{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T17:52:10Z","timestamp":1785433930815,"version":"3.56.0"},"reference-count":37,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["RS-2026-25473885"],"award-info":[{"award-number":["RS-2026-25473885"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"award":["RS-2026-25473885"],"award-info":[{"award-number":["RS-2026-25473885"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Institute of Information & Communications Technology Planning & Evaluation","award":["RS-2022-II220641"],"award-info":[{"award-number":["RS-2022-II220641"]}]},{"award":["RS-2022-II220641"],"award-info":[{"award-number":["RS-2022-II220641"]}],"id":[{"id":"https:\/\/ror.org\/01g0hqq23","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Ministry of Science and ICT (MSIT), Republic of Korea"},{"id":[{"id":"https:\/\/ror.org\/01wpjm123","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Low-dose contrast-enhanced computed tomography (CT) is widely used to reduce contrast-induced toxicity, but reduced iodine concentration and inconsistent acquisition conditions often produce uneven contrast attenuation and spatial misalignment between scans. In this context, we define dose-up translation as the computational process of synthetically enhancing low-dose contrast images to approximate the visual and diagnostic quality of full-dose acquisitions. These factors limit the effective use of routinely acquired imaging data for dose-up translation, particularly in veterinary abdominal CT where respiratory motion and postural variability further degrade anatomical correspondence. We present a weakly aligned enhancement framework designed to operate under spatial misalignment and limited paired data. Registration-based pseudo-references are constructed using a hybrid strategy that combines deformable anatomical alignment with feature-level correspondence. Dose-up translation is performed using structure-preserving translation with multi-scale consistency and edge-aware regularization to maintain anatomical boundaries. To address limited low-dose datasets, a two-stage knowledge transfer strategy transfers anatomical and contrast priors from abundant pre-contrast data. Quantitative evaluation demonstrated region-level contrast-to-noise ratio improvements of up to 31.5% (e.g., from 5.55 to 8.38 in the caudal vena cava (CVC), p &lt; 0.05) compared with baseline enhancement methods across 1171 test slices. Experiments demonstrate consistent improvements in structural fidelity, distributional realism, and region-level vascular conspicuity compared with paired, unpaired, and synthetic-pairing baselines. These findings suggest that the dose-up translation of low-enhanced CT is better formulated as a weakly aligned domain adaptation problem rather than a strictly paired reconstruction task, enabling practical image translation under realistic clinical acquisition variability.<\/jats:p>","DOI":"10.3390\/jimaging12050190","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T08:12:31Z","timestamp":1777363951000},"page":"190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Practical Weakly Supervised Framework for Dose-Up Translation of Low-Enhanced CT Under Clinical Acquisition Variability"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0586-3262","authenticated-orcid":false,"given":"Jong Bub","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, 100 Inha-ro, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5943-3902","authenticated-orcid":false,"given":"Se Hwan","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Inha University, 100 Inha-ro, Incheon 22212, Republic of Korea"},{"name":"Department of Artificial Intelligence Semiconductor Engineering, Inha University, 100 Inha-ro, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9475-4481","authenticated-orcid":false,"given":"Yu Jin","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, 120 Neungdong-ro, Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5728-4820","authenticated-orcid":false,"given":"Jae Hwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, 120 Neungdong-ro, Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6123-2556","authenticated-orcid":false,"given":"Hyun Gyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Inha University, 100 Inha-ro, Incheon 22212, Republic of Korea"},{"name":"College of Medicine, Inha University, 100 Inha-ro, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","article-title":"A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies with Progress Highlights, and Future Promises","volume":"109","author":"Zhou","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e180031","DOI":"10.1148\/ryai.2019180031","article-title":"Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions","volume":"1","author":"Prevedello","year":"2019","journal-title":"Radiol. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1111\/vaa.12335","article-title":"Adverse reactions following administration of contrast media for diagnostic imaging in anaesthetized dogs and cats: A retrospective study","volume":"43","author":"Scarabelli","year":"2016","journal-title":"Vet. Anaesth. Analg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.2460\/javma.20.02.0058","article-title":"Suspected contrast-induced nephropathy in three sequential patients undergoing computed tomography angiography and transarterial embolization for nonresectable neoplasia","volume":"259","author":"Griffin","year":"2021","journal-title":"J. Am. Vet. Med. Assoc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1148\/radiol.2015150605","article-title":"CT Scanning: Is the Contrast Material Enhancing the Radiation Dose and Cancer Risk as Well as the Image?","volume":"275","author":"Kleinerman","year":"2015","journal-title":"Radiology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1111\/vru.13122","article-title":"Lean body weight-adjusted intravenous iodinated contrast dose for abdominal CT in dogs reduces interpatient enhancement variability while providing diagnostic quality organ enhancement","volume":"63","author":"Kan","year":"2022","journal-title":"Vet. Radiol. Ultrasound"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Perfetti, S., Guglielmini, C., Linta, N., and Diana, A. (2024). Factors Influencing Contrast Enhancement in Abdominal Computed Tomography Angiography in the Dog: A Systematic Review. Animals, 14.","DOI":"10.3390\/ani14233521"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17635","DOI":"10.1038\/s41598-024-68705-z","article-title":"Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans","volume":"14","author":"Han","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"20403","DOI":"10.1038\/s41598-021-00058-3","article-title":"Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network","volume":"11","author":"Choi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_10","first-page":"1964","article-title":"Breaking the dilemma of medical image-to-image translation","volume":"34","author":"Kong","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1002\/mp.13927","article-title":"Patch-based generative adversarial neural network models for head and neck MR-only planning","volume":"47","author":"Klages","year":"2020","journal-title":"Med. