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

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 目标1。用于大脑图像分割的图形切割算法 该项目旨在获取用于结构大脑图像分割的新型算法和软件。我们将考虑单模式和多模式数据。该项目将导致图像分割成3个组织类别：白质，灰质和脑脊髓液。我们将使用剪切方法，这很受欢迎是用于分割任务的计算机视觉。 AIM 2。使用图切割的贝叶斯重建平行MRI 该项目旨在通过使用计算机科学和图理论的最新进展来开发一种新的算法范式来重建MR并行成像（MRPI）数据。 具体而言，我们将进一步开发和完善有效的MRI计算高效贝叶斯方法，这些方法有可能克服传统MR成像的基本限制 目标3。用于大脑图像分割的图形切割算法 重建核心中的该亚号旨在改善图像分割。具体而言，其目的是基于剪图剪切，以完全关节图像分割，注册和偏置场校正开发新算法