dc.contributor |
Universitat de Barcelona |
dc.contributor.author |
Reynés-Llompart, Gabriel |
dc.contributor.author |
Gámez Cenzano, Cristina |
dc.contributor.author |
Vercher Conejero, José Luís |
dc.contributor.author |
Sabaté Llobera, Aida |
dc.contributor.author |
Calvo Malvar, Nahúm |
dc.contributor.author |
Martí Climent, Josep M. |
dc.date |
2020-01-31T12:28:59Z |
dc.date |
2020-01-31T12:28:59Z |
dc.date |
2018-06-08 |
dc.date |
2020-01-31T12:28:59Z |
dc.identifier.citation |
0094-2405 |
dc.identifier.citation |
687560 |
dc.identifier.uri |
http://hdl.handle.net/2445/149147 |
dc.format |
9 p. |
dc.format |
application/pdf |
dc.language.iso |
eng |
dc.publisher |
American Association of Physicists in Medicine |
dc.relation |
Reproducció del document publicat a: https://doi.org/10.1002/mp.12986 |
dc.relation |
Medical Physics, 2018, vol. 45, num. 7, p. 3214-3222 |
dc.relation |
https://doi.org/10.1002/mp.12986 |
dc.rights |
(c) American Association of Physicists in Medicine, 2018 |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Tomografia per emissió de positrons |
dc.subject |
Tomografia computada per emissió de fotó simple |
dc.subject |
Imatges mèdiques |
dc.subject |
Positron emission tomography |
dc.subject |
Single-photon emission computed tomography |
dc.subject |
Imaging systems in medicine |
dc.title |
Phantom, clinical, and texture indices evaluation and optimization of a penalized-likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner |
dc.type |
info:eu-repo/semantics/article |
dc.type |
info:eu-repo/semantics/publishedVersion |
dc.description.abstract |
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. |