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sriram, A., Zbontar, J., Murrell, T., Zitnick, C.L., Defazio, A., and Sodickson, D.K. (2020, January 14\u201319). GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01432"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","article-title":"A deep cascade of convolutional neural networks for dynamic MR image reconstruction","volume":"37","author":"Schlemper","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TMI.2018.2865356","article-title":"MoDL: Model-based deep learning architecture for inverse problems","volume":"38","author":"Aggarwal","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111355","DOI":"10.1016\/j.ejrad.2024.111355","article-title":"Deep learning-based algorithms for low-dose CT imaging: A review","volume":"172","author":"Chen","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Seyyedi, N., Ghafari, A., Seyyedi, N., and Sheikhzadeh, P. (2024). Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: A systematic review. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01417-y"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6087","DOI":"10.1007\/s00330-021-07714-2","article-title":"Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network","volume":"31","author":"Haubold","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_18","first-page":"681","article-title":"Unsupervised Domain Adaptation Using Soft-Labeled Contrastive Learning with Reversed Monte Carlo Method for Cardiac Image Segmentation","volume":"Volume 15009","author":"Linguraru","year":"2024","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2024; Lecture Notes in Computer Science"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"055014","DOI":"10.1088\/1361-6560\/adb2d7","article-title":"CycleH-CUT: An unsupervised medical image translation method based on cycle consistency and hybrid contrastive learning","volume":"70","author":"Jiang","year":"2025","journal-title":"Phys. Med. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Seo, M., Kim, D., Lee, K., Hong, S., Bae, J.S., Kim, J.H., and Kwak, S. (2021, January 5\u20139). Neural contrast enhancement of CT image. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual.","DOI":"10.1109\/WACV48630.2021.00402"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1097\/RLI.0000000000000875","article-title":"Contrast media reduction in computed tomography with deep learning using a generative adversarial network in an experimental animal study","volume":"57","author":"Haubold","year":"2022","journal-title":"Investig. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/978-981-97-2004-0_30","article-title":"MRI Denoising with Residual Connections and Two-Way Scaling Using Unsupervised Swin Convolutional U-Net Transformer (USCUNT)","volume":"Volume 966","author":"Anu","year":"2024","journal-title":"International Conference on Applied Soft Computing and Communication Networks; Lecture Notes in Networks and Systems"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107391","DOI":"10.1016\/j.cmpb.2023.107391","article-title":"United multi-task learning for abdominal contrast-enhanced CT synthesis through joint deformable registration","volume":"231","author":"Zhong","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Graikos, A., Yellapragada, S., Le, M.Q., Kapse, S., Prasanna, P., Saltz, J., and Samaras, D. (2024, January 16\u201322). Learned representation-guided diffusion models for large-image generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00815"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lu, H., Tunanyan, H., Wang, K., Navasardyan, S., Wang, Z., and Shi, H. (2023, January 17\u201324). Specialist diffusion: Plug-and-play sample-efficient fine-tuning of text-to-image diffusion models to learn any unseen style. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01371"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","article-title":"Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain","volume":"12","author":"Avants","year":"2008","journal-title":"Med. Image Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","article-title":"A reproducible evaluation of ANTs similarity metric performance in brain image registration","volume":"54","author":"Avants","year":"2011","journal-title":"NeuroImage"},{"key":"ref_28","first-page":"608","article-title":"DINO-Reg: General Purpose Image Encoder for Training-Free Multi-modal Deformable Medical Image Registration","volume":"Volume 15002","author":"Song","year":"2024","journal-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention; Lecture Notes in Computer Science"},{"key":"ref_29","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., and El-Nouby, A. (2023). DINOv2: Learning robust visual features without supervision. arXiv."},{"key":"ref_30","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."},{"key":"ref_31","unstructured":"Wang, Z., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA."},{"key":"ref_32","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). GANs trained by a two time-scale update rule converge to a local Nash equilibrium. NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201322). The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1109\/TMI.2019.2933425","article-title":"ADN: Artifact disentanglement network for unsupervised metal artifact reduction","volume":"39","author":"Liao","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103397","DOI":"10.1016\/j.media.2024.103397","article-title":"NCCT-to-CECT synthesis with contrast-enhanced knowledge and anatomical perception for multi-organ segmentation in non-contrast CT images","volume":"100","author":"Zhong","year":"2025","journal-title":"Med. Image Anal."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/190\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T04:20:33Z","timestamp":1778732433000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,27]]},"references-count":37,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jimaging12050190"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12050190","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,27]]}}}