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的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 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 中心，不一定是研究者的机构。 脑图像分割的图切割算法：本项目旨在获得结构脑图像分割的新算法和软件。我们将同时考虑单模态和多模态数据。该项目将导致图像分割为3种组织类别：白色物质、灰质和脊髓液。我们将使用图切割方法，这是一种用于分割任务的流行计算机视觉。 用于并行MRI重建的图切割算法：该项目旨在通过使用计算机科学和图论的最新进展开发用于MR并行成像（MRPI）数据重建的新算法范例。 具体来说，我们将进一步开发和完善计算效率高的贝叶斯方法的MRI，有可能克服传统的MR成像的基本限制。最近提出了一种基于图的配方的并行成像问题使用MRF先验。 这个子项目将进一步完善这种技术在结构和动态脑成像中的具体应用。

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.