{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:12:36Z","timestamp":1772791956666,"version":"3.50.1"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T00:00:00Z","timestamp":1754092800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018608","name":"High-end Foreign Experts Recruitment Plan of China","doi-asserted-by":"publisher","award":["G2023030025L"],"award-info":[{"award-number":["G2023030025L"]}],"id":[{"id":"10.13039\/501100018608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371221"],"award-info":[{"award-number":["62371221"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62201245"],"award-info":[{"award-number":["62201245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12,326,616"],"award-info":[{"award-number":["12,326,616"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.bspc.2025.108514","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T18:57:48Z","timestamp":1754506668000},"page":"108514","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["DuDoCS-Net: dual-domain and self-attention based CycleGAN for low-dose SPECT myocardial perfusion image enhancement"],"prefix":"10.1016","volume":"112","author":[{"given":"Songyi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Emma","family":"Bos","sequence":"additional","affiliation":[]},{"given":"Yeung","family":"Yam","sequence":"additional","affiliation":[]},{"given":"Anahita","family":"Tavoosi","sequence":"additional","affiliation":[]},{"given":"Gary R","family":"Small","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5376-1263","authenticated-orcid":false,"given":"R.Glenn","family":"Wells","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Benjamin J.W.","family":"Chow","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.bspc.2025.108514_b0005","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s00259-003-1344-5","article-title":"Myocardial perfusion scintigraphy: the evidence","volume":"31","author":"Underwood","year":"2004","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"11","key":"10.1016\/j.bspc.2025.108514_b0010","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1136\/heart.89.11.1291","article-title":"Role of myocardial perfusion imaging for risk stratification in suspected or known coronary artery disease","volume":"89","author":"Sabharwal","year":"2003","journal-title":"Heart"},{"issue":"1","key":"10.1016\/j.bspc.2025.108514_b0015","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1007\/s12350-018-1378-5","article-title":"Dose reduction is good but it is image quality that matters","volume":"27","author":"Wells","year":"2020","journal-title":"J. Nucl. Cardiol."},{"issue":"10","key":"10.1016\/j.bspc.2025.108514_b0020","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1016\/j.jcmg.2015.07.008","article-title":"Nationwide Laboratory Adherence to Myocardial Perfusion Imaging Radiation Dose Reduction Practices: a Report from the Intersocietal Accreditation Commission Data Repository","volume":"8","author":"Jerome","year":"2015","journal-title":"JACC Cardiovasc. Imaging"},{"issue":"12","key":"10.1016\/j.bspc.2025.108514_b0025","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCIMAGING.118.008383","article-title":"High Radiation Doses from SPECT Myocardial Perfusion Imaging in the United States","volume":"11","author":"Einstein","year":"2018","journal-title":"Circ. Cardiovasc. Imaging"},{"issue":"26","key":"10.1016\/j.bspc.2025.108514_b0030","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1093\/eurheartj\/ehv117","article-title":"Current worldwide nuclear cardiology practices and radiation exposure: results from the 65 country IAEA Nuclear Cardiology Protocols Cross-Sectional Study (INCAPS)","volume":"36","author":"Einstein","year":"2015","journal-title":"Eur. Heart J."},{"key":"10.1016\/j.bspc.2025.108514_b0035","article-title":"Recent advances in cardiac SPECT instrumentation and imaging methods","volume":"64(6):06TR01","author":"Wu","year":"2019","journal-title":"Phys. Med. Biol."},{"issue":"5","key":"10.1016\/j.bspc.2025.108514_b0040","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1007\/s12350-009-9108-7","article-title":"Wide beam reconstruction \u201cquarter-time\u201d gated myocardial perfusion SPECT functional imaging: a comparison to \u201cfull-time\u201d ordered subset expectation maximum","volume":"16","author":"DePuey","year":"2009","journal-title":"J. Nucl. Cardiol."},{"issue":"6","key":"10.1016\/j.bspc.2025.108514_b0045","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1007\/s12350-017-0920-1","article-title":"Investigation of dose reduction in cardiac perfusion SPECT via optimization and choice of the image reconstruction strategy","volume":"25","author":"Juan Ramon","year":"2018","journal-title":"J. Nucl. Cardiol."},{"issue":"4","key":"10.1016\/j.bspc.2025.108514_b0050","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1007\/s00259-009-1300-0","article-title":"New reconstruction algorithm allows shortened acquisition time for myocardial perfusion SPECT","volume":"37","author":"Valenta","year":"2010","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"3","key":"10.1016\/j.bspc.2025.108514_b0055","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1007\/s12350-015-0382-2","article-title":"Comparative analysis of full-time, half-time, and quarter-time myocardial ECG-gated SPECT quantification in normal-weight and overweight patients","volume":"24","author":"Lecchi","year":"2017","journal-title":"J. Nucl. Cardiol."},{"issue":"4","key":"10.1016\/j.bspc.2025.108514_b0060","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1002\/mp.14024","article-title":"Spatially guided nonlocal mean approach for denoising of PET images","volume":"47","author":"Arabi","year":"2020","journal-title":"Med. Phys."