{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:08:19Z","timestamp":1773965299728,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["60974042"],"award-info":[{"award-number":["60974042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01254-z","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T18:03:07Z","timestamp":1726077787000},"page":"1245-1264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention"],"prefix":"10.1007","volume":"38","author":[{"given":"Ju","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Lieli","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Weiwei","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Mingyang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Guangyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"issue":"22","key":"1254_CR1","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1056\/NEJMra072149","volume":"357","author":"DJ Brenner","year":"2007","unstructured":"Brenner D J, Hall E J. Computed tomography\u2014an increasing source of radiation exposure[J]. New England journal of medicine, 2007, 357(22): 2277-2284.","journal-title":"New England journal of medicine"},{"issue":"1","key":"1254_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.annemergmed.2010.10.018","volume":"58","author":"BM Baumann","year":"2011","unstructured":"Baumann B M, Chen E H, Mills A M, et al. Patient perceptions of computed tomographic imaging and their understanding of radiation risk and exposure[J]. Annals of Emergency Medicine, 2011, 58(1): 1-7. e2.","journal-title":"Annals of Emergency Medicine"},{"issue":"11","key":"1254_CR3","doi-asserted-by":"publisher","first-page":"4911","DOI":"10.1118\/1.3232004","volume":"36","author":"A Manduca","year":"2009","unstructured":"Manduca A, Yu L, Trzasko J D, et al. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT[J]. Medical physics, 2009, 36(11): 4911-4919.","journal-title":"Medical physics"},{"issue":"6","key":"1254_CR4","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1109\/TMI.2012.2187213","volume":"31","author":"M Balda","year":"2012","unstructured":"Balda M, Hornegger J, Heismann B. Ray contribution masks for structure adaptive sinogram filtering[J]. IEEE transactions on medical imaging, 2012, 31(6): 1228-1239.","journal-title":"IEEE transactions on medical imaging"},{"issue":"23","key":"1254_CR5","doi-asserted-by":"publisher","first-page":"7923","DOI":"10.1088\/0031-9155\/57\/23\/7923","volume":"57","author":"Y Liu","year":"2012","unstructured":"Liu Y, Ma J, Fan Y, et al. Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction[J]. Physics in Medicine & Biology, 2012, 57(23): 7923.","journal-title":"Physics in Medicine & Biology"},{"issue":"22","key":"1254_CR6","doi-asserted-by":"publisher","first-page":"7519","DOI":"10.1088\/0031-9155\/57\/22\/7519","volume":"57","author":"J Ma","year":"2012","unstructured":"Ma J, Zhang H, Gao Y, et al. Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior[J]. Physics in Medicine & Biology, 2012, 57(22): 7519.","journal-title":"Physics in Medicine & Biology"},{"issue":"9","key":"1254_CR7","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1109\/TMI.2012.2195669","volume":"31","author":"Q Xu","year":"2012","unstructured":"Xu Q, Yu H, Mou X, et al. Low-dose X-ray CT reconstruction via dictionary learning[J]. IEEE transactions on medical imaging, 2012, 31(9): 1682-1697.","journal-title":"IEEE transactions on medical imaging"},{"issue":"1","key":"1254_CR8","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TMI.2016.2600249","volume":"36","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Mou X, Wang G, et al. Tensor-based dictionary learning for spectral CT reconstruction[J]. IEEE transactions on medical imaging, 2016, 36(1): 142-154.","journal-title":"IEEE transactions on medical imaging"},{"issue":"7","key":"1254_CR9","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian denoiser: Residual learning of deep cnn for image denoising[J]. IEEE transactions on image processing, 2017, 26(7): 3142-3155.","journal-title":"IEEE transactions on image processing"},{"key":"1254_CR10","doi-asserted-by":"crossref","unstructured":"Guo S, Yan Z, Zhang K, et al. Toward convolutional blind denoising of real photographs[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2019: 1712\u20131722.","DOI":"10.1109\/CVPR.2019.00181"},{"issue":"9","key":"1254_CR11","doi-asserted-by":"publisher","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","volume":"27","author":"K Zhang","year":"2018","unstructured":"Zhang K, Zuo W, Zhang L. FFDNet: Toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4608-4622.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1254_CR12","doi-asserted-by":"crossref","unstructured":"Anwar S, Barnes N. Real image denoising with feature attention[C]\/\/Proceedings of the IEEE\/CVF international conference on computer vision. 