Novel acquisition and computation in vibrational spectroscopic imaging

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，以开发全面和创新的方法，通过以下方式从化学成像（CI）中提取知识：（i）及时改进CI数据，（通过压缩感知）和信噪比（ii）开发新的数据分析算法，协同处理空间和光谱信息;以及（iii）提取关于复杂模式和自然相互作用的信息，这些信息目前不能单独通过结构检查来辨别。该方法正在现有的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变。传统上，成像专注于可视化结构。该项目广泛关注通过增强CI新兴领域的理论和计算来有效地可视化材料的化学。本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，预计该项目将广泛加速材料科学（如聚合物复合材料）、生物科学（如致命癌症转化）和法医学（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加强基础设施，并帮助建立国家和国际传播和信息社区。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-允许将结果快速传播给行业和其他从业者。该项目将培养一批跨学科的本科生和研究生，包括数学，物理，化学，工程，生物和计算。代表性不足的群体将通过持续的辅导参与和培养进入这一新兴领域。最后，预计开发的技术将转化为商业工具，促进广泛使用。

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.