Phantom, clinical, and texture indices evaluation and optimization of a penalized‐likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner
Open accessAbstract
Introduction: The aim of this study was to evaluate the behavior of a penalized‐likelihood image reconstruction method (Q.Clear) under different count statistics and lesion‐to‐background ratios (LBR) on a BGO scanner, in order to obtain an optimum penalization factor (β value) to study and optimize for different acquisition protocols and clinical goals. Methods: Both phantom and patient images were evaluated. Data from an image quality phantom were acquired using different Lesion‐to‐Background ratios and acquisition times. Then, each series of the phantom was reconstructed using β values between 50 and 500, at intervals of 50. Hot and cold contrasts were obtained, as well as background variability and contrast‐to‐noise ratio (CNR). Fifteen 18 F‐FDG patients (five brain scans and 10 torso acquisitions) were acquired and reconstructed using the same β values as in the phantom reconstructions. From each lesion in the torso acquisition, noise, contrast, and signal‐to‐noise ratio (SNR) were computed. Image quality was assessed by two different nuclear medicine physicians. Additionally, the behaviors of 12 different textural indices were studied over 20 different lesions. Results: Q.Clear quantification and optimization in patient studies depends on the activity concentration as well as on the lesion size. In the studied range, an increase on β is translated in a decrease in lesion contrast and noise. The net product is an overall increase in the SNR, presenting a tendency to a steady value similar to the CNR in phantom data. As the activity concentration or the sphere size increase the optimal β increases, similar results are obtained from clinical data. From the subjective quality assessment, the optimal β value for torso scans is in a range between 300 and 400, and from 100 to 200 for brain scans. For the recommended torso β values, texture indices present coefficients of variation below 10%. Conclusions: Our phantom and patients demonstrate that improvement of CNR and SNR of Q.Clear algorithm which depends on the studied conditions and the penalization factor. Using the Q.Clear reconstruction algorithm in a BGO scanner, a β value of 350 and 200 appears to be the optimal value for 18F‐FDG oncology and brain PET/CT, respectively.
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Several studies on subjective image quality in whole-body [18F]FDG-PET with PL reconstruction found that image quality was highest at beta values of 450 (to 600) despite lower lesion contrast recovery or image sharpness at these beta values compared to lower values.
“Several studies on subjective image quality in whole-body [18F]FDG-PET with PL reconstruction found that image quality was highest at beta values of 450 (to 600) despite lower lesion CR or “image sharpness” at these beta values compared to lower values [41,108,112,175,179].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [179]
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- Authors
- Reynés‐Llompart, G., Gámez‐Cenzano, C., Vercher‐Conejero, J. L., Sabaté‐Llobera, A., Calvo‐Malvar, N., & Martí‐Climent, J. M.
- Affiliations
- Bellvitge University Hospital (ES)
- PET Unit. Nuclear Medicine Dept IDI. Hospital U. de Bellvitge‐IDIBELL L'Hospitalet de Llobregat Barcelona Spain
- Nuclear Medicine Clínica Universidad de Navarra Pamplona Spain
- Corresponding author
- Reynés‐Llompart, Gabriel
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2018-06-08
- Volume
- 45
- Issue
- 7
- Pages
- 3214-3222
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- OpenAlex
- W2804213310
- Cited by
- 55
- References
- 31
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 6 · 2022: 8 · 2021: 9 · 2020: 8 · 2019: 6 · 2018: 1
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Medical Physics (publishedVersion)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- penalized-likelihood reconstruction, Q.Clear, lesion-to-background ratio, count statistics, 18F-FDG PET, image quality, texture indices, contrast-to-noise ratio, signal-to-noise ratio, image quality phantom
- MeSH terms
- Positron Emission Tomography Computed Tomography, Brain, Humans, Image Processing, Computer-Assisted, Likelihood Functions, Phantoms, Imaging, Signal-To-Noise Ratio, Torso
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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