{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:24:18Z","timestamp":1777879458388,"version":"3.51.4"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","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":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110305","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:07:14Z","timestamp":1776841634000},"page":"110305","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Residual-guided multiscale diffusion model for low-dose CT denoising"],"prefix":"10.1016","volume":"121","author":[{"given":"Ju","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jikang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Guangyu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Cheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7221-381X","authenticated-orcid":false,"given":"Qing","family":"Chen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.bspc.2026.110305_b1","doi-asserted-by":"crossref","first-page":"213","DOI":"10.2967\/jnmt.106.037846","article-title":"Principles of CT: Radiation dose and image quality","volume":"35","author":"Goldman","year":"2007","journal-title":"J. Nucl. Med. Technol."},{"issue":"12","key":"10.1016\/j.bspc.2026.110305_b2","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1109\/TMI.2017.2767290","article-title":"Robust low-dose CT sinogram preprocessing via exploiting noise-generating mechanism","volume":"36","author":"Xie","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110305_b3","series-title":"Medical Imaging 2003: Physics of Medical Imaging","first-page":"759","article-title":"Adaptive noise reduction toward low-dose computed tomography","volume":"vol. 5030","author":"Lu","year":"2003"},{"issue":"23","key":"10.1016\/j.bspc.2026.110305_b4","doi-asserted-by":"crossref","first-page":"7923","DOI":"10.1088\/0031-9155\/57\/23\/7923","article-title":"Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction","volume":"57","author":"Liu","year":"2012","journal-title":"Phys. Med. Biol."},{"issue":"9","key":"10.1016\/j.bspc.2026.110305_b5","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1109\/TMI.2012.2195669","article-title":"Low-dose X-ray CT reconstruction via dictionary learning","volume":"31","author":"Xu","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10.1016\/j.bspc.2026.110305_b6","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"issue":"18","key":"10.1016\/j.bspc.2026.110305_b7","doi-asserted-by":"crossref","first-page":"5401","DOI":"10.1088\/0031-9155\/55\/18\/009","article-title":"Block matching 3D random noise filtering for absorption optical projection tomography","volume":"55","author":"Feruglio","year":"2010","journal-title":"Phys. Med. Biol."},{"issue":"16","key":"10.1016\/j.bspc.2026.110305_b8","doi-asserted-by":"crossref","first-page":"5803","DOI":"10.1088\/0031-9155\/58\/16\/5803","article-title":"Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing","volume":"58","author":"Chen","year":"2013","journal-title":"Phys. Med. Biol."},{"issue":"10","key":"10.1016\/j.bspc.2026.110305_b9","doi-asserted-by":"crossref","first-page":"5713","DOI":"10.1118\/1.3638125","article-title":"Low-dose computed tomography image restoration using previous normal-dose scan","volume":"38","author":"Ma","year":"2011","journal-title":"Med. Phys."},{"issue":"1","key":"10.1016\/j.bspc.2026.110305_b10","doi-asserted-by":"crossref","DOI":"10.1118\/1.4851635","article-title":"Adaptive nonlocal means filtering based on local noise level for CT denoising","volume":"41","author":"Li","year":"2014","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110305_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107510","article-title":"TransGraphNet: A novel network for medical image segmentation based on transformer and graph convolution","volume":"104","author":"Zhang","year":"2025","journal-title":"Biomed. Signal Process. Control."},{"issue":"12","key":"10.1016\/j.bspc.2026.110305_b12","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","article-title":"Low-dose CT with a residual encoder-decoder convolutional neural network","volume":"36","author":"Chen","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110305_b13","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"UNeXt: A low-dose CT denoising unet model with the modified ConvNeXt block","author":"Mazandarani","year":"2023"},{"key":"10.1016\/j.bspc.2026.110305_b14","series-title":"2020 15th IEEE International Conference on Signal Processing","first-page":"193","article-title":"Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising","volume":"vol. 1","author":"Liang","year":"2020"},{"issue":"9","key":"10.1016\/j.bspc.2026.110305_b15","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1002\/mp.13713","article-title":"A performance comparison of convolutional neural network-based image denoising methods: The effect of loss functions on low-dose CT images","volume":"46","author":"Kim","year":"2019","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110305_b16","series-title":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1175","article-title":"A bias-reducing loss function for CT image denoising","author":"Nagare","year":"2021"},{"issue":"2","key":"10.1016\/j.bspc.2026.110305_b17","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1002\/mp.13284","article-title":"Cycle-consistent adversarial denoising network for multiphase coronary CT angiography","volume":"46","author":"Kang","year":"2019","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110305_b18","first-page":"1","article-title":"DU-GAN: Generative adversarial networks with dual-domain U-net-based discriminators for low-dose CT denoising","volume":"71","author":"Huang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.bspc.2026.110305_b19","doi-asserted-by":"crossref","unstructured":"R. