BAYESIAN RECONSTRUCTION FROM MULTICHANNEL K-SPACE DATA USING GRAPH-CUT ALGORITHM

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。 一种用于脑图像分割的图割算法：该项目旨在获得用于结构脑图像分割的新算法和软件。我们将同时考虑单通道数据和多通道数据。该项目将把图像分割成3个组织类别：白质、灰质和脑脊液。我们将使用图割方法，这是一种用于分割任务的流行的计算机视觉。 用于并行MRI重建的图割算法：该项目旨在利用计算机科学和图论的最新进展，开发一种用于重建MR并行成像(MRPI)数据的新的算法范式。具体地说，我们将进一步开发和改进计算效率高的MRI贝叶斯方法，这些方法有可能克服传统MR成像的基本限制。最近提出了一种基于图的形式来解决基于马尔可夫随机场先验的并行成像问题。这一子项目将进一步改进这项技术，使其适用于结构和动态脑成像的具体应用。

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. A Graph Cut Algorithm For Brain Image Segmentation: This project aims to obtain novel algorithms and software for structural brain image segmentation. We will consider both single-modality as well as multi-modality data. The project will result in image segmentation into 3 tissue classes: white matter, gray matter and cerebro-spinal fluid. We will use a graph cut approach, which is a popular computer vision for segmentation tasks. Graph Cut Algorithm For Reconstruction of Parallel MRI: This project aims to develop a new algorithmic paradigm for the reconstruction of MR Parallel Imaging (MRPI) data by using recent advances in Computer Science and Graph Theory. Specifically, we will further develop and refine computationally efficient Bayesian methods for MRI that have the potential to overcome fundamental limits of traditional MR imaging. A graph-based formulation was recently proposed for the Parallel Imaging problem using MRF priors. This subproject will further refine this technique for specific applications in structural and dynamic brain imaging.