Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images

Nishiyama Y, Kinuya S, Kato T, Kayano D, Sato S, Tashiro M, et al. Nuclear medicine practice in Japan: a report of the eighth nationwide survey in 2017. Ann Nucl Med. 2019;33:725–32. https://doi.org/10.1007/s12149-019-01382-5.

Article  CAS  Google Scholar 

Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007;48:932–45. https://doi.org/10.2967/jnumed.106.035774.

Article  Google Scholar 

van der Vos CS, Koopman D, Rijnsdorp S, Arends AJ, Boellaard R, van Dalen JA, et al. Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET. Eur J Nucl Med Mol Imaging. 2017;44:4–16. https://doi.org/10.1007/s00259-017-3727-z.

Article  Google Scholar 

Aide N, Lasnon C, Kesner A, Levin CS, Buvat I, Iagaru A, et al. New PET technologies: embracing progress and pushing the limits. Eur J Nucl Med Mol Imaging. 2021;48:2711–26. https://doi.org/10.1007/s00259-021-05390-4.

Article  Google Scholar 

Miwa K, Wagatsuma K, Iimori T, Sawada K, Kamiya T, Sakurai M, et al. Multicenter study of quantitative PET system harmonization using NIST-traceable 68Ge/68Ga cross-calibration kit. Phys Med. 2018;52:98–103. https://doi.org/10.1016/j.ejmp.2018.07.001.

Article  Google Scholar 

Karaoglanis K, Polycarpou I, Efthimiou N, Tsoumpas C. Appropriately regularized OSEM can improve the reconstructed PET images of data with low count statistics. Hell J Nucl Med. 2015;18:140–5. https://doi.org/10.1967/s002449910209.

Article  Google Scholar 

Ahn S, Ross SG, Asma E, Miao J, Jin X, Cheng L, et al. Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET. Phys Med Biol. 2015;60:5733–51. https://doi.org/10.1088/0031-9155/60/15/5733.

Article  Google Scholar 

Teoh EJ, McGowan DR, Macpherson RE, Bradley KM, Gleeson FV. Phantom and clinical evaluation of the Bayesian penalized likelihood reconstruction algorithm Q.Clear on an LYSO PET/CT system. J Nucl Med. 2015;56:1447–52. https://doi.org/10.2967/jnumed.115.159301.

Article  CAS  Google Scholar 

Howard BA, Morgan R, Thorpe MP, Turkington TG, Oldan J, James OG, et al. Comparison of Bayesian penalized likelihood reconstruction versus OS-EM for characterization of small pulmonary nodules in oncologic PET/CT. Ann Nucl Med. 2017. https://doi.org/10.1007/s12149-017-1192-1.

Article  Google Scholar 

Teoh EJ, McGowan DR, Bradley KM, Belcher E, Black E, Gleeson FV. Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules. Eur Radiol. 2016;26:576–84. https://doi.org/10.1007/s00330-015-3832-y.

Article  Google Scholar 

Schwyzer M, Martini K, Benz DC, Burger IA, Ferraro DA, Kudura K, et al. Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance. Eur Radiol. 2019. https://doi.org/10.1007/s00330-019-06498-w.

Article  Google Scholar 

Rogasch JM, Suleiman S, Hofheinz F, Bluemel S, Lukas M, Amthauer H, et al. Reconstructed spatial resolution and contrast recovery with Bayesian penalized likelihood reconstruction (Q.Clear) for FDG-PET compared to time-of-flight (TOF) with point spread function (PSF). EJNMMI Phys. 2020;7:2. https://doi.org/10.1186/s40658-020-0270-y.

Article  Google Scholar 

Kurita Y, Ichikawa Y, Nakanishi T, Tomita Y, Hasegawa D, Murashima S, et al. The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point spread function correction. Ann Nucl Med. 2020. https://doi.org/10.1007/s12149-020-01446-x.

Article  Google Scholar 

Asma E, Ahn S, Ross SG, Chen A, Manjeshwar RM. Accurate and consistent lesion quantitation with clinically acceptable penalized likelihood images. In: 2012 IEEE nuclear science symposium and medical imaging conference record (NSS/MIC); 2012. p. 4062–6.

Miwa K, Wagatsuma K, Nemoto R, Masubuchi M, Kamitaka Y, Yamao T, et al. Detection of sub-centimeter lesions using digital TOF-PET/CT system combined with Bayesian penalized likelihood reconstruction algorithm. Ann Nucl Med. 2020. https://doi.org/10.1007/s12149-020-01500-8.

Article  Google Scholar 

Yamaguchi S, Wagatsuma K, Miwa K, Ishii K, Inoue K, Fukushi M. Bayesian penalized-likelihood reconstruction algorithm suppresses edge artifacts in PET reconstruction based on point-spread-function. Phys Med. 2018;47:73–9. https://doi.org/10.1016/j.ejmp.2018.02.013.

