GENERALIZED EFFICIENT NMR SIGNAL LOCALIZATION

描述(改编自申请者摘要)：广泛的，长期的 该项目的目标是扩展和完善一个通用的解决方案 核磁共振信号的先验定位问题 信息。该信息可以与其他信息在数学上组合 给出代谢物的光谱、浓度和性质的信号 和其他物质同时存在于所有解剖区域，但在更多的 比传统成像所需的时间更短，而且越来越少 对成像系统的要求比一般的要求要低 方法：研究方法。只需要适度增加信号处理软件。 调查人员为这些想法制定了方法论，包括 隔室建模法(SLIM模型)和一个广义级数 间隔成像的进路(GSLIM)。在将这一概念扩展到 动态成像，他们开发了两种新的基于模型的方法，称为 RIGR(广义级数重建简化编码成像)及其 变体TRIGR(双参考RIGR)和DIME(运动动态成像 估计)。该提案的目标是改进这些技术，以 这是一个可以广泛应用于体内的点。 在延续期间，申请者期望实现以下目标 具体目标：(1)改进第一阶段开发的软件工具 资助期，方便使用；(2) 通过模拟和模型实验，继续检验理论 关于B1不均匀性、共振偏移量的校正的预测 和其他实验性的非理想性；(3)制定标准 对最有效的数据采集条件进行前瞻性评估 一项具体的研究，以及数据的可能可靠性 和标准，以便对数据的可靠性进行回溯性评估 (随机和系统误差)可以进行；(4)制定方案 使用导航信号和(k，t)空间采样策略来确定 从理论上和实践上讲，运动补偿可以达到的程度 完成SLIM/GSLIM、RIGR/TRIGR和DIME；(5)制定方案 使用RIGR和相关方法进一步定义可能的边界 分区，以加强SLIM和GSLIM分析的本地化； (6)演示SLIM和GSLIM在定量分析中的应用 包括肌肉、脑和肝脏在内的动物组织的1H和31P光谱， 并将结果与现有技术的结果进行比较。

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.