{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:51:40Z","timestamp":1757627500347,"version":"3.44.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant 62471297"],"award-info":[{"award-number":["Grant 62471297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["Grant 2023YFC2411401"],"award-info":[{"award-number":["Grant 2023YFC2411401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Jiao Tong University Medical Engineering Cross Research Funds","award":["Grant YG2021ZD05"],"award-info":[{"award-number":["Grant YG2021ZD05"]}]},{"name":"Shanghai Zhangjiang National Independent Innovation Demonstration Zone Special Development Fund Major Project","award":["Grant ZJ2021-ZD-007"],"award-info":[{"award-number":["Grant ZJ2021-ZD-007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01910-y","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T14:35:21Z","timestamp":1756737321000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sliding volume-based streak artifact reduction network (S-STAR Net) for ultra-sparse-view computed tomography"],"prefix":"10.1186","volume":"25","author":[{"given":"Shiang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziheng","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"issue":"2","key":"1910_CR1","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1109\/TMI.2014.2365179","volume":"34","author":"JH Cho","year":"2015","unstructured":"Cho JH, Fessler JA. Regularization designs for uniform Spatial resolution and noise properties in statistical image reconstruction for 3D X-ray CT. IEEE Trans Med Imaging. 2015;34(2):678\u201389.","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"1910_CR2","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/JPROC.2019.2936204","volume":"108","author":"S Ravishankar","year":"2020","unstructured":"Ravishankar S, Ye JC, Fessler JA. Image reconstruction: from sparsity to data-adaptive methods and machine learning. Proc IEEE Inst Electr Electron Eng. 2020;108(1):86\u2013109.","journal-title":"Proc IEEE Inst Electr Electron Eng"},{"key":"1910_CR3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/TCI.2022.3225680","volume":"9","author":"A Lahiri","year":"2023","unstructured":"Lahiri A, Maliakal G, Klasky ML, et al. Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data. IEEE Trans Comput Imaging. 2023;9:13\u201328.","journal-title":"IEEE Trans Comput Imaging"},{"key":"1910_CR4","doi-asserted-by":"publisher","first-page":"8914","DOI":"10.1109\/ACCESS.2016.2624938","volume":"4","author":"G Wang","year":"2016","unstructured":"Wang G. A perspective on deep imaging. IEEE Access. 2016;4:8914\u201324.","journal-title":"IEEE Access"},{"issue":"1","key":"1910_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1148\/radiol.2231012100","volume":"223","author":"TL Slovis","year":"2002","unstructured":"Slovis TL. The ALARA concept in pediatric CT: myth or reality. Radiology. 2002;223(1):5\u20136.","journal-title":"Radiology"},{"key":"1910_CR6","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1109\/TCI.2023.3240078","volume":"9","author":"T Cheslerean-Boghiu","year":"2023","unstructured":"Cheslerean-Boghiu T, Hofmann FC, Schulthei\u00df M, et al. Wnet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer. IEEE Trans Comput Imaging. 2023;9:120\u201332.","journal-title":"IEEE Trans Comput Imaging"},{"key":"1910_CR7","doi-asserted-by":"crossref","unstructured":"Ulyanov D, Vedaldi A, Lempitsky V. Deep image prior. in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2018;9446\u20139454.","DOI":"10.1109\/CVPR.2018.00984"},{"key":"1910_CR8","doi-asserted-by":"crossref","unstructured":"Shu Z, Pan Z. SDIP: Self-reinforcement deep image prior framework for image processing. ArXiv: 240412142 (2024).","DOI":"10.1016\/j.patcog.2025.111786"},{"key":"1910_CR9","doi-asserted-by":"publisher","first-page":"106740","DOI":"10.1016\/j.neunet.2024.106740","volume":"180","author":"Z Shu","year":"2024","unstructured":"Shu Z, Entezari A. RBP-DIP: residual back projection with deep image prior for ill-posed CT reconstruction. Neural Netw. 2024;180:106740.","journal-title":"Neural Netw"},{"key":"1910_CR10","doi-asserted-by":"publisher","first-page":"107167","DOI":"10.1016\/j.cmpb.2022.107167","volume":"226","author":"Z Shu","year":"2022","unstructured":"Shu Z, Entezari A. Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior. Comput Meth Prog Bio. 2022;226:107167.","journal-title":"Comput Meth Prog Bio"},{"issue":"9","key":"1910_CR11","doi-asserted-by":"publisher","first-page":"094004","DOI":"10.1088\/1361-6420\/aba415","volume":"36","author":"DO Baguer","year":"2020","unstructured":"Baguer DO, Leuschner J, Schmidt M. Computed tomography reconstruction using deep image prior and learned reconstruction methods. Inverse Probl. 2020;36(9):094004.","journal-title":"Inverse Probl"},{"issue":"1","key":"1910_CR12","doi-asserted-by":"publisher","first-page":"6700","DOI":"10.1038\/s41598-018-25153-w","volume":"8","author":"S Xie","year":"2018","unstructured":"Xie S, Zheng X, Chen Y, et al. Artifact removal using improved GoogLeNet for sparse-view CT reconstruction. Sci Rep. 2018;8(1):6700.","journal-title":"Sci Rep"},{"issue":"9","key":"1910_CR13","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","volume":"26","author":"KH Jin","year":"2017","unstructured":"Jin KH, McCann MT, Froustey E, et al. Deep convolutional neural network for inverse problems in imaging. IEEE Trans Image Process. 2017;26(9):4509\u201322.","journal-title":"IEEE Trans Image Process"},{"key":"1910_CR14","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.ejmp.2020.11.021","volume":"80","author":"M Lee","year":"2020","unstructured":"Lee M, Kim H, Kim H-J. Sparse-view CT reconstruction based on multi-level wavelet Convolution neural network. Med Phys. 2020;80:352\u201362.","journal-title":"Med Phys"},{"issue":"6","key":"1910_CR15","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TMI.2018.2823768","volume":"37","author":"Y Han","year":"2018","unstructured":"Han Y, Ye JC. Framing U-Net via deep convolutional framelets: application to sparse-view CT. IEEE Trans Med Imaging. 2018;37(6):1418\u201329.","journal-title":"IEEE Trans Med Imaging"},{"key":"1910_CR16","doi-asserted-by":"publisher","first-page":"106888","DOI":"10.1016\/j.compbiomed.2023.106888","volume":"161","author":"Y Chan","year":"2023","unstructured":"Chan Y, Liu X, Wang T, et al. An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction. Comput Biol Med. 2023;161:106888.","journal-title":"Comput Biol Med"},{"key":"1910_CR17","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T et al. Image-to-image translation with conditional adversarial networks, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2017;1125\u20131134.","DOI":"10.1109\/CVPR.2017.632"},{"key":"1910_CR18","unstructured":"Mirza M, Osindero S. Conditional Generative Adversarial Nets arXiv:14111784 (2014)."},{"key":"1910_CR19","doi-asserted-by":"crossref","unstructured":"Wang Z, Cun X, Bao J et al. Uformer: A general U-shaped transformer for image restoration, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2022;17683\u201317693.","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"1910_CR20","unstructured":"Vaswani A, Shazeer N, Parmar N et al. Attention is all you need, in proc. Conf. Neural Inf. Process. Syst. 2017;6000\u20136010."},{"issue":"6","key":"1910_CR21","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1109\/TMI.2018.2823338","volume":"37","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liang X, Dong X, et al. A sparse-view CT reconstruction method based on combination of densenet and Deconvolution. IEEE Trans Med Imaging. 2018;37(6):1407\u201317.","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"1910_CR22","doi-asserted-by":"publisher","first-page":"5271","DOI":"10.21037\/qims-22-1384","volume":"13","author":"Z Xia","year":"2023","unstructured":"Xia Z, Liu J, Kang Y, et al. Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging. Quant Imaging Med Surg. 2023;13(8):5271\u201393.","journal-title":"Quant Imaging Med Surg"},{"issue":"12","key":"1910_CR23","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen H, Zhang Y, Kalra MK, et al. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging. 2017;36(12):2524\u201335.","journal-title":"IEEE Trans Med Imaging"},{"key":"1910_CR24","doi-asserted-by":"publisher","first-page":"24698","DOI":"10.1109\/ACCESS.2017.2766438","volume":"5","author":"W Yang","year":"2017","unstructured":"Yang W, Zhang H, Yang J, et al. Improving low-dose CT image using residual convolutional network. IEEE Access. 2017;5:24698\u2013705.","journal-title":"IEEE Access"},{"issue":"3","key":"1910_CR25","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1109\/TMI.2019.2933425","volume":"39","author":"H Liao","year":"2019","unstructured":"Liao H, Lin WA, Zhou SK, et al. ADN: artifact disentanglement network for unsupervised metal artifact reduction. IEEE Trans Med Imaging. 