IMAGE RECONSTRUCTION FROM SPARSELY SAMPLED (K, T)-SPACE DATA

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 目标1：优化广义系列模型的新颖（k，t）-空间公式，以允许在所提出的成像中心的各种时空成像应用中遇到的时变目标函数的联合时空建模（例如，动态灌注成像）。 目标二：开发一种有效的图像重建算法，该算法可以处理使用单个或多个相控阵线圈收集的常规和灵敏度编码（k，t）空间数据。 目标3：使用验证章节中描述的方法验证拟议的（k，t）-空间成像方法。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Aim 1: Optimizing a novel (k, t)-space formulation of the generalized series model to allow joint spatiotemporal modeling of the time-varying object function encountered in various spatiotemporal imaging applications of the proposed imaging center (e.g., dynamic perfusion imaging). Aim 2: Development of an efficient image reconstruction algorithm that can handle both conventional and sensitivity-encoded (k, t)-space data collected using a single or multiple phased array coils. Aim 3: Validation of the proposed (k, t)-space imaging method using the methodologies described in the Validation section.