2019: 3155\u20133164.","DOI":"10.1109\/ICCV.2019.00325"},{"key":"1254_CR13","unstructured":"Vaswani A, Shazier N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30."},{"key":"1254_CR14","unstructured":"Dosovitskiy, Alexey , et al. \"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.\" International Conference on Learning Representations 2021."},{"key":"1254_CR15","doi-asserted-by":"crossref","unstructured":"Yuan L, Chen Y, Wang T, et al. Tokens-to-token vit: Training vision transformers from scratch on imagenet[C]\/\/Proceedings of the IEEE\/CVF international conference on computer vision. 2021: 558\u2013567.","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"1254_CR16","doi-asserted-by":"crossref","unstructured":"Tu Z, Talebi H, Zhang H, et al. Maxvit: Multi-axis vision transformer[C]\/\/European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 459\u2013479.","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"1254_CR17","doi-asserted-by":"crossref","unstructured":"Pan J, Liu S, Sun D, et al. Learning dual convolutional neural networks for low-level vision[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3070\u20133079.","DOI":"10.1109\/CVPR.2018.00324"},{"key":"1254_CR18","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.neunet.2019.08.022","volume":"121","author":"C Tian","year":"2020","unstructured":"Tian C, Xu Y, Zuo W. Image denoising using deep CNN with batch renormalization[J]. Neural Networks, 2020, 121: 461-473.","journal-title":"Neural Networks"},{"key":"1254_CR19","doi-asserted-by":"publisher","first-page":"110291","DOI":"10.1016\/j.patcog.2024.110291","volume":"149","author":"W Wu","year":"2024","unstructured":"Wu W, Liu S, Xia Y, et al. Dual residual attention network for image denoising[J]. Pattern Recognition, 2024, 149: 110291.","journal-title":"Pattern Recognition"},{"issue":"12","key":"1254_CR20","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 M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE transactions on medical imaging, 2017, 36(12): 2524-2535.","journal-title":"IEEE transactions on medical imaging"},{"issue":"6","key":"1254_CR21","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","volume":"37","author":"Q Yang","year":"2018","unstructured":"Yang Q, Yan P, Zhang Y, et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss[J]. IEEE transactions on medical imaging, 2018, 37(6): 1348-1357.","journal-title":"IEEE transactions on medical imaging"},{"key":"1254_CR22","doi-asserted-by":"crossref","unstructured":"Yun S, Choi J, Yoo Y, et al. Action-decision networks for visual tracking with deep reinforcement learning[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2711\u20132720.","DOI":"10.1109\/CVPR.2017.148"},{"key":"1254_CR23","first-page":"1","volume":"71","author":"Z Huang","year":"2021","unstructured":"Huang Z, Zhang J, Zhang Y, et al. DU-GAN: Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 71: 1-12.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1254_CR24","doi-asserted-by":"crossref","unstructured":"Zhang Z, Yu L, Liang X, et al. TransCT: dual-path transformer for low dose computed tomography[C]\/\/Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part VI 24. Springer International Publishing, 2021: 55-64","DOI":"10.1007\/978-3-030-87231-1_6"},{"key":"1254_CR25","doi-asserted-by":"crossref","unstructured":"Yang L, Li Z, Ge R,et al. Low-Dose CT Denoising via Sinogram Inner-Structure Transformer[J].IEEE Transactions on Medical Imaging, 2023.","DOI":"10.1109\/TMI.2022.3219856"},{"key":"1254_CR26","doi-asserted-by":"crossref","unstructured":"Zhu L, Han Y, Xi X, et al. STEDNet: Swin transformer\u2010based encoder-decoder network for noise reduction in low\u2010dose CT[J]. Medical Physics, 2023.","DOI":"10.1002\/mp.16249"},{"key":"1254_CR27","doi-asserted-by":"crossref","unstructured":"Wu Z, Zhong X, Lyv T, et al. Deep Dual-domain United Guiding Learning with Global-Local Transformer-Convolution U-Net for LDCT Reconstruction[J]. IEEE Transactions on Instrumentation and Measurement, 2023.","DOI":"10.1109\/TIM.2023.3329200"},{"key":"1254_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"1254_CR29","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1109\/LSP.2021.3079850","volume":"28","author":"Z Luo","year":"2021","unstructured":"Luo Z, Li J, Zhu Y. A deep feature fusion network based on multiple attention mechanisms for joint iris-periocular biometric recognition[J]. IEEE Signal Processing Letters, 2021, 28: 1060-1064.","journal-title":"IEEE Signal Processing Letters"},{"key":"1254_CR30","doi-asserted-by":"crossref","unstructured":"Wu W, Lv G, Duan Y, et al. DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising[J]. arXiv preprint arXiv:2304.01498, 2023.","DOI":"10.1007\/s00530-024-01469-8"},{"key":"1254_CR31","doi-asserted-by":"crossref","unstructured":"Zhong J, Chen J, Mian A. DualConv: Dual convolutional kernels for lightweight deep neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022.","DOI":"10.1109\/TNNLS.2022.3151138"},{"key":"1254_CR32","first-page":"1","volume":"60","author":"S Liu","year":"2021","unstructured":"Liu S, Lei Y, Zhang L, et al. MRDDANet: A multiscale residual dense dual attention network for SAR image denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-13.