T. Balcaen , S. Vangrunderbeeck , W.M. De Borggraeve , G. Kerckhofs
{"title":"Contrast-enhancing staining agents for ex vivo contrast-enhanced computed tomography: A review","authors":"T. Balcaen , S. Vangrunderbeeck , W.M. De Borggraeve , G. Kerckhofs","doi":"10.1016/j.tmater.2025.100052","DOIUrl":null,"url":null,"abstract":"<div><div><em>Ex vivo</em> microCT imaging has emerged as a powerful tool for 3D histology of biological tissues, offering significant advantages in terms of spatial resolution, simplicity of protocols and acquisition speed. Among the various techniques available, contrast-enhanced computed tomography (CECT) is particularly favored for its ability to simultaneously visualize soft and mineralized tissue types through the use of contrast agents (CAs), making it suitable for laboratory-based microCT devices. This review focuses on contrast-enhancing staining agents (CESAs), a subclass of CAs, which enrich the X-ray attenuating atom content in soft tissues through interactions. Within this review, CESAs are categorized based on their chemical composition into organic, mixed (<em>i.e.</em> heavy metal and organic ligand) and inorganic compounds, each with specific properties and applications. Despite the growing interest and numerous studies on CESAs, the selection process often relies on trial-and-error, anecdotal knowledge, or commercial availability. This review aims to enhance understanding of the chemical interactions and distribution patterns of CESAs within biological tissues, by discussing a selection of studies grouping observations by tissues and organs, to gain a better understanding of consistent affinity patterns. The findings highlight the complexity and accompanying challenges of predicting CESA distribution. This review will provide a foundation for both intelligent CESA selection and design, tailored to specific research needs as well as a guide for the application expert in choosing relevant literature for designing their experiments.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100052"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tomography of Materials and Structures","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949673X25000051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Ex vivo microCT imaging has emerged as a powerful tool for 3D histology of biological tissues, offering significant advantages in terms of spatial resolution, simplicity of protocols and acquisition speed. Among the various techniques available, contrast-enhanced computed tomography (CECT) is particularly favored for its ability to simultaneously visualize soft and mineralized tissue types through the use of contrast agents (CAs), making it suitable for laboratory-based microCT devices. This review focuses on contrast-enhancing staining agents (CESAs), a subclass of CAs, which enrich the X-ray attenuating atom content in soft tissues through interactions. Within this review, CESAs are categorized based on their chemical composition into organic, mixed (i.e. heavy metal and organic ligand) and inorganic compounds, each with specific properties and applications. Despite the growing interest and numerous studies on CESAs, the selection process often relies on trial-and-error, anecdotal knowledge, or commercial availability. This review aims to enhance understanding of the chemical interactions and distribution patterns of CESAs within biological tissues, by discussing a selection of studies grouping observations by tissues and organs, to gain a better understanding of consistent affinity patterns. The findings highlight the complexity and accompanying challenges of predicting CESA distribution. This review will provide a foundation for both intelligent CESA selection and design, tailored to specific research needs as well as a guide for the application expert in choosing relevant literature for designing their experiments.