Rogasch JMM, Boellaard R, Pike L, Borchmann P, Johnson P, Wolf J, Barrington SF, Kobe C. Moving the goalposts while scoring-the dilemma posed by new PET technologies. Eur J Nucl Med Mol Imaging. 2021. https://doi.org/10.1007/s00259-021-05403-2.
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
PubMed
PubMed Central
Google Scholar
Bellevre D, Blanc Fournier C, Switsers O, Dugué AE, Levy C, Allouache D, et al. Staging the axilla in breast cancer patients with 18F-FDG PET: how small are the metastases that we can detect with new generation clinical PET systems? Eur J Nucl Med Mol Imaging. 2014;41:1103–12. https://doi.org/10.1007/s00259-014-2689-7.
CAS
Article
PubMed
PubMed Central
Google Scholar
Hotta M, Minamimoto R, Yano H, Gohda Y, Shuno Y. Diagnostic performance of (18)F-FDG PET/CT using point spread function reconstruction on initial staging of rectal cancer: a comparison study with conventional PET/CT and pelvic MRI. Cancer imaging : the official publication of the International Cancer Imaging Society. 2018;18:4. https://doi.org/10.1186/s40644-018-0137-9.
Article
Google Scholar
Kawashima K, Kato K, Tomabechi M, Matsuo M, Otsuka K, Ishida K, et al. Clinical evaluation of (18)F-fludeoxyglucose positron emission tomography/CT using point spread function reconstruction for nodal staging of colorectal cancer. Br J Radiol. 2016;89:20150938. https://doi.org/10.1259/bjr.20150938.
Article
PubMed
PubMed Central
Google Scholar
Lasnon C, Hicks RJ, Beauregard JM, Milner A, Paciencia M, Guizard AV, et al. Impact of point spread function reconstruction on thoracic lymph node staging with 18F-FDG PET/CT in non-small cell lung cancer. Clin Nucl Med. 2012;37:971–6. https://doi.org/10.1097/RLU.0b013e318251e3d1.
Article
PubMed
Google Scholar
Schaefferkoetter J, Casey M, Townsend D, El Fakhri G. Clinical impact of time-of-flight and point response modeling in PET reconstructions: a lesion detection study. Phys Med Biol. 2013;58:1465–78. https://doi.org/10.1088/0031-9155/58/5/1465.
Article
PubMed
PubMed Central
Google Scholar
Akamatsu G, Mitsumoto K, Taniguchi T, Tsutsui Y, Baba S, Sasaki M. Influences of point-spread function and time-of-flight reconstructions on standardized uptake value of lymph node metastases in FDG-PET. Eur J Radiol. 2014;83:226–30. https://doi.org/10.1016/j.ejrad.2013.09.030.
Article
PubMed
Google Scholar
Armstrong IS, Kelly MD, Williams HA, Matthews JC. Impact of point spread function modelling and time of flight on FDG uptake measurements in lung lesions using alternative filtering strategies. EJNMMI physics. 2014;1:99. https://doi.org/10.1186/s40658-014-0099-3.
Article
PubMed
PubMed Central
Google Scholar
Prieto E, Domínguez-Prado I, García-Velloso MJ, Peñuelas I, Richter J, Martí-Climent JM. Impact of time-of-flight and point-spread-function in SUV quantification for oncological PET. Clin Nucl Med. 2013;38:103–9. https://doi.org/10.1097/RLU.0b013e318279b9df.
Article
PubMed
Google Scholar
Ashrafinia S, Mohy-Ud-Din H, Karakatsanis NA, Jha AK, Casey ME, Kadrmas DJ, et al. Generalized PSF modeling for optimized quantitation in PET imaging. Phys Med Biol. 2017;62:5149–79. https://doi.org/10.1088/1361-6560/aa6911.
Article
PubMed
Google Scholar
Enilorac B, Lasnon C, Nganoa C, Fruchart C, Gac AC, Damaj G, et al. Does PET reconstruction method affect Deauville score in lymphoma patients? Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018;59:1049–55. https://doi.org/10.2967/jnumed.117.202721.
CAS
Article
Google Scholar
Boellaard R, Kobe C, Zijlstra JM, Mikhaeel NG, Johnson PWM, Muller S, et al. Does PET reconstruction method affect Deauville scoring in lymphoma patients? Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018;59:1167–9. https://doi.org/10.2967/jnumed.118.211607.
