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

使用图割算法从多通道 K 空间数据进行贝叶斯重建

• 批准号: 8362778
• 负责人: Ashish Raj
• 金额: $ 9.48万
• 依托单位: NORTHERN CALIFORNIA INSTITUTE/RES/EDU
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8362778
• 关键词: Algorithms; Bayesian Method; Brain imaging; Cerebrospinal Fluid; Computer Vision Systems; Computer software; Data; Funding; Grant; Graph; Image; Joints; Magnetic Resonance Imaging; Modality; National Center for Research Resources; Neurodegenerative Disorders; Principal Investigator; Research; Research Infrastructure; Resources; Source; Tissues; United States National Institutes of Health; base; computer science; cost; data space; gray matter; imaging Segmentation; improved; novel; reconstruction; theories; white matter

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. 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

该子项目是利用资源的众多研究子项目之一 由 NIH/NCRR 资助的中心拨款提供。子项目的主要支持 并且子项目的主要研究者可能是由其他来源提供的， 包括其他 NIH 来源。 子项目可能列出的总成本 代表子项目使用的中心基础设施的估计数量， NCRR 赠款不直接向子项目或子项目工作人员提供资金。 目标 1. 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颖算法和软件。我们将考虑单模态和多模态数据。该项目将图像分割为 3 个组织类别：白质、灰质和脑脊液。我们将使用图形切割方法，这是流行的计算机视觉分割任务。 目标 2. 使用图割对并行 MRI 进行贝叶斯重建 该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例，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高效的 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 目标 3. 脑图像分割的图割算法 该重建核心子项目旨在改进图像分割。具体来说，目标是开发基于图割的新算法，以实现完全联合的图像分割、配准和偏差场校正