{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:14:48Z","timestamp":1772165688226,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Youth Science and Technology Project of Shanghai Pudong New Area Health Commission","award":["No.PW2023-B08"],"award-info":[{"award-number":["No.PW2023-B08"]}]},{"name":"Key Discipline Construction Project of Shanghai Pudong New Area Health Commission","award":["No.PWZxk2022-12"],"award-info":[{"award-number":["No.PWZxk2022-12"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-024-01422-1","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T09:02:42Z","timestamp":1725872562000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy"],"prefix":"10.1186","volume":"24","author":[{"given":"Na","family":"Qi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyuan","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengbei","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan-Jie","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"issue":"9","key":"1422_CR1","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1007\/s00259-016-3415-4","volume":"43","author":"T Van den Wyngaert","year":"2016","unstructured":"Van den Wyngaert T, Strobel K, Kampen WU, et al. The EANM practice guidelines for bone scintigraphy. Eur J Nucl Med Mol Imaging. 2016;43(9):1723\u201338.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"12","key":"1422_CR2","first-page":"BP99","volume":"30","author":"E Bombardieri","year":"2003","unstructured":"Bombardieri E, Aktolun C, Baum RP, et al. Bone scintigraphy: procedure guidelines for tumour imaging. Eur J Nucl Med Mol Imaging. 2003;30(12):BP99\u2013106.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"12","key":"1422_CR3","doi-asserted-by":"publisher","first-page":"2604","DOI":"10.1007\/s00330-011-2221-4","volume":"21","author":"HL Yang","year":"2011","unstructured":"Yang HL, Liu T, Wang XM, Xu Y, Deng SM. Diagnosis of bone metastases: a meta-analysis comparing (1)(8)FDG PET, CT, MRI and bone scintigraphy. Eur Radiol. 2011;21(12):2604\u201317.","journal-title":"Eur Radiol"},{"issue":"6","key":"1422_CR4","first-page":"975","volume":"37","author":"S Kosuda","year":"1996","unstructured":"Kosuda S, Kaji T, Yokoyama H, et al. Does bone SPECT actually have lower sensitivity for detecting vertebral metastasis than MRI? J Nucl Med. 1996;37(6):975\u20138.","journal-title":"J Nucl Med"},{"key":"1422_CR5","first-page":"747","volume":"49","author":"JLS Wang","year":"2020","unstructured":"Wang JLS. A brief report on the results of the national survey of nuclear medicine in 2020. Chin J Nucl Med Mol Imaging. 2020;49:747\u20139.","journal-title":"Chin J Nucl Med Mol Imaging"},{"issue":"4","key":"1422_CR6","first-page":"398","volume":"46","author":"TB Bartel","year":"2018","unstructured":"Bartel TB, Kuruva M, Gnanasegaran G, et al. SNMMI Procedure Standard for Bone Scintigraphy 4.0. J Nucl Med Technol. 2018;46(4):398\u2013404.","journal-title":"J Nucl Med Technol"},{"issue":"12","key":"1422_CR7","doi-asserted-by":"publisher","first-page":"3817","DOI":"10.1007\/s00259-021-05413-0","volume":"48","author":"J Schaefferkoetter","year":"2021","unstructured":"Schaefferkoetter J, Yan J, Moon S, et al. Deep learning for whole-body medical image generation. Eur J Nucl Med Mol Imaging. 2021;48(12):3817\u201326.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"1422_CR8","doi-asserted-by":"crossref","unstructured":"Huang K, Huang S, Chen G et al. An end-to-end multi-task system of automatic lesion detection and anatomical localization in whole-body bone scintigraphy by deep learning. Bioinformatics 2023, 39(1).","DOI":"10.1093\/bioinformatics\/btac753"},{"issue":"9","key":"1422_CR9","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1007\/s12149-022-01763-3","volume":"36","author":"K Motegi","year":"2022","unstructured":"Motegi K, Miyaji N, Yamashita K, Koizumi M, Terauchi T. Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients. Ann Nucl Med. 2022;36(9):834\u201341.","journal-title":"Ann Nucl Med"},{"key":"1422_CR10","doi-asserted-by":"publisher","first-page":"51","DOI":"10.2147\/CMAR.S340114","volume":"14","author":"S Liu","year":"2022","unstructured":"Liu S, Feng M, Qiao T, et al. Deep learning for the Automatic diagnosis and analysis of bone metastasis on bone scintigrams. Cancer Manag Res. 2022;14:51\u201365.","journal-title":"Cancer Manag Res"},{"key":"1422_CR11","doi-asserted-by":"crossref","unstructured":"Wuestemann J, Hupfeld S, Kupitz D et al. Analysis of bone scans in various tumor entities using a deep-learning-based Artificial neural network algorithm-evaluation of diagnostic performance. Cancers (Basel) 2020, 12(9).","DOI":"10.3390\/cancers12092654"},{"issue":"12","key":"1422_CR12","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12149-023-01872-7","volume":"37","author":"S Han","year":"2023","unstructured":"Han S, Oh JS, Seo SY, Lee JJ. Performance of deep learning models for response evaluation on whole-body bone scans in prostate cancer. Ann Nucl Med. 2023;37(12):685\u201394.","journal-title":"Ann Nucl Med"},{"key":"1422_CR13","doi-asserted-by":"crossref","unstructured":"Hajianfar G, Sabouri M, Salimi Y et al. Artificial intelligence-based analysis of whole-body bone scintigraphy: the quest for the optimal deep learning algorithm and comparison with human observer performance. Z Med Phys 2023.","DOI":"10.1016\/j.zemedi.2023.01.008"},{"key":"1422_CR14","doi-asserted-by":"crossref","unstructured":"Jafari M, Auer D, Francis S, Garibaldi J, Chen X. DRU-Net: an efficient deep convolutional neural network for medical image segmentation. In2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 2020 Apr 3 (pp. 1144\u20131148). IEEE.","DOI":"10.1109\/ISBI45749.2020.9098391"},{"key":"1422_CR15","doi-asserted-by":"crossref","unstructured":"Ansari MY, Mangalote IA, Meher PK, Aboumarzouk O et al. Advancements in Deep Learning for B-Mode Ultrasound Segmentation: a Comprehensive Review. IEEE Trans Emerg Top Comput Intell. 2024 Apr 2.","DOI":"10.1109\/TETCI.2024.3377676"},{"key":"1422_CR16","doi-asserted-by":"publisher","first-page":"109512","DOI":"10.1016\/j.knosys.2022.109512","volume":"253","author":"HZ Meng","year":"2022","unstructured":"Meng HZ, Jian MW, Wang GG. ConvUNeXt: an efficient convolution neural network for medical image segmentation. Knowl Based Syst. 2022;253:109512.","journal-title":"Knowl Based Syst"},{"issue":"6","key":"1422_CR17","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1109\/TRPMS.2021.3071148","volume":"6","author":"Y Akhtar","year":"2021","unstructured":"Akhtar Y, Dakua SP, Abdalla A, et al. Risk assessment of computer-aided diagnostic software for hepatic resection. IEEE Trans Radiation Plasma Med Sci. 2021;6(6):667\u201377.","journal-title":"IEEE Trans Radiation Plasma Med Sci"},{"key":"1422_CR18","doi-asserted-by":"publisher","first-page":"1282536","DOI":"10.3389\/fonc.2023.1282536","volume":"13","author":"MY Ansari","year":"2023","unstructured":"Ansari MY, Qaraqe M, Righetti R, Serpedin E, Qaraqe K. Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound. Front Oncol. 2023;13:1282536.","journal-title":"Front Oncol"},{"issue":"1","key":"1422_CR19","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s12194-023-00776-5","volume":"17","author":"T Murata","year":"2024","unstructured":"Murata T, Hashimoto T, Onoguchi M, et al. Verification of image quality improvement of low-count bone scintigraphy using deep learning. Radiol Phys Technol. 2024;17(1):269\u201379.","journal-title":"Radiol Phys Technol"},{"key":"1422_CR20","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.ejmp.2022.06.006","volume":"100","author":"T Ito","year":"2022","unstructured":"Ito T, Maeno T, Tsuchikame H, et al. Adapting a low-count acquisition of the bone scintigraphy using deep denoising super-resolution convolutional neural network. Phys Med. 2022;100:18\u201325.","journal-title":"Phys Med"},{"issue":"6","key":"1422_CR21","doi-asserted-by":"publisher","first-page":"e13978","DOI":"10.1002\/acm2.13978","volume":"24","author":"S Ichikawa","year":"2023","unstructured":"Ichikawa S, Sugimori H, Ichijiri K, Yoshimura T, Nagaki A. Acquisition time reduction in pediatric (99m) Tc-DMSA planar imaging using deep learning. J Appl Clin Med Phys. 2023;24(6):e13978.","journal-title":"J Appl Clin Med Phys"},{"key":"1422_CR22","doi-asserted-by":"crossref","unstructured":"Pan Z, Qi N, Meng Q et al. Fast SPECT\/CT planar bone imaging enabled by deep learning enhancement. Med Phys 2024.","DOI":"10.1002\/mp.17094"},{"key":"1422_CR23","doi-asserted-by":"crossref","unstructured":"Ian G, Jean PA, Mehdi M et al. Generative Adversarial Networks. COMMUNICATIONS OF THE ACM. 2020, 63(11):139\u2013144.","DOI":"10.1145\/3422622"},{"key":"1422_CR24","unstructured":"Mehdi M. Nov. Conditional Generative Adversarial Nets. arXiv:1411.1784v1 [cs.LG] 6 2014."