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: 8170580
• 负责人: Ashish Raj
• 金额: $ 6.56万
• 依托单位: NORTHERN CALIFORNIA INSTITUTE/RES/EDU
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8170580
• 关键词: Algorithms; Bayesian Method; Brain imaging; Cerebrospinal Fluid; Computer Retrieval of Information on Scientific Projects Database; Computer Vision Systems; Computer software; Data; Funding; Grant; Graph; Image; Institution; Joints; Magnetic Resonance Imaging; Modality; Research; Research Personnel; Resources; Source; Tissues; United States National Institutes of Health; base; computer science; 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. 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

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。 目的1.一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，这是计算机视觉中流行的分割任务。 目的2.基于图割的并行核磁共振贝叶斯重建 该项目旨在利用计算机科学和图论的最新进展，为重建磁共振并行成像(MRPI)数据开发一种新的算法范式。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。 目的3.一种用于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。具体地说，目标是开发基于图割的新算法，用于完全联合图像的分割、配准和偏场校正