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: 7957226
• 负责人: Ashish Raj
• 金额: $ 10.99万
• 依托单位: NORTHERN CALIFORNIA INSTITUTE/RES/EDU
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7957226
• 关键词: Algorithms; Bayesian Method; Brain imaging; Cerebrospinal Fluid; Computer Retrieval of Information on Scientific Projects Database; Computer Vision Systems; Computer software; Data; Drug Formulations; Funding; Grant; Graph; Image; Imaging problem; Institution; Magnetic Resonance Imaging; Modality; Neurodegenerative Disorders; Research; Research Personnel; Resources; Source; Techniques; Tissues; United States National Institutes of Health; base; computer science; data space; gray matter; imaging Segmentation; 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. 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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 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。我们将使用图割方法，这是一种流行的用于分割任务的计算机视觉。 用于并行 MRI 重建的图割算法：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。最近提出了一种基于图的公式，用于使用 MRF 先验的并行成像问题。 该子项目将进一步完善这项技术，以适应结构和动态脑成像的具体应用。