},{"key":"1422_CR25","unstructured":"Wang XT, Yu K, WuSX et al. ESRGAN: enhanced Super-resolution Generative Adversarial Networks. 4arXiv:1809.00219[cs.CV]. 17 Sep 2018."},{"key":"1422_CR26","doi-asserted-by":"publisher","first-page":"106478","DOI":"10.1016\/j.compbiomed.2022.106478","volume":"153","author":"MY Ansari","year":"2023","unstructured":"Ansari MY, Yang Y, Meher PK, Dakua SP. Dense-PSP-UNet: a neural network for fast inference liver ultrasound segmentation. Comput Biol Med. 2023;153:106478.","journal-title":"Comput Biol Med"},{"key":"1422_CR27","doi-asserted-by":"publisher","first-page":"24528","DOI":"10.1109\/ACCESS.2022.3154771","volume":"10","author":"S Mohanty","year":"2022","unstructured":"Mohanty S, Dakua SP. Toward computing cross-modality symmetric non-rigid medical image registration. IEEE Access. 2022;10:24528\u201339.","journal-title":"IEEE Access"},{"key":"1422_CR28","doi-asserted-by":"crossref","unstructured":"De Hond AAH, Steyerberg EW, Van Calster B. Interpreting area under the receiver operating characteristic curve. Lancet Digit Health. 2022, 4(12):e853-e855.","DOI":"10.1016\/S2589-7500(22)00188-1"},{"issue":"5","key":"1422_CR29","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1007\/s00259-023-06558-w","volume":"51","author":"A Bahloul","year":"2024","unstructured":"Bahloul A, Verger A, Lamash Y, et al. Ultra-fast whole-body bone tomoscintigraphies achieved with a high-sensitivity 360 degrees CZT camera and a dedicated deep-learning noise reduction algorithm. Eur J Nucl Med Mol Imaging. 2024;51(5):1215\u201320.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"3","key":"1422_CR30","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1053\/j.semnuclmed.2022.01.004","volume":"52","author":"P Ritt","year":"2022","unstructured":"Ritt P. Recent developments in SPECT\/CT. Semin Nucl Med. 2022;52(3):276\u201385.","journal-title":"Semin Nucl Med"},{"issue":"2","key":"1422_CR31","doi-asserted-by":"publisher","first-page":"298","DOI":"10.2967\/jnumed.119.226613","volume":"61","author":"D Minarik","year":"2020","unstructured":"Minarik D, Enqvist O, Tragardh E. Denoising of scintillation camera images using a deep convolutional neural network: a Monte Carlo Simulation Approach. J Nucl Med. 2020;61(2):298\u2013303.","journal-title":"J Nucl Med"},{"issue":"1","key":"1422_CR32","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13550-015-0127-x","volume":"5","author":"O Ardenfors","year":"2015","unstructured":"Ardenfors O, Svanholm U, Jacobsson H, et al. Reduced acquisition times in whole body bone scintigraphy using a noise-reducing Pixon(R)-algorithm-a qualitative evaluation study. EJNMMI Res. 2015;5(1):48.","journal-title":"EJNMMI Res"},{"key":"1422_CR33","doi-asserted-by":"crossref","unstructured":"Qi N, Pan B, Meng Q et al. Deep learning enhanced ultra-fast SPECT\/CT bone scan in patients with suspected malignancy: quantitative assessment and clinical performance. Phys Med Biol 2023, 68(13).","DOI":"10.1088\/1361-6560\/acddc6"},{"issue":"1","key":"1422_CR34","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s40658-022-00472-0","volume":"9","author":"B Pan","year":"2022","unstructured":"Pan B, Qi N, Meng Q, Wang J, et al. Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept. EJNMMI Phys. 2022;9(1):43.","journal-title":"EJNMMI Phys"},{"issue":"4","key":"1422_CR35","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1007\/s00259-022-06028-9","volume":"50","author":"JC Dickson","year":"2023","unstructured":"Dickson JC, Armstrong IS, Gabina PM, et al. EANM practice guideline for quantitative SPECT-CT. Eur J Nucl Med Mol Imaging. 