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

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 核磁共振基本上是一种基于傅里叶变换的成像技术。虽然傅立叶重建算法在最小范数、最小二乘意义下是最优的，但它受到许多实际问题的困扰，最明显的是，当测量的编码数量较小时，有限的分辨率和Gibbs振铃现象。这些问题限制了磁共振成像的速度、效率和定量准确性。虽然这些问题在很大程度上在传统的解剖成像中是可以容忍的，但它们已经成为功能和代谢成像的重要障碍。在过去的十年里，图像重建核心的研究人员从不同的角度做出了巨大的努力来解决图像重建问题，产生了一些非常有前途的想法和技术，它们可以有效地利用先验(或边)信息来弥补测量图像数据的不足，从而产生比基于傅立叶变换的同行更高的分辨率和成像速度。 图像重建核心的子项目1旨在提供一种有效的方法来利用(k，t)空间信号的时空相关性进行稀疏采样(从而减少成像时间)。 项目1(稀疏采样(k，t)空间数据的时空成像的广义序列重建)有三个具体目标： 目标1：优化广义级数模型的新的(k，t)空间公式，以允许对所提出的成像中心的各种时空成像应用(例如，动态灌注成像)中遇到的时变目标函数进行联合时空建模。 目标2：开发一种高效的图像重建算法，它可以处理使用单个或多个相控阵线圈采集的常规和灵敏度编码的(k，t)空间数据。 目标3：使用验证部分中描述的方法验证所建议的(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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. MRI is basically a Fourier transform-based imaging technique. Although the Fourier reconstruction algorithm is optimal in the minimum-norm, least-squares sense, it suffers from a number of practical problems, most notably, limited resolution and Gibbs ringing artifact, when the number of encodings measured is small. These problems have limited the speed, efficiency, and quantitative accuracy of MRI. Although these problems are tolerable to a large extent in conventional anatomical imaging, they have become an important obstacle for functional and metabolic imaging. Over the past decade, the investigators of the Image Reconstruction Core have made great effort to address the image reconstruction problem from different angles, resulting in several very promising ideas and techniques that can use prior (or side) information effectively to compensate for the lack of sufficient measured imaging data, thus giving rise to much higher resolution and imaging speeds than the Fourier transform-based counterparts do. Subproject 1 of the Image reconstruction Core aims to provide an effective method to exploit spatiotemporal correlations of (k, t)-space signals for sparse sampling (thus reducing imaging time). Project 1 (Generalized Series Reconstruction from Spatiotemporal Imaging with Sparsely Sampled (k, t)-Space Data) has three specific aims: 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.