3D RADIAL MR IMAGE RECONSTRUCTION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 径向编码MR成像（MRI）由于其运动不敏感性和改善的伪影特性，在超极化气体成像、对比增强MR血管造影和动态成像等应用中获得了越来越多的关注。然而，由于该技术径向地收集k空间样本，因此用于3D径向采样MRI的图像重建具有挑战性。当处理大数据集时，重建精度和速度之间的平衡变得至关重要。这项工作开发了通用的多维非均匀快速傅立叶变换（NUFFT）算法，并将其纳入k空间模拟和图像重建框架。应用NUFFT改进了数据驱动的网格化共轭相位重建方法，加快了最大似然重建速度。这项工作的目标是在非常大的图像尺寸和高维数据集上测试k空间模拟和重建算法。我们需要大的RAM（大于4G）和更快的处理（CPU）来协助我们的工作。

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. Radially encoded MR imaging (MRI) has gained increasing attention in applications such as hyperpolarized gas imaging, contrast-enhanced MR angiography, and dynamic imaging, due to its motion insensitivity and improved artifact properties. However, since the technique collects k-space samples radially, image reconstruction for 3D radially sampled MRI is challenging. The balance between reconstruction accuracy and speed becomes critical when a large data set is processed. The work develops general multi-dimensional nonuniform fast Fourier transform (NUFFT) algorithms and incorporates them into a k-space simulation and image reconstruction framework. applies NUFFT to improve the data-driven gridding method for conjugate phase reconstruction and accelerate maximum likelihood reconstruction. The goal of this work is to test the k-space simulation and reconstruction algorithms on very large image sizes and high dimension datasets. We need large RAM (larger than 4G) and faster processing (CPU) to assist our work.