2023;50(4):980\u201395.","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"3","key":"1422_CR36","doi-asserted-by":"publisher","first-page":"250","DOI":"10.2967\/jnmt.120.259168","volume":"49","author":"F Halim","year":"2021","unstructured":"Halim F, Yahya H, Jaafar KN, Mansor S. Accuracy Assessment of SUV measurements in SPECT\/CT: a Phantom Study. J Nucl Med Technol. 2021;49(3):250\u20135.","journal-title":"J Nucl Med Technol"},{"issue":"5","key":"1422_CR37","first-page":"262","volume":"6","author":"T 37 Kaneta","year":"2016","unstructured":"37 Kaneta T, Ogawa M, Daisaki H, et al. SUV measurement of normal vertebrae using SPECT\/CT with Tc-99m methylene diphosphonate. Am J Nucl Med Mol Imaging. 2016;6(5):262\u20138.","journal-title":"Am J Nucl Med Mol Imaging"},{"issue":"1","key":"1422_CR38","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s13550-016-0217-4","volume":"6","author":"M Beck","year":"2016","unstructured":"Beck M, Sanders JC, Ritt P, et al. Longitudinal analysis of bone metabolism using SPECT\/CT and (99m)Tc-diphosphono-propanedicarboxylic acid: comparison of visual and quantitative analysis. EJNMMI Res. 2016;6(1):60.","journal-title":"EJNMMI Res"},{"issue":"1","key":"1422_CR39","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s13550-019-0475-z","volume":"9","author":"S Arvola","year":"2019","unstructured":"Arvola S, Jambor I, Kuisma A, et al. Comparison of standardized uptake values between (99m)Tc-HDP SPECT\/CT and (18)F-NaF PET\/CT in bone metastases of breast and prostate cancer. EJNMMI Res. 2019;9(1):6.","journal-title":"EJNMMI Res"},{"issue":"1","key":"1422_CR40","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s12880-021-00569-5","volume":"21","author":"N Qi","year":"2021","unstructured":"Qi N, Meng Q, You Z, et al. Standardized uptake values of (99m)Tc-MDP in normal vertebrae assessed using quantitative SPECT\/CT for differentiation diagnosis of benign and malignant bone lesions. BMC Med Imaging. 2021;21(1):39.","journal-title":"BMC Med Imaging"},{"key":"1422_CR41","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jocs.2018.05.002","volume":"27","author":"M ZhaiXJ, Eslamib","year":"2018","unstructured":"ZhaiXJ, Eslamib M, Hussein ES, et al. Real-time automated image segmentation technique for cerebral aneurysm on reconfigurable system-on-chip. J Comput Sci. 2018;27:35\u201345.","journal-title":"J Comput Sci"},{"key":"1422_CR42","doi-asserted-by":"crossref","unstructured":"Zhai XJ, Amira A, Bensaali F et al. Zynq SoC based acceleration of the lattice boltzmann method. Concurrency Computation: Pract Experience 31.17 (2019): e5184.","DOI":"10.1002\/cpe.5184"},{"issue":"4","key":"1422_CR43","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s11548-020-02120-3","volume":"15","author":"SS Esfahani","year":"2020","unstructured":"Esfahani SS, Zhai X, Chen M, et al. Lattice-boltzmann interactive blood flow simulation pipeline. Int J Comput Assist Radiol Surg. 2020;15(4):629\u201339.","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"1422_CR44","doi-asserted-by":"crossref","unstructured":"Zhai SJ, Chen M, Esfahani ss et al. Heterogeneous system-on-chip-based Lattice-Boltzmann visual simulation system. IEEE Syst Journal14.2 (2019): 1592\u2013601.","DOI":"10.1109\/JSYST.2019.2952459"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01422-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01422-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01422-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T09:03:59Z","timestamp":1725872639000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01422-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1422"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01422-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4721424\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]},"assertion":[{"value":"11 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Institutional Review Board of Shanghai East Hospital. All patients have signed the informed consent forms.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human ethics and consent to participate declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"236"}}