Novel acquisition and computation in vibrational spectroscopic imaging

振动光谱成像中的新颖采集和计算

• 批准号: 0957849
• 负责人: Rohit Bhargava
• 金额: $ 40万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-15 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0957849&HistoricalAwards=false
• 关键词: Novel; acquisition; computation; vibrational; spectroscopic

Prof. Rohit Bhargava and his interdisciplinary team at the University of Illinois are supported by the Chemical Measurement and Imaging Program in the Division of Chemistry to develope comprehensive and innovative methods for extracting knowledge from chemical imaging (CI) by (i) improving CI data both in time (by compressed sensing) and in signal-to-noise ratio (by noise reduction); (ii) developing novel data analysis algorithms that synergistically process spatial and spectral information; and (iii) extracting information about complex patterns and natural interactions that cannot be discerned currently by examination of structure alone. The approaches are being optimized on existing FT-IR and Raman imaging systems, and applied to study structure evolution in complex human tissues during malignant transformation. Imaging has traditionally focused on visualizing structure. This project broadly focuses on effectively visualizing the chemistry of materials by enhancing theory and computation in the emerging area of CI. The data enhancement approaches developed in this project will be broadly applicable to multiple analytical techniques and to all fields of application. Hence, the project is expected to widely accelerate scientific discovery and technological progress in, for example, materials science (e.g. in polymer composites), biological sciences (e.g. in lethal cancer transformation), and forensics (e.g. detecting prints). The developed tools and educational activities will enhance infrastructure and help build the national and international CI community. Tools and dissemination proposed here will help build partnerships between CI, computational, and biomedical scientists - allowing for rapid dissemination of results to industry and other practitioners. The project will train a diverse group of undergraduate and graduate students in cross-disciplinary science, including mathematics, physics, chemistry, engineering, biology, and computing. Underrepresented groups will be involved and nurtured into this emerging area by sustained mentoring. Finally, the developed techniques are expected to be translated to commercial instruments, contributing to widespread use.

Rohit Bhargava 教授和他在伊利诺伊大学的跨学科团队得到了化学系化学测量和成像项目的支持，开发了从化学成像 (CI) 中提取知识的全面创新方法，方法是：(i) 改进 CI 数据的时间（通过压缩传感）和信噪比（通过降噪）； (ii) 开发协同处理空间和光谱信息的新型数据分析算法； (iii) 提取目前仅通过结构检查无法辨别的复杂模式和自然相互作用的信息。这些方法正在现有的 FT-IR 和拉曼成像系统上进行优化，并应用于研究复杂人体组织在恶性转化过程中的结构演化。成像传统上侧重于结构的可视化。该项目主要致力于通过增强 CI 新兴领域的理论和计算来有效地可视化材料化学。该项目开发的数据增强方法将广泛适用于多种分析技术和所有应用领域。因此，该项目预计将广泛加速材料科学（例如聚合物复合材料）、生物科学（例如致死性癌症转化）和法医学（例如检测指纹）等领域的科学发现和技术进步。开发的工具和教育活动将增强基础设施并帮助建立国内和国际 CI 社区。这里提出的工具和传播将有助于在 CI、计算和生物医学科学家之间建立伙伴关系，从而能够将结果快速传播给行业和其他从业者。该项目将培养跨学科科学领域的多元化本科生和研究生，包括数学、物理、化学、工程、生物学和计算机。代表性不足的群体将通过持续的指导参与并培育进入这一新兴领域。最后，所开发的技术预计将转化为商业仪器，有助于广泛使用。