Article
Google Scholar
Aide N, Lasnon C, Veit-Haibach P, Sera T, Sattler B, Boellaard R. EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies. Eur J Nucl Med Mol Imaging. 2017;44:17–31. https://doi.org/10.1007/s00259-017-3740-2.
CAS
Article
PubMed
PubMed Central
Google Scholar
Barrington SF, Sulkin T, Forbes A, Johnson PWM. All that glitters is not gold - new reconstruction methods using Deauville criteria for patient reporting. Eur J Nucl Med Mol Imaging. 2018;45:316–7. https://doi.org/10.1007/s00259-017-3893-z.
Article
PubMed
Google Scholar
Lasnon C, Enilorac B, Aide N. Reply to: "all that glitters is not gold - new reconstruction methods using Deauville criteria for patient reporting". Eur J Nucl Med Mol Imaging. 2018;45:878–81. https://doi.org/10.1007/s00259-018-3938-y.
Article
PubMed
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. American journal of nuclear medicine and molecular imaging. 2018;8:110–8.
CAS
PubMed
PubMed Central
Google Scholar
Green PJ. Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Trans Med Imaging. 1990;9:84–93. https://doi.org/10.1109/42.52985.
CAS
Article
PubMed
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;31:623–8. https://doi.org/10.1007/s12149-017-1192-1.
CAS
Article
PubMed
Google Scholar
Teoh EJ, McGowan DR, Bradley KM, Belcher E, Black E, Moore A, et al. 18F-FDG PET/CT assessment of histopathologically confirmed mediastinal lymph nodes in non-small cell lung cancer using a penalised likelihood reconstruction. Eur Radiol. 2016;26:4098–106. https://doi.org/10.1007/s00330-016-4253-2.
Article
PubMed
PubMed Central
Google Scholar
Parvizi N, Franklin JM, McGowan DR, Teoh EJ, Bradley KM, Gleeson FV. Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases? Eur J Radiol. 2015;84:1873–8. https://doi.org/10.1016/j.ejrad.2015.06.025.
Article
PubMed
Google Scholar
Sampaio Vieira T, Borges Faria D, Azevedo Silva F, Barroso S, Fonseca G, Pereira OJ. The impact of a Bayesian penalized-likelihood reconstruction algorithm on delayed-time-point Ga-68-PSMA PET for improved recurrent prostate cancer detection. Eur J Nucl Med Mol Imaging. 2018;45:1461–2. https://doi.org/10.1007/s00259-018-4023-2.
CAS
Article
PubMed
PubMed Central
Google Scholar
Ter Voert E, Muehlematter UJ, Delso G, Pizzuto DA, Müller J, Nagel HW, et al. Quantitative performance and optimal regularization parameter in block sequential regularized expectation maximization reconstructions in clinical (68)Ga-PSMA PET/MR. EJNMMI Res. 2018;8:70. https://doi.org/10.1186/s13550-018-0414-4.
Article
PubMed
PubMed Central
Google Scholar
Lindstrom E, Lindsjo L, Sundin A, Sorensen J, Lubberink M. Evaluation of block-sequential regularized expectation maximization reconstruction of (68)Ga-DOTATOC, (18)F-fluoride, and (11)C-acetate whole-body examinations acquired on a digital time-of-flight PET/CT scanner. EJNMMI physics. 2020;7:40. https://doi.org/10.1186/s40658-020-00310-1.
Article
PubMed
PubMed Central
Google Scholar
Baratto L, Duan H, Ferri V, Khalighi M, Iagaru A. The effect of various β values on image quality and semiquantitative measurements in 68Ga-RM2 and 68Ga-PSMA-11 PET/MRI images reconstructed with a block sequential regularized expectation maximization algorithm. Clin Nucl Med. 2020;45:506–13. https://doi.org/10.1097/rlu.0000000000003075.
Article
PubMed
Google Scholar
Rowley LM, Bradley KM, Boardman P, Hallam A, McGowan DR. Optimization of image reconstruction for (90)Y selective internal radiotherapy on a lutetium yttrium Orthosilicate PET/CT system using a Bayesian penalized likelihood reconstruction algorithm. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2017;58:658–64. https://doi.org/10.2967/jnumed.116.176552.
CAS
Article
Google Scholar
Scott NP, McGowan DR. Optimising quantitative (90)Y PET imaging: an investigation into the effects of scan length and Bayesian penalised likelihood reconstruction. EJNMMI Res. 2019;9:40. https://doi.org/10.1186/s13550-019-0512-y.
