GENERALIZED EFFICIENT NMR SIGNAL LOCALIZATION

广义高效核磁共振信号定位

• 批准号: 2395788
• 负责人: PAUL C LAUTERBUR
• 金额: $ 20.09万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-30 至 2000-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2395788
• 关键词: Rana; computer data analysis; computer human interaction; computer program /software; computer simulation; computer system design /evaluation; human tissue; image processing; mathematical model; model design /development; nuclear magnetic resonance spectroscopy

DESCRIPTION (Adapted from Applicant's Abstract): The broad, long-term objectives of this project are to extend and perfect a general solution to the problem of localizing nuclear magnetic resonance signals using a-priori information. This information may be combined mathematically with other signals to give the spectra, concentrations, and properties of metabolites and other substances in all anatomical regions simultaneously, but in a much shorter time than required by conventional imaging, and with fewer and less stringent demands on the imaging system than those made by less general methods. Only modest additions to signal processing software are required. The investigators have developed methodologies for these ideas, including a compartmental modeling approach (the SLIM model) and a generalized series approach (GSLIM) for compartmental imaging. In extending this concept to dynamic imaging, they have developed two novel model-based methods called RIGR (Reduced-Encoding Imaging by Generalized Series Reconstruction) and its variant TRIGR (Two-Referenced RIGR) and DIME (Dynamic Imaging by Motion Estimation). The goal of this proposal is refinement of these techniques to the point where wide-spread in-vivo applications are possible. During this continuation the applicants expect to achieve the following specific aims: (1) to improve the software tools developed during the first funding period so that they are convenient and user friendly; (2) to continue to test, by simulation and phantom experiments, theoretical predictions concerning corrections for B1 inhomogeneities, resonance offsets and other experimental nonidealities; (3) to develop criteria for prospective evaluation of the most effective data acquisition conditions for a specific study, and the probable reliability of the data which will be obtained, and criteria so that retrospective evaluation of data reliability (random and systematic errors) can be carried out; (4) to develop schemes using navigator signals and (k,t)-space sampling strategies to determine theoretically and practically the extent to which motion compensation can be accomplished in SLIM/GSLIM, RIGR/TRIGR and DIME; (5) to develop schemes using RIGR and related methods to further define possible boundaries within compartments so as to enhance the localization of SLIM and GSLIM analysis; and (6) to demonstrate the use of SLIM and GSLIM for quantitative analysis of 1H and 31P spectra of animal tissues, including muscle, brain and liver, and to compare the results with those of existing techniques.

描述（改编自申请人的摘要）：广泛的、长期的 该项目的目标是扩展和完善通用解决方案 使用先验定位核磁共振信号的问题 信息。 该信息可以与其他信息以数学方式结合起来 给出代谢物的光谱、浓度和特性的信号 和其他物质在所有解剖区域同时，但在一个多 比传统成像所需的时间更短，并且越来越少 对成像系统的要求比不那么一般的要求严格 方法。 只需要对信号处理软件进行适度的添加。 研究人员为这些想法开发了方法论，包括 房室建模方法（SLIM 模型）和广义序列 用于隔室成像的方法（GSLIM）。 在将这一概念延伸至 动态成像，他们开发了两种基于模型的新颖方法，称为 RIGR（广义序列重建简化编码成像）及其 变体 TRIGR（双参考 RIGR）和 DIME（运动动态成像） 估计）。 该提案的目标是完善这些技术 广泛的体内应用成为可能。 在此延续期间，申请人期望实现以下目标 具体目标：（1）改进第一阶段开发的软件工具 资助期限，以便方便和用户友好； (2) 至 通过模拟和模型实验继续测试理论 关于 B1 不均匀性、共振偏移校正的预测 以及其他实验性的非理想性； (3) 制定标准 最有效的数据采集条件的前瞻性评估 一项具体的研究，以及数据的可能可靠性 获得的数据和标准，以便回顾性评估数据的可靠性 （随机和系统误差）可以进行； (4)制定方案 使用导航器信号和 (k,t) 空间采样策略来确定 理论上和实践上运动补偿可以达到的程度 在 SLIM/GSLIM、RIGR/TRIGR 和 DIME 中完成； (5)制定方案 使用 RIGR 和相关方法进一步定义可能的边界 隔室以增强 SLIM 和 GSLIM 分析的定位； (6) 演示如何使用 SLIM 和 GSLIM 进行定量分析 动物组织（包括肌肉、大脑和肝脏）的 1H 和 31P 光谱， 并将结果与​​现有技术进行比较。