BAYESIAN IMAGE RECONSTRUCTION FROM REDUCED K-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。 该项目的总体目标是开发和评估一种新的用于低分辨率MRI模式的贝叶斯重建方法，该方法相对于标准的离散傅立叶变换方法可以减少伪影并有效地提高分辨率。新的重建方法充分利用k空间数据来减少伪影，分辨率的提高是通过结合在同一扫描过程中获得的分段结构MRI扫描的高分辨率信息来实现的。资源研究应用程序的工作重点将是直接应用、扩展和验证贝叶斯重建方法。磁共振灌注成像(PWI)是一种特殊的检查方法。该项目的具体目标是1)将贝叶斯低分辨率重建算法应用于PWI；2)相对于标准DFT重建验证贝叶斯算法；3)研究贝叶斯重建方法对未配准和分割错误的稳健性；以及4)将贝叶斯重建方法应用于全面的临床研究。行政补充项目的目标是：1)将方法扩展到3D Grase；2)评估和验证3D Grase的K-Bayes。

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. The overall goal of this project is to develop and evaluate a new Bayesian reconstruction method for low-resolution MRI modalities that reduce artifacts and effectively increase resolution relative to standard Discrete Fourier Transform approaches. The new reconstruction method fully utilizes k-space data to reduce artifacts and the increase in resolution is achieved by incorporating high-resolution information from segmented structural MRI scans acquired in the same scanning session. The focus of the work within the Resource Research application will be to directly apply, extend and validate the Bayesian reconstruction methodology. Perfusion-Weighted MR Imaging (PWI) is the particular modality chosen for application. The Specific Aims of the project are to 1) To apply the Bayesian low-resolution reconstruction algorithm to PWI; 2) To validate the Bayesian algorithm relative to standard DFT reconstruction; 3) To study the robustness of the Bayesian reconstruction method to miss-registration and segmentation error; and 4) To apply the Bayesian reconstruction methodology to a full clinical study. The Administrative Supplement project aims to AS1) extend methods to 3D GRASE and AS2) evaluate and validate K-Bayes for 3D GRASE.