Multispectral Retinal Imaging and Mapping of Naturally Occurring Fluorophore and Chromophore Distributions in Health and Early Pathology

健康和早期病理学中自然发生的荧光团和发色团分布的多光谱视网膜成像和绘图

• 批准号: 0854233
• 负责人: Wojciech Czaja
• 金额: $ 38.49万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0854233&HistoricalAwards=false
• 关键词: Multispectral; Retinal; Imaging; Mapping; Naturally

0854233CzajaThe overall objective of this proposal is to integrate robust automated multispectral image analysis methods with multispectral retinal reflectance and autofluorescence imaging to provide quantitative maps of naturally occurring fluorophores and chromophore distri-butions within the retina. The resulting high-resolution multi-component molecular maps would be applied to better understand molecular events in normal retinal aging and in early retinal disease progression.Tasks:- To optimize the quality of molecular image maps generated by automated imageanalysis methods from different multispectral sets of autofluorescence and reflectance images obtained using special filter sets in standard fundus cameras. Envisioned as an iterative process of refinement of filters, imaging protocols and analysis tools.- To apply spectral linear demixing models for extraction of pure subpixel spectral signatures to obtain clinically important changes in local concentrations of multiple endogenous molecular species- To develop spectral classification schemes based on dimension reduction algorithms to classify local cellular function and pathology- To develop locally supported multi-scale (wavelet) representation systems for sparse representation of reproducible features in order to follow changes overtime, particularly for different classes of early pathologic lesions on the order of 100 microns.

该提案的总体目标是将稳健的自动化多光谱图像分析方法与多光谱视网膜反射和自体荧光成像相结合，以提供视网膜内天然存在的荧光团和发色团分布的定量图。由此产生的高分辨率多组分分子地图将被应用于更好地了解分子事件在正常视网膜老化和早期视网膜疾病progress.Tasks：-优化质量的分子图像地图所产生的自动imagesalysis方法从不同的多光谱集的自体荧光和反射图像使用特殊的过滤器集在标准眼底相机。设想为过滤器，成像协议和分析工具的改进的迭代过程。 应用光谱线性分层模型提取纯子像素光谱特征，以获得多种内源性分子种类局部浓度的临床重要变化-基于降维算法开发光谱分类方案，以分类局部细胞功能和病理-开发局部支持的多尺度（小波）用于可再现特征的稀疏表示的表示系统，以便随时间跟踪变化，特别是对于100微米量级的不同类别的早期病理损伤。