2019;39(3):634\u201343.","journal-title":"IEEE Trans Med Imaging"},{"key":"1910_CR26","doi-asserted-by":"crossref","unstructured":"Sarkissian HD, Lucka F, Eijnatten M et al. A cone-beam X-ray computed tomography data collection designed for machine learning. Sci Data 6 2019.","DOI":"10.1038\/s41597-019-0235-y"},{"key":"1910_CR27","doi-asserted-by":"crossref","unstructured":"Chilamkurthy S, Ghosh R, Tanamala S et al. Development and validation of deep learning algorithms for detection of critical findings in head CT scans. ArXiv:180305854 (2018).","DOI":"10.1016\/S0140-6736(18)31645-3"},{"key":"1910_CR28","doi-asserted-by":"publisher","first-page":"103831","DOI":"10.1016\/j.bspc.2022.103831","volume":"77","author":"L Huang","year":"2022","unstructured":"Huang L, Zhou Z, Guo Y, et al. A stability-enhanced cyclegan for effective domain transformation of unpaired ultrasound images, biomed. Signal Process Control. 2022;77:103831.","journal-title":"Signal Process Control"},{"key":"1910_CR29","doi-asserted-by":"crossref","unstructured":"Li C, Wand M. Precomputed real-time texture synthesis with markovian generative adversarial networks, in Proc. Eur. Conf. Comput. Vis. 2016;702\u2013716.","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"1910_CR30","doi-asserted-by":"crossref","unstructured":"Misra D, Nalamada T, Arasanipalai AU et al. Rotate to attend: Convolutional triplet attention module, in Proc. IEEE Winter Conf. Appl. Comput. Vis. 2021;3139\u20133148.","DOI":"10.1109\/WACV48630.2021.00318"},{"issue":"6","key":"1910_CR31","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1364\/JOSAA.1.000612","volume":"1","author":"LA Feldkamp","year":"1984","unstructured":"Feldkamp LA, Davis LC, Kress JW. Practical cone beam algorithm. J Opt Soc Am A. 1984;1(6):612\u20139.","journal-title":"J Opt Soc Am A"},{"key":"1910_CR32","unstructured":"Chi L, Jiang B, Mu Y. Fast Fourier convolution, in Proc. Adv. Neural Inf. Process. Syst. 2020;4479\u20134488."},{"key":"1910_CR33","doi-asserted-by":"crossref","unstructured":"Suvorov R, Logacheva E, Mashikhin A et al. Resolution-robust large mask inpainting with Fourier convolutions, in Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis. 2022;3172\u20133182.","DOI":"10.1109\/WACV51458.2022.00323"},{"issue":"5","key":"1910_CR34","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.1109\/TMI.2024.3351722","volume":"43","author":"Z Li","year":"2024","unstructured":"Li Z, Gao Q, Wang Y, et al. Quad-Net: Quad-Domain network for CT metal artifact reduction. IEEE Trans Med Imaging. 2024;43(5):1866\u201379.","journal-title":"IEEE Trans Med Imaging"},{"key":"1910_CR35","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks, in Proc. Int. Conf. Comput. Vis. 2017;2223\u20132232.","DOI":"10.1109\/ICCV.2017.244"},{"issue":"1","key":"1910_CR36","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2017","unstructured":"Zhao H, Gallo O, Frosio I, et al. Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging. 2017;3(1):47\u201357.","journal-title":"IEEE Trans Comput Imaging"},{"key":"1910_CR37","doi-asserted-by":"crossref","unstructured":"Hor\u00e9 A, Ziou D. Image Quality Metrics: PSNR vs. SSIM, in Proc. Int. Conf. Pattern Recognit. 2010;2366\u20132369.","DOI":"10.1109\/ICPR.2010.579"},{"issue":"10","key":"1910_CR38","doi-asserted-by":"publisher","first-page":"2925","DOI":"10.1109\/TMI.2022.3174827","volume":"41","author":"X Zhang","year":"2021","unstructured":"Zhang X, He X, Guo J, et al. PTNet3D: A 3D High-Resolution longitudinal infant brain MRI synthesizer based on Transformers. IEEE Trans Med Imaging. 2021;41(10):2925\u201340.","journal-title":"IEEE Trans Med Imaging"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01910-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01910-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01910-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T03:28:56Z","timestamp":1757474936000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01910-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1910"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01910-y","relation":{},"ISSN":["1471-2342"],"issn-type":[{"type":"electronic","value":"1471-2342"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"13 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"364"}}