., Koole, M., D’Asseler, Y., Van Den Broeck, B., & Vandenberghe, S.
- Affiliations
- Universitair Ziekenhuis Leuven (BE)
- Ghent University Hospital (BE)
- Ghent University (BE)
- Venue
- EJNMMI Physics
- Published
- 2019-12
- Volume
- 6
- Issue
- 1
- Pages
- 22-22
- ISSN
- 2197-7364 (ISSN-L)
- PMC
- PMC6904688
- OpenAlex
- W2995364088
- Cited by
- 51
- References
- 30
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 11 · 2023: 9 · 2022: 5 · 2021: 9 · 2020: 5 · 2018: 1
- FWCI
- 3.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- EJNMMI Physics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (process number 235040/2014-2)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- PET image reconstruction, noise reduction, Bayesian penalized likelihood reconstruction, Q.Clear, BSREM, OSEM, time-of-flight PET, PET/CT, contrast recovery, coefficient of variation, contrast-to-noise ratio, point spread function, acquisition time reduction
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