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.
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