CAS
Article
PubMed
PubMed Central
Google Scholar
Witkowska-Patena E, Budzyńska A, Giżewska A, Dziuk M, Walęcka-Mazur A. Ordered subset expectation maximisation vs Bayesian penalised likelihood reconstruction algorithm in 18F-PSMA-1007 PET/CT. Ann Nucl Med. 2020;34:192–9. https://doi.org/10.1007/s12149-019-01433-x.
CAS
Article
PubMed
PubMed Central
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 physics. 2020;7:56. https://doi.org/10.1186/s40658-020-00325-8.
Article
PubMed
PubMed Central
Google Scholar
Seo Y, Khalighi MM, Wangerin KA, Deller TW, Wang YH, Jivan S, et al. Quantitative and qualitative improvement of low-count [(68)Ga]citrate and [(90)Y]microspheres PET image reconstructions using block sequential regularized expectation maximization algorithm. Mol Imaging Biol. 2020;22:208–16. https://doi.org/10.1007/s11307-019-01347-0.
CAS
Article
PubMed
PubMed Central
Google Scholar
Teoh EJ, McGowan DR, Schuster DM, Tsakok MT, Gleeson FV, Bradley KM. Bayesian penalised likelihood reconstruction (Q.Clear) of (18)F-fluciclovine PET for imaging of recurrent prostate cancer: semi-quantitative and clinical evaluation. Br J Radiol. 2018;91:20170727. https://doi.org/10.1259/bjr.20170727.
Article
PubMed
PubMed Central
Google Scholar
O’ Doherty J, McGowan DR, Abreu C, Barrington S. Effect of Bayesian-penalized likelihood reconstruction on [13N]-NH3 rest perfusion quantification. Journal of nuclear cardiology: official publication of the American Society of Nuclear Cardiology. 2017;24:282–90. https://doi.org/10.1007/s12350-016-0554-8.
Kirchner J, O'Donoghue JA, Becker AS, Ulaner GA. Improved image reconstruction of (89)Zr-immunoPET studies using a Bayesian penalized likelihood reconstruction algorithm. EJNMMI physics. 2021;8:6. https://doi.org/10.1186/s40658-021-00352-z.
Article
PubMed
PubMed Central
Google Scholar
Chilcott AK, Bradley KM, McGowan DR. Effect of a Bayesian penalized likelihood PET reconstruction compared with ordered subset expectation maximization on clinical image quality over a wide range of patient weights. AJR Am J Roentgenol. 2018;210:153–7. https://doi.org/10.2214/ajr.17.18060.
Article
PubMed
Google Scholar
Vallot D, Caselles O, Chaltiel L, Fernandez A, Gabiache E, Dierickx L, et al. A clinical evaluation of the impact of the Bayesian penalized likelihood reconstruction algorithm on PET FDG metrics. Nucl Med Commun. 2017;38:979–84. https://doi.org/10.1097/mnm.0000000000000729.
Article
PubMed
Google Scholar
Bradley KM, McGowan DR, Gleeson FV, Johnson GB, Young JR, Levin CS, et al. Embrace progress. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018;59:1169. https://doi.org/10.2967/jnumed.118.212761.
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. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2015;56:1447–52. https://doi.org/10.2967/jnumed.115.159301.
CAS
Article
Google Scholar
Rausch I, Ruiz A, Valverde-Pascual I, Cal-Gonzalez J, Beyer T, Carrio I. Performance evaluation of the Vereos PET/CT system according to the NEMA NU2-2012 standard. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2019;60:561–7. https://doi.org/10.2967/jnumed.118.215541.
CAS
Article
Google Scholar
Zimmermann PA, Houdu B, Césaire L, Nakouri I, De Pontville M, Lasnon C, et al. Revisiting detection of in-transit metastases in melanoma patients using digital 18F-FDG PET/CT with small-voxel reconstruction. Ann Nucl Med. 2021. https://doi.org/10.1007/s12149-021-01608-5.
Baratto L, Park SY, Hatami N, Davidzon G, Srinivas S, Gambhir SS, et al. 18F-FDG silicon photomultiplier PET/CT: a pilot study comparing semi-quantitative measurements with standard PET/CT. PLoS One. 2017;12:e0178936. https://doi.org/10.1371/journal.pone.0178936.
CAS
Article
PubMed
PubMed Central
Google Scholar
Park S, Hatami N, Baratto L, Yohannan T, Davidzon G, Iagaru A. J Nucl Med. 2018; 59 (supplement 1):431.