Article  Google Scholar 

Asma E, Ahn S, Qian H, Gopalakrishnan G, Thielemans K, Ross SG, et al. Quantitatively accurate image reconstruction for clinical whole-body PET imaging. In: Proceedings of The 2012 Asia Pacific signal and information processing association annual summit and conference; 2012. p. 1–9.

Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi MC. Physical performance of the new hybrid PETCT Discovery-690. Med Phys. 2011;38:5394–411. https://doi.org/10.1118/1.3635220.

Article  CAS  Google Scholar 

National Electrical Manufacturers Association. NEMA standards publication NU 2–2018: performance measurements of positron emission tomographs (PET). National Electrical Manufacturers Association: Rosslyn; 2018. p. 41.

Google Scholar 

Wangerin KA, Ahn S, Wollenweber S, Ross SG, Kinahan PE, Manjeshwar RM. Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method. J Med Imaging (Bellingham). 2017;4:011002. https://doi.org/10.1117/1.JMI.4.1.011002.

Article  Google Scholar 

Aljared A, Alharbi AA, Huellner MW. BSREM reconstruction for improved detection of in-transit metastases with digital FDG-PET/CT in patients with malignant melanoma. Clin Nucl Med. 2018;43:370–1. https://doi.org/10.1097/RLU.0000000000002024.

Article  Google Scholar 

Ahn S, Fessler JA. Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms. IEEE Trans Med Imaging. 2003;22:613–26. https://doi.org/10.1109/tmi.2003.812251.

Article  Google Scholar 

Gnesin S, Kieffer C, Zeimpekis K, Papazyan JP, Guignard R, Prior JO, et al. Phantom-based image quality assessment of clinical (18)F-FDG protocols in digital PET/CT and comparison to conventional PMT-based PET/CT. EJNMMI Phys. 2020;7:1. https://doi.org/10.1186/s40658-019-0269-4.

Article  Google Scholar 

Lantos J, Mittra ES, Levin CS, Iagaru A. Standard OSEM vs. regularized PET image reconstruction: qualitative and quantitative comparison using phantom data and various clinical radiopharmaceuticals. Am J Nucl Med Mol Imaging. 2018;8:110–8.

CAS  Google Scholar 

Hsu DFC, Ilan E, Peterson WT, Uribe J, Lubberink M, Levin CS. Studies of a next-generation silicon-photomultiplier-based time-of-flight PET/CT system. J Nucl Med. 2017;58:1511–8. https://doi.org/10.2967/jnumed.117.189514.

Article  CAS  Google Scholar 

te Riet J, Rijnsdorp S, Roef MJ, Arends AJ. Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical 18F-FDG PET/CT. EJNMMI Phys. 2019. https://doi.org/10.1186/s40658-019-0262-y.

Article  Google Scholar 

Hashimoto N, Morita K, Tsutsui Y, Himuro K, Baba S, Sasaki M. Time-of-flight information improved the detectability of subcentimeter spheres using a clinical PET/CT scanner. J Nucl Med Technol. 2018;46:268–73. https://doi.org/10.2967/jnmt.117.204735.

Article  Google Scholar 

Zimmermann PA, Houdu B, Cesaire L, Nakouri I, De Pontville M, Lasnon C, et al. Revisiting detection of in-transit metastases in melanoma patients using digital (18)F-FDG PET/CT with small-voxel reconstruction. Ann Nucl Med. 2021;35:669–79. https://doi.org/10.1007/s12149-021-01608-5.

Article  CAS  Google Scholar 

Wu Z, Guo B, Huang B, Hao X, Wu P, Zhao B, et al. Phantom and clinical assessment of small pulmonary nodules using Q.Clear reconstruction on a silicon-photomultiplier-based time-of-flight PET/CT system. Sci Rep. 2021;11:10328. https://doi.org/10.1038/s41598-021-89725-z.

Article  CAS  Google Scholar 

Wu Z, Guo B, Huang B, Zhao B, Qin Z, Hao X, et al. Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography? J Appl Clin Med Phys. 2021;22:224–33. https://doi.org/10.1002/acm2.13129.

Article  Google Scholar 

Nuyts J, Michel C, Brepoels L, Ceuninck LD, Deroose C, Goffin K, et al. Performance of MAP reconstruction for hot lesion detection in whole-body PET/CT: an evaluation with human and numerical observers. IEEE Trans Med Imaging. 2009;28:67–73. https://doi.org/10.1109/TMI.2008.927349.

Article  Google Scholar 

Yoshii T, Miwa K, Yamaguchi M, Shimada K, Wagatsuma K, Yamao T, et al. Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom. EJNMMI Phys. 2020;7:56. https://doi.org/10.1186/s40658-020-00325-8.

Article  Google Scholar 

Reynes-Llompart G, Gamez-Cenzano C, Vercher-Conejero JL, Sabate-Llobera A, Calvo-Malvar N, Marti-Climent JM. Phantom, clinical, and texture indices evaluation and optimization of a penalized-likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner. Med Phys. 2018;45:3214–22. https://doi.org/10.1002/mp.12986.

Article  Google Scholar 

留言 (0)

沒有登入
gif