{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:04:56Z","timestamp":1780549496947,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Medical Research Project of Jiangsu Provincial Health Commission","award":["K2025007"],"award-info":[{"award-number":["K2025007"]}]},{"name":"The Medical Research Project of Jiangsu Provincial Health Commission","award":["Z2025004"],"award-info":[{"award-number":["Z2025004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Objectives: Contrast-enhanced magnetic resonance imaging (CE-MRI) plays an important role in the diagnosis, treatment monitoring, and follow-up of brain tumors. However, the use of gadolinium-based contrast agents (GBCAs) is limited in patients with contraindications, such as severe renal impairment or situations requiring repeated examinations. This study aimed to develop a diffusion model-based Difference-Aware Guided Control Network (DAGCN) for synthesizing high-quality contrast-enhanced T1-weighted MRI (T1-CE) from non-contrast T1-weighted images in combination with an auxiliary sequence. Methods: Using the BraTS 2021 dataset, we proposed a two-stage generative framework that first localizes lesion-related enhancement cues and then guides image synthesis. In the first stage, a Difference-Aware Fusion and Prediction (DAFP) module was designed to extract complementary information from non-contrast T1-weighted images and an auxiliary sequence (T2-weighted or FLAIR) through dual-branch feature extraction and cross-modal channel attention fusion, followed by prediction of a lesion-related discrepancy map. In the second stage, the predicted discrepancy map was concatenated with the original T1-weighted images and introduced into a ControlNet-guided diffusion model to constrain the reverse denoising process and generate the target T1-CE image. Model performance was evaluated by visual comparison, quantitative metrics including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), and normalized cross-correlation (NCC), as well as blinded radiologist scoring of image quality (IQ), clinical replaceability (IC), contrast enhancement (CE), and lesion conformity (CF). Results: DAGCN generated synthetic T1-CE images with preserved global anatomical structure and faithful local lesion enhancement without the need for contrast agent administration. Compared with baseline methods, DAGCN achieved the highest PSNR and NCC under both T1 + T2 and T1 + FLAIR settings, while showing competitive SSIM and VIF performance. Visual comparison and radiologist-based subjective evaluation further indicated improved lesion-focused enhancement fidelity and reduced false-positive enhancement. Among the two auxiliary sequence settings, the T1 + FLAIR configuration provided more specific lesion localization and cleaner background suppression than the T1 + T2 configuration, particularly by reducing interference from cerebrospinal fluid signals. Conclusions: The proposed DAGCN framework enables the synthesis of clinically informative contrast-enhanced-like MRI from non-contrast multi-sequence inputs and may provide a promising alternative for patients in whom gadolinium administration is contraindicated or should be avoided. In particular, the FLAIR-guided setting showed advantages in lesion specificity, background cleanliness, and overall diagnostic quality.<\/jats:p>","DOI":"10.3390\/bioengineering13060634","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:17:31Z","timestamp":1780467451000},"page":"634","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Sequence Guided Generation of Contrast-Enhanced Magnetic Resonance Imaging Using Diffusion Models"],"prefix":"10.3390","volume":"13","author":[{"given":"Yue","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Clinical Medical Engineering, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Information, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanbing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangnan","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Clinical Medical Engineering, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9112-8461","authenticated-orcid":false,"given":"Da","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wujin","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Clinical Medical Engineering, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-3178","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Information, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9702-6316","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Intelligent Theranostics Technology and Instruments, Ministry of Education, School of Biomedical Engineering and Information, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1097\/RLI.0000000000000963","article-title":"Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in Neuroradiology","volume":"58","author":"Haase","year":"2023","journal-title":"Investig. Radiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1097\/RLI.0000000000000983","article-title":"Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI: A Literature Review","volume":"58","author":"Mallio","year":"2023","journal-title":"Investig. Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1097\/RLI.0000000000001107","article-title":"Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal Extraction","volume":"60","author":"Haase","year":"2025","journal-title":"Investig. Radiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1097\/RLI.0000000000000811","article-title":"Can Deep Learning Replace Gadolinium in Neuro-Oncology? A Reader Study","volume":"57","author":"Ammari","year":"2022","journal-title":"Investig. Radiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.carj.2018.04.002","article-title":"Gadolinium Deposition in the Brain: A Systematic Review of Existing Guidelines and Policy Statement Issued by the Canadian Association of Radiologists","volume":"69","author":"Costa","year":"2018","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1148\/radiol.13131669","article-title":"High Signal Intensity in the Dentate Nucleus and Globus Pallidus on Unenhanced T1-Weighted MR Images: Relationship with Increasing Cumulative Dose of a Gadolinium-Based Contrast Material","volume":"270","author":"Kanda","year":"2014","journal-title":"Radiology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1148\/radiol.15150025","article-title":"Intracranial Gadolinium Deposition after Contrast-Enhanced MR Imaging","volume":"275","author":"McDonald","year":"2015","journal-title":"Radiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/S1474-4422(17)30158-8","article-title":"Gadolinium Deposition in the Brain: Summary of Evidence and Recommendations","volume":"16","author":"Gulani","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1093\/ndt\/gfk062","article-title":"Gadolinium\u2014A Specific Trigger for the Development of Nephrogenic Fibrosing Dermopathy and Nephrogenic Systemic Fibrosis?","volume":"21","author":"Grobner","year":"2006","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1681\/ASN.2006060601","article-title":"Nephrogenic Systemic Fibrosis: Suspected Causative Role of Gadodiamide Used for Contrast-Enhanced Magnetic Resonance Imaging","volume":"17","author":"Marckmann","year":"2006","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Abdusalomov, A., Rakhimov, M., Karimberdiyev, J., Belalova, G., and Cho, Y.I. (2024). Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments. Bioengineering, 11.","DOI":"10.3390\/bioengineering11060627"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Saeed, Z., Bouhali, O., Ji, J.X., Hammoud, R., Al-Hammadi, N., Aouadi, S., and Torfeh, T. (2024). Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach. Bioengineering, 11.","DOI":"10.3390\/bioengineering11050410"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Moya-S\u00e1ez, E., de Luis-Garc\u00eda, R., and Alberola-L\u00f3pez, C. (2023). Toward Deep Learning Replacement of Gadolinium in Neuro-Oncology: A Review of Contrast-Enhanced Synthetic MRI. Front. Neuroimaging, 2.","DOI":"10.3389\/fnimg.2023.1055463"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1097\/RLI.0000000000000583","article-title":"Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study","volume":"54","author":"Kleesiek","year":"2019","journal-title":"Investig. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2400269","DOI":"10.1002\/gch2.202400269","article-title":"Gadolinium-Based Contrast Agents (GBCAs) for MRI: A Benefit\u2013Risk Balance Analysis from a Chemical, Biomedical, and Environmental Point of View","volume":"9","author":"Scarciglia","year":"2025","journal-title":"Glob. Chall."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4905","DOI":"10.1038\/s41598-024-53871-x","article-title":"Low-Dose GBCA Administration for Brain Tumour Dynamic Contrast Enhanced MRI: A Feasibility Study","volume":"14","author":"Lewis","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TBME.2022.3192309","article-title":"Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets","volume":"70","author":"Chen","year":"2023","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1002\/jmri.25970","article-title":"Deep Learning Enables Reduced Gadolinium Dose for Contrast-Enhanced Brain MRI","volume":"48","author":"Gong","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1002\/mrm.28808","article-title":"A Generic Deep Learning Model for Reduced Gadolinium Dose in Contrast-Enhanced Brain MRI","volume":"86","author":"Pasumarthi","year":"2021","journal-title":"Magn. Reson. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6419","DOI":"10.1007\/s00330-021-07848-3","article-title":"Deep Learning-Based Methods May Minimize GBCA Dosage in Brain MRI","volume":"31","author":"Luo","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3174\/ajnr.A8107","article-title":"Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning","volume":"45","author":"Murugesan","year":"2024","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1097\/RLI.0000000000000955","article-title":"Reduction of Gadolinium-Based Contrast Agents in MRI Using Convolutional Neural Networks and Different Input Protocols: Limited Interchangeability of Synthesized Sequences with Original Full-Dose Images Despite Excellent Quantitative Performance","volume":"58","author":"Haase","year":"2023","journal-title":"Investig. Radiol."},{"key":"ref_23","first-page":"2256","article-title":"Deep Unsupervised Learning Using Nonequilibrium Thermodynamics","volume":"Volume 37","author":"Weiss","year":"2015","journal-title":"Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 6\u201311 July 2015"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., and Agrawala, M. (2023, January 2\u20136). Adding Conditional Control to Text-to-Image Diffusion Models. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101565","DOI":"10.1016\/j.media.2019.101565","article-title":"Semi-Supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization","volume":"59","author":"Wang","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103554","DOI":"10.1016\/j.media.2025.103554","article-title":"MedScale-Former: Self-Guided Multiscale Transformer for Medical Image Segmentation","volume":"103","author":"Karimijafarbigloo","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_28","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance Normalization: The Missing Ingredient for Fast Stylization. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L. (2022, January 18\u201324). RePaint: Inpainting Using Denoising Diffusion Probabilistic Models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"ref_30","first-page":"6840","article-title":"Denoising Diffusion Probabilistic Models","volume":"Volume 33","author":"Ho","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)","volume":"34","author":"Menze","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"170117","DOI":"10.1038\/sdata.2017.117","article-title":"Advancing the Cancer Genome Atlas Glioma MRI Collections with Expert Segmentation Labels and Radiomic Features","volume":"4","author":"Bakas","year":"2017","journal-title":"Sci. Data"},{"key":"ref_33","unstructured":"Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., and Pati, S. (2021). The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4664","DOI":"10.1002\/mp.16529","article-title":"SynthRAD2023 Grand Challenge Dataset: Generating Synthetic CT for Radiotherapy","volume":"50","author":"Thummerer","year":"2023","journal-title":"Med. Phys."},{"key":"ref_35","unstructured":"Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., and Yang, D. (2022). MONAI: An Open-Source Framework for Deep Learning in Healthcare. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","article-title":"Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks","volume":"38","author":"Dar","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1109\/TMI.2020.2975344","article-title":"Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","article-title":"U2Fusion: A Unified Unsupervised Image Fusion Network","volume":"44","author":"Xu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xu, S., Zhang, C., Liu, J., Zhang, J., and Li, P. (2020). DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion. arXiv.","DOI":"10.24963\/ijcai.2020\/135"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-Domain Long-Range Learning for General Image Fusion via Swin Transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain Tumor Segmentation with Deep Neural Networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5444","DOI":"10.1038\/s41467-020-19160-7","article-title":"The Misuse of Colour in Science Communication","volume":"11","author":"Crameri","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 18\u201324). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cornfeld, D., Condron, P., Newburn, G., McGeown, J., Scadeng, M., Bydder, M., Griffin, M., Handsfield, G., Perera, M.R., and Melzer, T. (2024). Ultra-High Contrast MRI: Using Divided Subtracted Inversion Recovery (dSIR) and Divided Echo Subtraction (dES) Sequences to Study the Brain and Musculoskeletal System. Bioengineering, 11.","DOI":"10.3390\/bioengineering11050441"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/634\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:21:04Z","timestamp":1780546864000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/6\/634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["bioengineering13060634"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13060634","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]}}}