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer, High-resolution image synthesis with latent diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10.1016\/j.bspc.2026.110305_b20","doi-asserted-by":"crossref","unstructured":"B. Xia, Y. Zhang, S. Wang, Y. Wang, X. Wu, Y. Tian, W. Yang, L. Van Gool, Diffir: Efficient diffusion model for image restoration, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 13095\u201313105.","DOI":"10.1109\/ICCV51070.2023.01204"},{"key":"10.1016\/j.bspc.2026.110305_b21","series-title":"Low-dose CT using denoising diffusion probabilistic model for 20\u00d7 speedup","author":"Xia","year":"2022"},{"key":"10.1016\/j.bspc.2026.110305_b22","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110305_b23","series-title":"International Conference on Machine Learning","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","author":"Nichol","year":"2021"},{"key":"10.1016\/j.bspc.2026.110305_b24","doi-asserted-by":"crossref","unstructured":"J. Liu, Q. Wang, H. Fan, Y. Wang, Y. Tang, L. Qu, Residual denoising diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 2773\u20132783.","DOI":"10.1109\/CVPR52733.2024.00268"},{"key":"10.1016\/j.bspc.2026.110305_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108112","article-title":"A review of deep learning methods for denoising of medical low-dose CT images","volume":"171","author":"Zhang","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110305_b26","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1671","article-title":"UNAD: Universal anatomy-initialized noise distribution learning framework towards low-dose CT denoising","author":"Gu","year":"2024"},{"issue":"11","key":"10.1016\/j.bspc.2026.110305_b27","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1109\/TMI.2021.3085839","article-title":"CT reconstruction with PDF: Parameter-dependent framework for data from multiple geometries and dose levels","volume":"40","author":"Xia","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"10.1016\/j.bspc.2026.110305_b28","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acc000","article-title":"CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising","volume":"68","author":"Wang","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.bspc.2026.110305_b29","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"67","article-title":"All-in-one medical image restoration via task-adaptive routing","author":"Yang","year":"2024"},{"issue":"6","key":"10.1016\/j.bspc.2026.110305_b30","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","article-title":"Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss","volume":"37","author":"Yang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110305_b31","series-title":"Denoising diffusion implicit models","author":"Song","year":"2020"},{"issue":"4","key":"10.1016\/j.bspc.2026.110305_b32","first-page":"4713","article-title":"Image super-resolution via iterative refinement","volume":"45","author":"Saharia","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bspc.2026.110305_b33","first-page":"5775","article-title":"Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps","volume":"35","author":"Lu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110305_b34","doi-asserted-by":"crossref","first-page":"41259","DOI":"10.52202\/075280-1789","article-title":"Cold diffusion: Inverting arbitrary image transforms without noise","volume":"36","author":"Bansal","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110305_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128817","article-title":"Multi-scale adaptive residual cold diffusion model for low-dose CT denoising","author":"Zhang","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10.1016\/j.bspc.2026.110305_b36","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1109\/TMI.2023.3320812","article-title":"CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization","volume":"43","author":"Gao","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110305_b37","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1002\/mp.17431","article-title":"Diffusion probabilistic priors for zero-shot low-dose CT image denoising","volume":"52","author":"Liu","year":"2025","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110305_b38","series-title":"DiffDenoise: self-supervised medical image denoising with conditional diffusion models","author":"Demir","year":"2025"},{"key":"10.1016\/j.bspc.2026.110305_b39","series-title":"Micro-batch training with batch-channel normalization and weight standardization","author":"Qiao","year":"2019"},{"key":"10.1016\/j.bspc.2026.110305_b40","series-title":"How do vision transformers work?","author":"Park","year":"2022"},{"issue":"2","key":"10.1016\/j.bspc.2026.110305_b41","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1007\/s10278-024-01254-z","article-title":"A novel network for low-dose ct denoising based on dual-branch structure and multi-scale residual attention","volume":"38","author":"Zhang","year":"2025","journal-title":"J. Imaging Inform. Med."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008591?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426008591?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:44:25Z","timestamp":1777592665000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426008591"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":41,"alternative-id":["S1746809426008591"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110305","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Residual-guided multiscale diffusion model for low-dose CT denoising","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110305","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":"110305"}}