GENERALIZED EFFICIENT NMR SIGNAL LOCALIZATION

描述（改编自申请人的摘要）： 该项目的目标是扩展和完善一个通用的解决方案， 利用先验信息定位核磁共振信号的问题 信息. 该信息可以与其他信息在数学上组合。 给出代谢物光谱、浓度和性质的信号 和其他物质在所有解剖区域同时，但在很大程度上， 比传统成像所需的时间更短， 对成像系统的要求比那些不太一般的 方法. 只需要对信号处理软件进行适度的添加。 研究人员已经为这些想法开发了方法，包括 房室模型方法（SLIM模型）和广义序列 方法（GSLIM）进行房室成像。 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（Reduced-encoding Imaging by Generalized Series Reconstruction）及其在图像重建中的应用 变体TRIGR（双参考RIGR）和DIME（动态运动成像 估计）。 本提案的目标是完善这些技术， 这一点使得广泛的体内应用成为可能。 在此期间，申请人希望实现以下目标 具体目标：（1）改进第一阶段开发的软件工具， （二）使其方便易用;（三） 继续测试，通过模拟和幻影实验，理论 关于B1不均匀性、共振偏移校正的预测 和其他实验非理想性;（3）制定标准， 最有效的数据采集条件的前瞻性评价， 一项具体的研究，以及数据的可能可靠性，这将是 获得的，和标准，使回顾性评价数据的可靠性 （随机误差和系统误差）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 在理论上和实践中，运动补偿可以被 完成SLIM/GSLIM、RIGR/TRIGR和DIME;（5）制定方案 使用RIGR和相关方法，进一步确定可能的边界， 以加强SLIM和GSLIM分析的本地化; 以及（6）演示SLIM和GSLIM用于定量分析 动物组织（包括肌肉、脑和肝脏）的1H和31 P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.