CDI Type I. Mixing the Data to Knowledge direction: Computational Thinking for Faint Feature Detection by Feedback Control and Sensitivity/Resolution Enhancement of Matrix Images

CDI类型I.混合数据与知识方向：通过反馈控制和矩阵图像的灵敏度/分辨率增强进行微弱特征检测的计算思维

• 批准号: 0835863
• 负责人: Yung-Ya Lin
• 金额: $ 67万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835863&HistoricalAwards=false
• 关键词: CDI; Type; I.; Mixing; Data

The investigator and his colleagues propose a new paradigm-shifting approach towards high resolution and high contrast imaging, which combines revolutions in magnetic resonance imaging (MRI) and optical imaging with equally cutting edge mathematical developments.The approach uses non-linear feedback between the detector and the sample, so that the measured field is fed back to the MRI magnet. The unstable feedback increases the dynamical contrast between normal and cancerous cells. This highly nontraditional approach will be complemented by incorporating compressed sensing, a data analysis technique where mathematical algorithms are used to extract specific features and images from a relatively small number of measurements.Finally, multiple sources and detectors, which coupled with compressed sensing and feedback imaging can collect data in parallel will be implemented together, and modern filter-diagonalization techniques will be used to synthesize the data leading to faster images. Overall, the research and development that the principal investigator and his colleagues propose will revolutionize MRI. By applying nontraditional measurement and imaging technique, the contrast between tumors and normal areas will be increased many fold. The increase will be based on the same physical phenomena, chaos, that is used to by birds and jet fighters to quickly switch their direction. The revolutionary paradigm will will eventually make it much cheaper and faster to do an MRI scan, thereby having enormously broad impacts. In 2008, an estimated 1,680,000 people in the U.S. will be diagnosed with cancer, and approximately 670,000 people will die. Between 10%-35% could have been saved with earlier detection.This highlights the need for improved early detection methods, which could have saved many patients. A large (2-5 or more) reduction MRI acquisition time, which is not feasible with conventional methods, coupled with the enhanced feature resolution native to the proposed approach, will allow for faster and cheaper cancer screening, which is crucial to improved early detection and thus reducing deaths due to cancer. Besides cancer detection, there are numerous other imaging applications that stand to benefit from significantly decreased scan time and cost, such as industrial sensing (for example uniformity of fruits in agriculture) and homeland security applications, including highly sensitive detection of concealed materials.

研究人员和他的同事们提出了一种新的范式转换方法，以实现高分辨率和高对比度成像，该方法将磁共振成像（MRI）和光学成像的革命与同样前沿的数学发展相结合。该方法使用探测器和样品之间的非线性反馈，以便将测量的磁场反馈到MRI磁体。 不稳定的反馈增加了正常细胞和癌细胞之间的动态对比。 这种高度非传统的方法将通过结合压缩传感来补充，压缩传感是一种数据分析技术，使用数学算法从相对少量的测量中提取特定的特征和图像。最后，多个源和探测器，与压缩传感和反馈成像相结合，可以并行收集数据，现代滤波对角化技术将用于合成数据，从而产生更快的图像。 总的来说，首席研究员和他的同事提出的研究和开发将彻底改变MRI。 通过应用非传统的测量和成像技术，肿瘤和正常区域之间的对比度将增加许多倍。 这种增加将基于相同的物理现象，即混乱，这是鸟类和喷气式战斗机用来快速切换方向的。这一革命性的范例最终将使MRI扫描更便宜、更快，从而产生巨大的广泛影响。2008年，美国估计有1，680，000人将被诊断患有癌症，大约670，000人将死亡。 早期检测可以挽救10%-35%的生命，这突出了改进早期检测方法的必要性，这可以挽救许多患者的生命。大幅度（2-5或更多）减少MRI采集时间，这对于传统方法是不可行的，再加上所提出的方法固有的增强特征分辨率，将允许更快和更便宜的癌症筛查，这对改善早期检测至关重要，从而减少癌症导致的死亡。 除了癌症检测之外，还有许多其他成像应用可以从显着降低的扫描时间和成本中受益，例如工业传感（例如农业中水果的均匀性）和国土安全应用，包括隐藏材料的高灵敏度检测。