PARALLEL MRSI RECON WITH ARBITRARY TRAJECTORIES USING K-SPACE SPARSE MATRICES

使用 K 空间稀疏矩阵进行任意轨迹的并行 MRSI 侦察

• 批准号: 8169839
• 负责人: MENG GU
• 金额: $ 1.23万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8169839
• 关键词: Algorithms; Chemicals; Computer Retrieval of Information on Scientific Projects Database; Convolution Kernel; Data; Funding; Grant; Image; Imaging Techniques; Institution; Knowledge; Magnetic Resonance Spectroscopy; Memory; Methods; Poly(ADP-ribose) Polymerases; Research; Research Personnel; Resources; Sampling; Scanning; Source; Time; United States National Institutes of Health; Work; base; data space; in vivo; reconstruction

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. Fast imaging techniques using non- Cartesian k-space trajectories, e.g. spiral, have been implemented to reduce the long scan times associated with volumetric MRSI. The scan time can be further reduced by acquiring partial k-space data with multiple receiving coils and reconstruct using the knowledge of each coil's sensitivity profile. With k-space data on a Cartesian grid, reconstruction can be achieved using image-domain based SENSE algorithm or k-space-domain based GRAPPA algorithm. For non-Cartesian k-space data, image-domain based iterative SENSE algorithm or k-space-domain based PARS algorithm can be used for the reconstruction. Although effective, these non-Cartesian k-space data reconstruction methods suffer from long computing times. In this work, we propose a parallel MRSI reconstruction method with arbitrary trajectories using k-space sparse matrices (KSPA). The algorithm achieves reduced computing times and memory requirements by taking advantage of the compactness of the convolution kernel defined by the coil sensitivity. Reconstruction using this algorithm is demonstrated using undersampled spiral k-space data from an in-vivo study with different reduction factors. Methods and Discussion: To encode chemical shift information, fast MRSI with spiral k-space trajectories samples data points on repeated spiral trajectories. With the KSPA algorithm, the reconstruction matrix is calculated using k-space data on the first spiral trajectory and then applied to k-space data on the remaining spiral trajectories to estimate the fully sampled k-space data on repeated Cartesian grids. Since the reconstruction matrix only needs to be computed once, significant reconstruction time can be reduced.

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。 已经实现了使用非笛卡尔k空间轨迹(例如螺旋)的快速成像技术，以减少与体积MRSI相关的长扫描时间。通过使用多个接收线圈获取部分k空间数据，并利用每个线圈的灵敏度分布的知识进行重建，可以进一步减少扫描时间。对于笛卡尔网格上的k-空间数据，可以使用基于图像域的Sense算法或基于k-空间域的GRAPPA算法来实现重建。对于非笛卡尔k-空间数据，可以采用基于图像域的迭代Sense算法或基于k-空间域的PARS算法进行重建。这些非笛卡尔k空间数据重建方法虽然有效，但存在计算时间长的问题。在这项工作中，我们提出了一种基于k空间稀疏矩阵的任意轨迹的并行MRSI重建方法。该算法利用线圈灵敏度定义的卷积核的紧凑性，减少了计算时间和内存需求。使用不同缩减因子的活体研究中的欠采样螺旋k空间数据，演示了使用该算法进行重建。 方法和讨论： 为了编码化学位移信息，具有螺旋k空间轨迹的快速MRSI对重复的螺旋轨迹上的数据点进行采样。利用KSPA算法，利用第一个螺旋轨迹上的k空间数据计算重构矩阵，然后将其应用于剩余螺旋轨迹上的k空间数据，以估计重复笛卡尔网格上的全采样k空间数据。由于重建矩阵只需要计算一次，因此可以显著减少重建时间。