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NSF/FDA SIR: Quantitative Characterization of Innovative Near-infrared Fluorescence Imaging Systems

NSF/FDA SIR: Quantitative Characterization of Innovative Near-infrared Fluorescence Imaging Systems
NSF/FDA SIR:创新近红外荧光成像系统的定量表征
批准号:
1445701
负责人:
Yu Chen
金额:
$8.8万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-01-01 至 2016-12-31

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PI: Chen, YuProposal: 1445701Title: Quantitative Characterization of Innovative Near-infrared Fluorescence Imaging SystemsSignificanceInsights gained regarding optimal methods for NIR fluorescence image quality assessment will be relevant well beyond the primary focus of intraoperative imaging, to applications such as retinal angiography and lymphatic imaging using fluorescent dyes. Development of standardized test methods will also have a significant impact on the emerging field of optical molecular imaging, by facilitate device development and intercomparison, clinical translation and regulatory clearance. Furthermore, the proposed research will be seamlessly integrated with educational, mentoring, and outreach activities to advance the cross-disciplinary program in the field of Biophotonics Imaging and regulatory science at the University of Maryland (UMD) and FDA. UMD was recently awarded as the Center for Excellence in Regulatory Science and Innovation (CERSI) by FDA. The proposed research will further strengthen the research and education collaboration between UMD and FDA.Technical DescriptionThis proposal aims to develop and validate a series of novel phantom-based test methods for standardized, quantitative assessment of fluorescence imaging instruments. The components of this work will include: 1) Development of NIR fluorescence systems and characterization of potential phantom constituent materials; 2) Development of phantoms for evaluation of resolution and illumination/detection uniformity, as well as the contrast-detail analysis and low-contrast detectability; 3) Investigation of the potential to use three-dimensional (3D) printing to generate fluorescence phantoms based on conventional-geometries as well as biomimetic phantoms based on image volumes to replicate realistic tissue morphology.
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