Meikle SR, Sossi V, Roncali E, Cherry SR, Banati R, Mankoff DA, et al. Quantitative PET in the 2020s: a roadmap. Phys Med Biol. 2020. https://doi.org/10.1088/1361-6560/abd4f7.
Brandner ED, Chetty IJ, Giaddui TG, Xiao Y, Huq MS. Motion management strategies and technical issues associated with stereotactic body radiotherapy of thoracic and upper abdominal tumors: a review from NRG oncology. Med Phys. 2017;44:2595–612. https://doi.org/10.1002/mp.12227.
Article
PubMed
Google Scholar
Walker MD, Morgan AJ, Bradley KM, McGowan DR. Data-driven respiratory gating outperforms device-based gating for clinical (18)F-FDG PET/CT. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2020;61:1678–83. https://doi.org/10.2967/jnumed.120.242248.
CAS
Article
Google Scholar
Kesner AL, Schleyer PJ, Büther F, Walter MA, Schäfers KP, Koo PJ. On transcending the impasse of respiratory motion correction applications in routine clinical imaging - a consideration of a fully automated data driven motion control framework. EJNMMI physics. 2014;1:8. https://doi.org/10.1186/2197-7364-1-8.
Article
PubMed
PubMed Central
Google Scholar
Büther F, Jones J, Seifert R, Stegger L, Schleyer P, Schäfers M. Clinical evaluation of a data-driven respiratory gating algorithm for whole-body PET with continuous bed motion. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2020;61:1520–7. https://doi.org/10.2967/jnumed.119.235770.
CAS
Article
Google Scholar
Feng T, Wang J, Dong Y, Zhao J, Li H. A novel data-driven cardiac gating signal extraction method for PET. IEEE Trans Med Imaging. 2019;38:629–37. https://doi.org/10.1109/tmi.2018.2868615.
Article
PubMed
Google Scholar
Lassen ML, Beyer T, Berger A, Beitzke D, Rasul S, Büther F, et al. Data-driven, projection-based respiratory motion compensation of PET data for cardiac PET/CT and PET/MR imaging. Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology. 2020;27:2216–30. https://doi.org/10.1007/s12350-019-01613-2.
Article
Google Scholar
Manber R, Thielemans K, Hutton BF, Wan S, McClelland J, Barnes A, et al. Joint PET-MR respiratory motion models for clinical PET motion correction. Phys Med Biol. 2016;61:6515–30. https://doi.org/10.1088/0031-9155/61/17/6515.
Article
PubMed
Google Scholar
Salomon A, Zhang B, Olivier P, Goedicke A. Robust real-time extraction of respiratory signals from PET list-mode data. Phys Med Biol. 2018;63:115009. https://doi.org/10.1088/1361-6560/aac1ac.
Article
PubMed
Google Scholar
Kang SY, Moon BS, Kim HO, Yoon HJ, Kim BS. The impact of data-driven respiratory gating in clinical F-18 FDG PET/CT: comparison of free breathing and deep-expiration breath-hold CT protocol. Ann Nucl Med. 2021;35:328–37. https://doi.org/10.1007/s12149-020-01574-4.
CAS
Article
PubMed
Google Scholar
Kesner AL, Chung JH, Lind KE, Kwak JJ, Lynch D, Burckhardt D, et al. Validation of software gating: a practical technology for respiratory motion correction in PET. Radiology. 2016;281:239–48. https://doi.org/10.1148/radiol.2016152105.
Article
PubMed
Google Scholar
Ren S, Jin X, Chan C, Jian Y, Mulnix T, Liu C, et al. Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution. Phys Med Biol. 2017;62:4741–55. https://doi.org/10.1088/1361-6560/aa700c.
Article
PubMed
PubMed Central
Google Scholar
Walker MD, Morgan AJ, Bradley KM, McGowan DR. Evaluation of data-driven respiratory gating waveforms for clinical PET imaging. EJNMMI Res. 2019;9:1. https://doi.org/10.1186/s13550-018-0470-9.
CAS
Article
PubMed
PubMed Central
Google Scholar
Liberini V, Kotasidis F, Treyer V, Messerli M, Orita E, Engel-Bicik I, et al. Impact of PET data driven respiratory motion correction and BSREM reconstruction of (68)Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS). Sci Rep. 2021;11:2273. https://doi.org/10.1038/s41598-020-80855-4.