},{"issue":"2","key":"10.1016\/j.bspc.2025.108514_b0065","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s12350-015-0290-5","article-title":"D-SPECT: New technology, old tricks","volume":"23","author":"Kao","year":"2016","journal-title":"J. Nucl. Cardiol."},{"issue":"10","key":"10.1016\/j.bspc.2025.108514_b0070","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.21037\/qims-19-1036","article-title":"The clinical utilities of multi-pinhole single photon emission computed tomography","volume":"10","author":"Ozsahin","year":"2020","journal-title":"Quant. Imaging Med. Surg."},{"issue":"6","key":"10.1016\/j.bspc.2025.108514_b0075","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s12350-009-9137-2","article-title":"Novel solid-state-detector dedicated cardiac camera for fast myocardial perfusion imaging: multicenter comparison with standard dual detector cameras","volume":"16","author":"Esteves","year":"2009","journal-title":"J. Nucl. Cardiol."},{"issue":"4","key":"10.1016\/j.bspc.2025.108514_b0080","doi-asserted-by":"crossref","first-page":"635","DOI":"10.2967\/jnumed.108.060020","article-title":"A novel high-sensitivity rapid-acquisition single-photon cardiac imaging camera","volume":"50","author":"Gambhir","year":"2009","journal-title":"J. Nucl. Med."},{"issue":"7","key":"10.1016\/j.bspc.2025.108514_b0085","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for image Denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"10.1016\/j.bspc.2025.108514_b0090","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ab831a","article-title":"Low-dose CT with deep learning regularization via proximal forward-backward splitting","volume":"65","author":"Ding","year":"2020","journal-title":"Phys. Med. Biol."},{"issue":"2","key":"10.1016\/j.bspc.2025.108514_b0095","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1007\/s10278-019-00274-4","article-title":"Deep Learning for Low-Dose CT Denoising using Perceptual loss and Edge Detection Layer","volume":"33","author":"Gholizadeh-Ansari","year":"2020","journal-title":"J. Digit. Imaging"},{"issue":"5","key":"10.1016\/j.bspc.2025.108514_b0100","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s10278-018-0150-3","article-title":"Full-Dose PET image Estimation from Low-Dose PET image using Deep Learning: a pilot Study","volume":"32","author":"Kaplan","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.bspc.2025.108514_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101770","article-title":"Supervised learning with cyclegan for low-dose FDG PET image denoising","volume":"65","author":"Zhou","year":"2020","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.bspc.2025.108514_b0110","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1109\/TMI.2020.2979940","article-title":"Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging with Convolutional Denoising Networks","volume":"39","author":"Ramon","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2025.108514_b0115","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1002\/mp.14577","article-title":"Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging","volume":"48","author":"Liu","year":"2021","journal-title":"Med. Phys."},{"issue":"6","key":"10.1016\/j.bspc.2025.108514_b0120","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s12350-020-02119-y","article-title":"Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks","volume":"28","author":"Shiri","year":"2021","journal-title":"J. Nucl. Cardiol."},{"issue":"5","key":"10.1016\/j.bspc.2025.108514_b0125","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.1007\/s00259-021-05614-7","article-title":"Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance","volume":"49","author":"Aghakhan Olia","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"10.1016\/j.bspc.2025.108514_b0130","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"653","article-title":"Low-dose cardiac-gated spect studies using a residual convolutional neural network","author":"Song","year":"2019"},{"issue":"9","key":"10.1016\/j.bspc.2025.108514_b0135","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.2967\/jnumed.119.239327","article-title":"Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in image Space","volume":"61","author":"Sanaat","year":"2020","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.bspc.2025.108514_b0140","first-page":"2672","volume":"vol 2","author":"Goodfellow","year":"2014","journal-title":"Generative Adversarial Nets"},{"key":"10.1016\/j.bspc.2025.108514_b0145","unstructured":"M. Mirza and S. Osindero, \u201cConditional generative adversarial nets,\u201d Nov. 2014, Preprint. doi:10.48550\/arXiv.1411.1784."},{"key":"10.1016\/j.bspc.2025.108514_b0150","doi-asserted-by":"crossref","unstructured":"P. Isola, J. Zhu, T. Zhou and A. A. Efros, \u201cImage-to-image translation with conditional adversarial networks,\u201d in IEEE Conference on Computer Vision and Pattern Recognition. (CVPR), Jul. 21-26, 2017, pp. 5967-5976. doi: 10.1109\/CVPR.2017.632.","DOI":"10.1109\/CVPR.2017.632"},{"key":"10.1016\/j.bspc.2025.108514_b0155","doi-asserted-by":"crossref","unstructured":"J. Zhu, T. Park, P. Isola and A. A. Efros, \u201cUnpaired image-to-image translation using cycle-consistent adversarial networks,\u201d in IEEE International Conference on Computing Vision (ICCV). Venice, Italy, Oct. 22-29, 2017, pp. 2242-2251. doi: 10.1109\/ICCV.2017.244.","DOI":"10.1109\/ICCV.2017.244"},{"key":"10.1016\/j.bspc.2025.108514_b0160","unstructured":"X. Mao, C. Shen, and Y.-B. Yang, \u201cImage restoration using very deep convolutional encoder-decoder networks with symmetric skip connections,\u201d in Proc. Adv. Neural Inf. Process. Syst., 2016, pp. 2802\u20132."