{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T12:18:56Z","timestamp":1783167536656,"version":"3.54.6"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100018628","name":"Scientific Research Foundation of Education Department of Anhui Province of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018628","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171177"],"award-info":[{"award-number":["62171177"]}],"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":["62101173"],"award-info":[{"award-number":["62101173"]}],"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":["62071165"],"award-info":[{"award-number":["62071165"]}],"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":["62541109"],"award-info":[{"award-number":["62541109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002947","name":"Anhui Medical University","doi-asserted-by":"publisher","award":["2023xkj017"],"award-info":[{"award-number":["2023xkj017"]}],"id":[{"id":"10.13039\/501100002947","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010814","name":"Anhui Province Department of Education","doi-asserted-by":"publisher","award":["2024AH050696"],"award-info":[{"award-number":["2024AH050696"]}],"id":[{"id":"10.13039\/501100010814","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":[[2026,10]]},"DOI":"10.1016\/j.bspc.2026.110736","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:31:15Z","timestamp":1780435875000},"page":"110736","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["3DMam-U2GAN: High-quality reconstruction of ultrasound power Doppler images using sparse data"],"prefix":"10.1016","volume":"125","author":[{"given":"Yan","family":"Fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7196-8903","authenticated-orcid":false,"given":"Yuanguo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihui","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chichao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1442-8142","authenticated-orcid":false,"given":"Yadan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.bspc.2026.110736_b1","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1148\/radiology.190.3.8115639","article-title":"Power doppler us: a potentially useful alternative to mean frequency-based color doppler us","volume":"190","author":"Rubin","year":"1994","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.bspc.2026.110736_b2","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/TUFFC.2014.2882","article-title":"Ultrafast imaging in biomedical ultrasound","volume":"61","author":"Tanter","year":"2014","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"7579","key":"10.1016\/j.bspc.2026.110736_b3","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1038\/nature16066","article-title":"Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging","volume":"527","author":"Errico","year":"2015","journal-title":"Nature"},{"issue":"2","key":"10.1016\/j.bspc.2026.110736_b4","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/TMI.2014.2359650","article-title":"In vivo acoustic super-resolution and super-resolved velocity mapping using microbubbles","volume":"34","author":"Christensen-Jeffries","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.bspc.2026.110736_b5","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/58.585126","article-title":"Clutter rejection filters in color flow imaging: A theoretical approach","volume":"44","author":"Torp","year":"2002","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"2","key":"10.1016\/j.bspc.2026.110736_b6","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/58.985705","article-title":"Clutter filter design for ultrasound color flow imaging","volume":"49","author":"S. Bj\u00e6rum","year":"2002","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"3","key":"10.1016\/j.bspc.2026.110736_b7","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/BF02458043","article-title":"Cardiac doppler blood-flow signal analysis: Part 2 time\/frequency representation based on autoregressive modelling","volume":"31","author":"Guo","year":"1993","journal-title":"Med. Biol. Eng. Comput."},{"issue":"4","key":"10.1016\/j.bspc.2026.110736_b8","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"issue":"11","key":"10.1016\/j.bspc.2026.110736_b9","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1109\/TMI.2015.2428634","article-title":"Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity","volume":"34","author":"C. Demen\u00e9","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.bspc.2026.110736_b10","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1109\/TUFFC.2017.2719942","article-title":"Expanding acquisition and clutter filter dimensions for improved perfusion sensitivity","volume":"64","author":"Kim","year":"2017","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"1","key":"10.1016\/j.bspc.2026.110736_b11","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/TMI.2016.2605819","article-title":"Ultrasound small vessel imaging with block-wise adaptive local clutter filtering","volume":"36","author":"Song","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.bspc.2026.110736_b12","doi-asserted-by":"crossref","first-page":"1574","DOI":"10.1109\/TMI.2018.2789499","article-title":"Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors","volume":"37","author":"Baranger","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.bspc.2026.110736_b13","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/TUFFC.2023.3289235","article-title":"Fast