CAS
Article
PubMed
PubMed Central
Google Scholar
Spangler-Bickell MG, Deller TW, Bettinardi V, Jansen F. Ultra-fast list-mode reconstruction of short PET frames and example applications. Journal of nuclear medicine: official publication, Society of Nuclear Medicine. 2021;62:287–92. https://doi.org/10.2967/jnumed.120.245597.
Cherry SR, Badawi RD, Karp JS, Moses WW, Price P, Jones T. Total-body imaging: transforming the role of positron emission tomography. Sci Transl Med. 2017;9. https://doi.org/10.1126/scitranslmed.aaf6169.
Cherry SR, Jones T, Karp JS, Qi J, Moses WW, Badawi RD. Total-Body PET: Maximizing sensitivity to create new opportunities for clinical research and patient care. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018;59:3–12. https://doi.org/10.2967/jnumed.116.184028.
CAS
Article
Google Scholar
Tan H, Gu Y, Yu H, Hu P, Zhang Y, Mao W, et al. Total-body PET/CT: current applications and future perspectives. AJR Am J Roentgenol. 2020;215:325–37. https://doi.org/10.2214/ajr.19.22705.
Article
PubMed
Google Scholar
Vandenberghe S, Moskal P, Karp JS. State of the art in total body PET. EJNMMI physics. 2020;7:35. https://doi.org/10.1186/s40658-020-00290-2.
Article
PubMed
PubMed Central
Google Scholar
Zhang YQ, Hu PC, Wu RZ, Gu YS, Chen SG, Yu HJ, et al. The image quality, lesion detectability, and acquisition time of (18)F-FDG total-body PET/CT in oncological patients. Eur J Nucl Med Mol Imaging. 2020;47:2507–15. https://doi.org/10.1007/s00259-020-04823-w.
CAS
Article
PubMed
Google Scholar
Beckford Vera D, Schulte B, Henrich T, Flavell R, Seo Y, Abdelhafez Y, et al. J Nucl Med. 2020;61 (supplement 1):545.
Badawi RD, Shi H, Hu P, Chen S, Xu T, Price PM, et al. First human imaging studies with the EXPLORER total-body PET scanner. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2019;60:299–303. https://doi.org/10.2967/jnumed.119.226498.
CAS
Article
Google Scholar
Kaplan S, Zhu YM. Full-dose PET image estimation from low-dose PET image using deep learning: a pilot study. J Digit Imaging. 2019;32:773–8. https://doi.org/10.1007/s10278-018-0150-3.
Article
PubMed
Google Scholar
Chen KT, Schürer M, Ouyang J, Koran MEI, Davidzon G, Mormino E, et al. Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning. Eur J Nucl Med Mol Imaging. 2020;47:2998–3007. https://doi.org/10.1007/s00259-020-04897-6.
CAS
Article
PubMed
Google Scholar
Liu CC, Qi J. Higher SNR PET image prediction using a deep learning model and MRI image. Phys Med Biol. 2019;64:115004. https://doi.org/10.1088/1361-6560/ab0dc0.
Article
PubMed
PubMed Central
Google Scholar
Wang YJ, Baratto L, Hawk KE, Theruvath AJ, Pribnow A, Thakor AS, et al. Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. Eur J Nucl Med Mol Imaging. 2021. https://doi.org/10.1007/s00259-021-05197-3.
Sibille L, Seifert R, Avramovic N, Vehren T, Spottiswoode B, Zuehlsdorff S, et al. (18)F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks. Radiology. 2020;294:445–52. https://doi.org/10.1148/radiol.2019191114.
Article
PubMed
Google Scholar
Capobianco N, Meignan M, Cottereau AS, Vercellino L, Sibille L, Spottiswoode B, et al. Deep-learning (18)F-FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-cell lymphoma. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2021;62:30–6. https://doi.org/10.2967/jnumed.120.242412.
CAS
Article
Google Scholar
Weber M, Kersting D, Umutlu L, Schäfers M, Rischpler C, Fendler WP, et al. Just another "Clever Hans"? Neural networks and FDG PET-CT to predict the outcome of patients with breast cancer. Eur J Nucl Med Mol Imaging. 2021. https://doi.org/10.1007/s00259-021-05270-x.
Mehranian A, Wollenweber SD, Walker MD, Bradley KM, Su K, Johnsen R, et al. Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise. Eur J Nucl Med Mol Imaging. 2021; under revision.