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"6","key":"1254_CR33","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Siddiquee M M R, Tajbakhsh N, et al. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE transactions on medical imaging, 2019, 39(6): 1856-1867.","journal-title":"IEEE transactions on medical imaging"},{"key":"1254_CR34","doi-asserted-by":"publisher","first-page":"3045","DOI":"10.1109\/JSTARS.2023.3257051","volume":"16","author":"H Pan","year":"2023","unstructured":"Pan H, Gao F, Dong J, et al. Multiscale adaptive fusion network for hyperspectral image denoising[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 3045-3059.","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"key":"1254_CR35","doi-asserted-by":"crossref","unstructured":"Feng R, Gao Y, Tse T H E, et al. DiffPose: SpatioTemporal diffusion model for video-based human pose estimation[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2023: 14861\u201314872.","DOI":"10.1109\/ICCV51070.2023.01365"},{"key":"1254_CR36","doi-asserted-by":"crossref","unstructured":"Iqbal S, Khan T M, Naqvi S S, et al. LDMRes-Net: A Lightweight Neural Network for Efficient Medical Image Segmentation on IoT and Edge Devices[J]. IEEE journal of biomedical and health informatics, 2023.","DOI":"10.1109\/JBHI.2023.3331278"},{"key":"1254_CR37","doi-asserted-by":"crossref","unstructured":"Li J, Wen Y, He L. SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2023: 6153\u20136162.","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"1254_CR38","doi-asserted-by":"crossref","unstructured":"Liang T, Jin Y, Li Y, et al. Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising[C]\/\/2020 15th IEEE International Conference on Signal Processing (ICSP). IEEE, 2020, 1: 193\u2013198.","DOI":"10.1109\/ICSP48669.2020.9320928"},{"key":"1254_CR39","doi-asserted-by":"publisher","first-page":"106949","DOI":"10.1016\/j.knosys.2021.106949","volume":"226","author":"C Tian","year":"2021","unstructured":"Tian C, Xu Y, Zuo W, et al. Designing and training of a dual CNN for image denoising[J]. Knowledge-Based Systems, 2021, 226: 106949.","journal-title":"Knowledge-Based Systems"},{"key":"1254_CR40","doi-asserted-by":"crossref","unstructured":"Wang Z, Cun X, Bao J, et al. Uformer: A general u-shaped transformer for image restoration[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2022: 17683\u201317693.","DOI":"10.1109\/CVPR52688.2022.01716"},{"issue":"6","key":"1254_CR41","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acc000","volume":"68","author":"D Wang","year":"2023","unstructured":"Wang D, Fan F, Wu Z, et al. CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising[J]. Physics in Medicine & Biology, 2023, 68(6): 065012.","journal-title":"Physics in Medicine & Biology"},{"issue":"6","key":"1254_CR42","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1007\/s11633-023-1466-0","volume":"20","author":"K Zhang","year":"2023","unstructured":"Zhang K, Li Y, Liang J, et al. Practical blind image denoising via Swin-Conv-UNet and data synthesis[J]. Machine Intelligence Research, 2023, 20(6): 822-836.","journal-title":"Machine Intelligence Research"},{"key":"1254_CR43","doi-asserted-by":"crossref","unstructured":"W. Lai, J. Huang, N. Ahuja, M. Yang, Deep laplacian pyramid networks for fast and accurate super-resolution, IEEE Conference on Computer Vision and Pattern Recognition (2017) 5835\u20135843.","DOI":"10.1109\/CVPR.2017.618"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01254-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01254-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01254-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T14:18:07Z","timestamp":1743344287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01254-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["1254"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01254-z","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,11]]},"assertion":[{"value":"9 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not involve human or animal subjects. We use publicly available datasets, and thus, no ethical approval was required. The study protocol adhered to the guidelines established by the journal.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Authors consent to participate in this research and confirm that the article has been approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the article has been approved by all of us.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Authors consent to publish this article, and the manuscript is approved by all authors for submission.","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"}}]}}