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 研究中心，而研究中心不一定是研究者所在的机构。 目标1。 一种脑图像分割的图割算法 本项目旨在获得新的算法和软件的结构脑图像分割。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，这是流行的分割任务的计算机视觉。 目标2.基于图割的并行MRI贝叶斯重建 本项目旨在利用计算机科学和图论的最新进展，开发一种新的算法范式，用于重建MR并行成像（MRPI）数据。 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，有可能克服传统磁共振成像的基本限制 目标3。 一种脑图像分割的图割算法 重建核心中的这个子项目旨在改进图像分割。具体而言，目标是开发基于图割的新算法，用于完全联合的图像分割、配准和偏场校正

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. Aim 1. 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 popular is computer vision for segmentation tasks. Aim 2. Bayesian Reconstruction of Parallel MRI Using Graph Cuts 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 Aim 3. A Graph Cut Algorithm For Brain Image Segmentation This subproject in the reconstruction core aims to improve image segmentation. Specifically, the aim is to develop new algorithms based on Graph cuts for completely joint image segmentation, registration and bias field correction