Alberts I, Hunermund JN, Prenosil G, Mingels C, Bohn KP, Viscione M, et al. Clinical performance of long axial field of view PET/CT: a head-to-head intra-individual comparison of the biograph vision Quadra with the biograph vision PET/CT. Eur J Nucl Med Mol Imaging. 2021. https://doi.org/10.1007/s00259-021-05282-7.
Sonni I, Baratto L, Park S, Hatami N, Srinivas S, Davidzon G, et al. Initial experience with a SiPM-based PET/CT scanner: influence of acquisition time on image quality. EJNMMI physics. 2018;5:9. https://doi.org/10.1186/s40658-018-0207-x.
Article
PubMed
PubMed Central
Google Scholar
Lasnon C, Coudrais N, Houdu B, Nganoa C, Salomon T, Enilorac B, et al. How fast can we scan patients with modern (digital) PET/CT systems? Eur J Radiol. 2020;129:109144. https://doi.org/10.1016/j.ejrad.2020.109144.
Article
PubMed
Google Scholar
Weber M, Jentzen W, Hofferber R, Herrmann K, Fendler WP, Conti M, et al. Evaluation of [(68)Ga]Ga-PSMA PET/CT images acquired with a reduced scan time duration in prostate cancer patients using the digital biograph vision. EJNMMI Res. 2021;11:21. https://doi.org/10.1186/s13550-021-00765-y.
CAS
Article
PubMed
PubMed Central
Google Scholar
Weber M, Jentzen W, Hofferber R, Herrmann K, Fendler WP, Rischpler C, et al. Evaluation of (18)F-FDG PET/CT images acquired with a reduced scan time duration in lymphoma patients using the digital biograph vision. BMC Cancer. 2021;21:62. https://doi.org/10.1186/s12885-020-07723-2.
CAS
Article
PubMed
PubMed Central
Google Scholar
Sanaat A, Shiri I, Arabi H, Mainta I, Nkoulou R, Zaidi H. Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging. Eur J Nucl Med Mol Imaging. 2021. https://doi.org/10.1007/s00259-020-05167-1.
Lasnon C, Desmonts C, Quak E, Gervais R, Do P, Dubos-Arvis C, et al. Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients. Eur J Nucl Med Mol Imaging. 2013;40:985–96. https://doi.org/10.1007/s00259-013-2391-1.
CAS
Article
PubMed
PubMed Central
Google Scholar
Lasnon C, Salomon T, Desmonts C, Dô P, Oulkhouir Y, Madelaine J, et al. Generating harmonized SUV within the EANM EARL accreditation program: software approach versus EARL-compliant reconstruction. Ann Nucl Med. 2017;31:125–34. https://doi.org/10.1007/s12149-016-1135-2.
Article
PubMed
Google Scholar
Quak E, Le Roux PY, Hofman MS, Robin P, Bourhis D, Callahan J, et al. Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients. Eur J Nucl Med Mol Imaging. 2015;42:2072–82. https://doi.org/10.1007/s00259-015-3128-0.
Article
PubMed
PubMed Central
Google Scholar
Quak E, Le Roux PY, Lasnon C, Robin P, Hofman MS, Bourhis D, et al. Does PET SUV harmonization affect PERCIST response classification? Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2016;57:1699–706. https://doi.org/10.2967/jnumed.115.171983.
CAS
Article
Google Scholar
Kaalep A, Burggraaff CN, Pieplenbosch S, Verwer EE, Sera T, Zijlstra J, et al. Quantitative implications of the updated EARL 2019 PET-CT performance standards. EJNMMI physics. 2019;6:28. https://doi.org/10.1186/s40658-019-0257-8.
Article
PubMed
PubMed Central
Google Scholar
Kaalep A, Sera T, Rijnsdorp S, Yaqub M, Talsma A, Lodge MA, et al. Feasibility of state of the art PET/CT systems performance harmonisation. Eur J Nucl Med Mol Imaging. 2018;45:1344–61. https://doi.org/10.1007/s00259-018-3977-4.
Article
PubMed
PubMed Central
Google Scholar
Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, et al. A postreconstruction harmonization method for multicenter radiomic studies in PET. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018;59:1321–8. https://doi.org/10.2967/jnumed.117.199935.
CAS
Article
Google Scholar
Lasnon C, Enilorac B, Popotte H, Aide N. Impact of the EARL harmonization program on automatic delineation of metabolic active tumour volumes (MATVs). EJNMMI Res. 2017;7:30. https://doi.org/10.1186/s13550-017-0279-y.
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