可视化
模态(人机交互)
磁共振成像
多模态
计算机科学
管道(软件)
医学影像学
工作流程
临床前影像学
断层摄影术
显微镜
离体
分子成像
正电子发射断层摄影术
生物医学工程
人工智能
计算机视觉
病理
放射科
生物
医学
体内
万维网
生物技术
程序设计语言
数据库
作者
Akanksha Bhargava,Benjamin Monteagudo,Rohit Bhakar,Janaka Senarathna,Yunke Ren,Ryan C. Riddle,Manisha Aggarwal,Arvind P. Pathak
出处
期刊:Nature Methods
[Springer Nature]
日期:2022-02-01
卷期号:19 (2): 242-254
被引量:22
标识
DOI:10.1038/s41592-021-01363-5
摘要
Despite advances in imaging, image-based vascular systems biology has remained challenging because blood vessel data are often available only from a single modality or at a given spatial scale, and cross-modality data are difficult to integrate. Therefore, there is an exigent need for a multimodality pipeline that enables ex vivo vascular imaging with magnetic resonance imaging, computed tomography and optical microscopy of the same sample, while permitting imaging with complementary contrast mechanisms from the whole-organ to endothelial cell spatial scales. To achieve this, we developed ‘VascuViz’—an easy-to-use method for simultaneous three-dimensional imaging and visualization of the vascular microenvironment using magnetic resonance imaging, computed tomography and optical microscopy in the same intact, unsectioned tissue. The VascuViz workflow permits multimodal imaging with a single labeling step using commercial reagents and is compatible with diverse tissue types and protocols. VascuViz’s interdisciplinary utility in conjunction with new data visualization approaches opens up new vistas in image-based vascular systems biology. VascuViz represents a versatile workflow for multimodal imaging of the vasculature in ex vivo tissue samples across length and resolution scales, paving the way for improved and novel image-based vascular systems biology applications.
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