thresholding of svd clutter filter using the spatial similarity matrix and a sum-table algorithm","volume":"70","author":"Baranger","year":"2023","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"5","key":"10.1016\/j.bspc.2026.110736_b14","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1109\/TUFFC.2023.3253257","article-title":"High-quality ultrafast power doppler imaging based on spatial angular coherence factor","volume":"70","author":"Huang","year":"2023","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"10.1016\/j.bspc.2026.110736_b15","article-title":"Computationally efficient svd filtering for ultrasound flow imaging and real-time application to ultrafast doppler","author":"Pialot","year":"2024","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.110736_b16","series-title":"Int. Conf. Image Anal. Recognit.","first-page":"473","article-title":"Auto svd clutter filtering for us doppler imaging using 3d clustering algorithm","author":"Waraich","year":"2019"},{"issue":"3","key":"10.1016\/j.bspc.2026.110736_b17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust principal component analysis?","volume":"58","author":"E. Cand\u00e8s","year":"2011","journal-title":"J. ACM"},{"issue":"4","key":"10.1016\/j.bspc.2026.110736_b18","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1109\/TMI.2019.2941865","article-title":"Low rank and sparse decomposition of ultrasound color flow images for suppressing clutter in real-time","volume":"39","author":"Ashikuzzaman","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.bspc.2026.110736_b19","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1109\/TUFFC.2022.3180053","article-title":"Randomized spatial downsampling-based cauchy-rpca clutter filtering for high-resolution ultrafast ultrasound microvasculature imaging and functional imaging","volume":"69","author":"Sui","year":"2022","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"4","key":"10.1016\/j.bspc.2026.110736_b20","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TMI.2019.2941271","article-title":"Deep unfolded robust pca with application to clutter suppression in ultrasound","volume":"39","author":"Solomon","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110736_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102664","article-title":"Ultra-fast ultrasound blood flow velocimetry for carotid artery with deep learning","volume":"144","author":"He","year":"2023","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110736_b22","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.ultrasmedbio.2022.08.016","article-title":"Perceptflow: real-time ultrafast doppler image enhancement using deep convolutional neural network and perceptual loss","volume":"49","author":"Blons","year":"2023","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.bspc.2026.110736_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2019.101608","article-title":"On the truncation of time frequency distributions to improve the computational performance in the estimation of fundamental parameters of a doppler ultrasound blood flow signal","volume":"54","author":"Rubio-Acosta","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110736_b24","article-title":"Deep learning-based middle cerebral artery blood flow abnormality detection using flow velocity waveform derived from transcranial doppler ultrasound","volume":"85","author":"Podder","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"7","key":"10.1016\/j.bspc.2026.110736_b25","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1109\/TMI.2022.3148728","article-title":"Deep-fus: A deep learning platform for functional ultrasound imaging of the brain using sparse data","volume":"41","author":"Ianni","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.bspc.2026.110736_b26","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1109\/TUFFC.2023.3304527","article-title":"Contrast-free super-resolution power doppler (cs-pd) based on deep neural networks","volume":"70","author":"You","year":"2023","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"10.1016\/j.bspc.2026.110736_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109133","article-title":"Crnn-refined spatiotemporal transformer for dynamic mri reconstruction","volume":"182","author":"Wang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110736_b28","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"3","article-title":"Learning dynamic mri reconstruction with convolutional network assisted reconstruction swin transformer","author":"Xu","year":"2023"},{"key":"10.1016\/j.bspc.2026.110736_b29","series-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023"},{"key":"10.1016\/j.bspc.2026.110736_b30","series-title":"U-mamba: enhancing long-range dependency for biomedical image segmentation","author":"Ma","year":"2024"},{"key":"10.1016\/j.bspc.2026.110736_b31","article-title":"Vm-unet: Vision mamba unet for medical image segmentation","author":"Ruan","year":"2024","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.bspc.2026.110736_b32","series-title":"Proceedings of the Asian Conference on Computer Vision","first-page":"548","article-title":"Log-vmamba: local\u2013global vision mamba for medical image segmentation","author":"Dang","year":"2024"},{"key":"10.1016\/j.bspc.2026.110736_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-net: Going deeper with nested u-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.bspc.2026.110736_b34","first-page":"11908","article-title":"Ffa-net: Feature fusion attention network for single image dehazing","volume":"Vol. 34","author":"Qin","year":"2020"},{"key":"10.1016\/j.bspc.2026.110736_b35","doi-asserted-by":"crossref","DOI":"10.1145\/3757324","article-title":"Mambavesselnet++: A hybrid cnn-mamba architecture for medical image segmentation","author":"Xu","year":"2025","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.bspc.2026.110736_b36","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"5967","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"key":"10.1016\/j.bspc.2026.110736_b37","series-title":"The relativistic discriminator: a key element missing from standard gan","author":"Jolicoeur-Martineau","year":"2018"},{"key":"10.1016\/j.bspc.2026.110736_b38","series-title":"Hdmba: hyperspectral remote sensing imagery dehazing with state space model","author":"Fu","year":"2024"},{"key":"10.1016\/j.bspc.2026.110736_b39","series-title":"Proc. Eur. Conf. Comput. Vis.","first-page":"3","article-title":"Cbam: Convolutional block attention module","author":"Woo","year":"2018"},{"key":"10.1016\/j.bspc.2026.110736_b40","doi-asserted-by":"crossref","first-page":"107547","DOI":"10.52202\/079017-3417","article-title":"Xlstm: Extended long short-term memory","volume":"37","author":"Beck","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110736_b41","series-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"10.1016\/j.bspc.2026.110736_b42","article-title":"F-gan: Training generative neural samplers using variational divergence minimization","volume":"29","author":"Nowozin","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110736_b43","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"4401","article-title":"A style-based generator architecture for generative adversarial networks","author":"Karras","year":"2019"},{"key":"10.1016\/j.bspc.2026.110736_b44","series-title":"Int. Conf. Mach. Learn.","first-page":"3481","article-title":"Which training methods for gans do actually converge?","author":"Mescheder","year":"2018"},{"key":"10.1016\/j.bspc.2026.110736_b45","doi-asserted-by":"crossref","first-page":"44177","DOI":"10.52202\/079017-1402","article-title":"The gan is dead; long live the gan! a modern gan baseline","volume":"37","author":"Huang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110736_b46","series-title":"Gradient penalty from a maximum margin perspective","author":"Jolicoeur-Martineau","year":"2019"},{"issue":"11","key":"10.1016\/j.bspc.2026.110736_b47","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"10.1016\/j.bspc.2026.110736_b48","first-page":"1398","article-title":"Multiscale structural similarity for image quality assessment","volume":"Vol. 2","author":"Wang","year":"2003"},{"key":"10.1016\/j.bspc.2026.110736_b49","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"4681","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","author":"Ledig","year":"2017"},{"key":"10.1016\/j.bspc.2026.110736_b50","series-title":"Eur. Conf. Comput. Vis.","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"Johnson","year":"2016"},{"key":"10.1016\/j.bspc.2026.110736_b51","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110736_b52","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"2794","article-title":"Least squares generative adversarial networks","author":"Mao","year":"2017"},{"key":"10.1016\/j.bspc.2026.110736_b53","doi-asserted-by":"crossref","DOI":"10.1038\/s41551-021-00824-8","article-title":"Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy","author":"Heiles","year":"2022","journal-title":"Nat. Biomed. Eng."},{"issue":"9","key":"10.1016\/j.bspc.2026.110736_b54","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1109\/TUFFC.2020.2988164","article-title":"Deep learning of spatiotemporal filtering for fast super-resolution ultrasound imaging","volume":"67","author":"Brown","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"7","key":"10.1016\/j.bspc.2026.110736_b55","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/abeb31","article-title":"Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network","volume":"66","author":"Lok","year":"2021","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110736_b56","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1177\/1742271X16689347","article-title":"The feasibility of implementation of ultrasound equipment standards set by united kingdom professional bodies","volume":"25","author":"Dudley","year":"2017","journal-title":"Ultrasound"},{"issue":"4","key":"10.1016\/j.bspc.2026.110736_b57","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012905?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012905?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:47:06Z","timestamp":1783165626000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426012905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":57,"alternative-id":["S1746809426012905"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110736","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"3DMam-UGAN: High-quality reconstruction of ultrasound power Doppler images using sparse data","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110736","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110736"}}