Shujie Li, Houde Wu, Li Guo, Xiaoyi Wang, Gang Shu, Xinxing Li, Shao-Kai Sun
{"title":"Artificial Intelligence-Assisted Low-Dose High Atomic Number Contrast Agent for Ultrahigh-Resolution Computed Tomography Angiography","authors":"Shujie Li, Houde Wu, Li Guo, Xiaoyi Wang, Gang Shu, Xinxing Li, Shao-Kai Sun","doi":"10.1021/acsnano.5c12047","DOIUrl":null,"url":null,"abstract":"Achieving high resolution while minimizing contrast agent dosage remains a key goal, yet a major challenge in contrast-enhanced computed tomography (CT) imaging. Herein, we propose an artificial intelligence-assisted low-dose high atomic number contrast agent for ultrahigh-resolution CT imaging. As a proof of concept, high-quality PEGylated hafnium oxide nanoparticles (DA-HfO<sub>2</sub> NPs) are synthesized, exhibiting superior X-ray attenuation, high hafnium content (36%), excellent water solubility, appropriate hydrodynamic size (13.5 nm), and prolonged circulation half-life (161.9 min). High-dose DA-HfO<sub>2</sub> NPs enable extended ultrahigh-resolution vascular imaging with a spatial resolution of 0.15 mm and a time window of at least 60 min. More importantly, by integrating artificial intelligence, the low-dose contrast agent (at 25% of the standard dose) achieves imaging quality comparable to that of the high-dose agent in both contrast density and spatial resolution, while simultaneously enhancing biosafety. This strategy enables high-resolution imaging at reduced contrast agent doses and offers a promising approach for sensitive and safe CT angiography.","PeriodicalId":21,"journal":{"name":"ACS Nano","volume":"101 1","pages":""},"PeriodicalIF":16.0000,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Nano","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1021/acsnano.5c12047","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Achieving high resolution while minimizing contrast agent dosage remains a key goal, yet a major challenge in contrast-enhanced computed tomography (CT) imaging. Herein, we propose an artificial intelligence-assisted low-dose high atomic number contrast agent for ultrahigh-resolution CT imaging. As a proof of concept, high-quality PEGylated hafnium oxide nanoparticles (DA-HfO2 NPs) are synthesized, exhibiting superior X-ray attenuation, high hafnium content (36%), excellent water solubility, appropriate hydrodynamic size (13.5 nm), and prolonged circulation half-life (161.9 min). High-dose DA-HfO2 NPs enable extended ultrahigh-resolution vascular imaging with a spatial resolution of 0.15 mm and a time window of at least 60 min. More importantly, by integrating artificial intelligence, the low-dose contrast agent (at 25% of the standard dose) achieves imaging quality comparable to that of the high-dose agent in both contrast density and spatial resolution, while simultaneously enhancing biosafety. This strategy enables high-resolution imaging at reduced contrast agent doses and offers a promising approach for sensitive and safe CT angiography.
期刊介绍:
ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.