},{"key":"10.1016\/j.bspc.2025.108514_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103893","article-title":"Classification models for SPECT myocardial perfusion imaging","volume":"123","author":"Kaplan Berkaya","year":"2020","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.bspc.2025.108514_b0170","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1007\/s12350-022-03045-x","article-title":"Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT","volume":"30","author":"Sun","year":"2023","journal-title":"J. Nucl. Cardiol."},{"issue":"5","key":"10.1016\/j.bspc.2025.108514_b0175","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s10278-018-0056-0","article-title":"Sharpness-Aware Low-Dose CT Denoising using Conditional Generative Adversarial Network","volume":"31","author":"Yi","year":"2018","journal-title":"J. Digit. Imaging"},{"issue":"11","key":"10.1016\/j.bspc.2025.108514_b0180","doi-asserted-by":"crossref","first-page":"e735","DOI":"10.1002\/mp.13763","article-title":"Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233","volume":"46","author":"Samei","year":"2019","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2025.108514_b0185","series-title":"In: MEdical Image Computing and Computer-Assisted Intervention (MICCAI)","first-page":"424","article-title":"3d u-net: learning dense volumetric segmentation from sparse annotation","author":"Cicek","year":"2016"},{"issue":"4","key":"10.1016\/j.bspc.2025.108514_b0190","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.1609\/aaai.v34i04.6100","article-title":"Non-Local U-Nets for Biomedical image Segmentation","volume":"34","author":"Wang","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.bspc.2025.108514_b0195","first-page":"6000","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"In Advances in Neural Information Processing Systems"},{"issue":"2","key":"10.1016\/j.bspc.2025.108514_b0200","article-title":"TA-Net: Triple attention network for medical image segmentation","volume":"137","author":"Li","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2025.108514_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107267","article-title":"An automatic segmentation method with self-attention mechanism on left ventricle in gated PET\/CT myocardial perfusion imaging","volume":"229","author":"Zhang","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2025.108514_b0210","series-title":"Rectifier nonlinearities improve neural network acoustic models","author":"Maas","year":"2013"},{"key":"10.1016\/j.bspc.2025.108514_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102004","article-title":"Residual cyclegan for robust domain transformation of histopathological tissue slides","volume":"70","author":"de Bel","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2025.108514_b0220","doi-asserted-by":"crossref","unstructured":"X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang and S. P. Smolley, \u201cLeast squares generative adversarial networks,\u201d in IEEE International Conference on Computer Vision (ICCV). Venice, Italy, Oct. 22-29, 2017, pp. 2813-2821. doi: 10.1109\/ICCV.2017.304.","DOI":"10.1109\/ICCV.2017.304"},{"key":"10.1016\/j.bspc.2025.108514_b0225","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R. Y. K., & Wang, Z. (2016). Multi-class generative adversarial networks with the l2 loss function."},{"issue":"4","key":"10.1016\/j.bspc.2025.108514_b0230","doi-asserted-by":"crossref","first-page":"592","DOI":"10.2967\/jnumed.111.094946","article-title":"Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison","volume":"53","author":"Caroli","year":"2012","journal-title":"J. Nucl. Med."},{"issue":"9","key":"10.1016\/j.bspc.2025.108514_b0235","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.1109\/TMI.2019.2894692","article-title":"A universal intensity standardization method based on a many-to-one weak-paired cycle generative adversarial network for magnetic resonance images","volume":"38","author":"Gao","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.bspc.2025.108514_b0240","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/TMI.2021.3069874","article-title":"Unpaired stain transfer using pathology-consistent constrained generative adversarial networks","volume":"40","author":"Liu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2025.108514_b0245","unstructured":"L. Kong, C. Lian, D. Huang, Z. Li, Y. Hu, Q. Zhou, Breaking the dilemma of medical image-to-image translation. in Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), Oct, 2021."},{"key":"10.1016\/j.bspc.2025.108514_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103831","article-title":"A stability-enhanced cyclegan for effective domain transformation of unpaired ultrasound images","author":"Huang","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2025.108514_b0255","series-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","first-page":"234","author":"Ronneberger","year":"2015"},{"issue":"12","key":"10.1016\/j.bspc.2025.108514_b0260","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1093\/bioinformatics\/bty923","article-title":"Computational modeling of cellular structures using conditional deep generative networks","volume":"35","author":"Yuan","year":"2018","journal-title":"Bioinformatics"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809425010250?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809425010250?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:22:20Z","timestamp":1772781740000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809425010250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":52,"alternative-id":["S1746809425010250"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2025.108514","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DuDoCS-Net: dual-domain and self-attention based CycleGAN for low-dose SPECT myocardial perfusion image enhancement","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2025.108514","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"108514"}}