Development of Software for Automated Quantification of Brain MR Images

脑 MR 图像自动量化软件的开发

• 批准号: 8934185
• 负责人: hangyi jiang
• 金额: $ 46.53万
• 依托单位: ANATOMYWORKS, LLC
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8934185
• 关键词: Adopted; Algorithms; Anatomy; Architecture; Atlases; Automation; Beds; Brain; Brain Diseases; Clinical; Clinical Data; Collaborations; Communities; Computer software; Data; Databases; Development; Diffusion Magnetic Resonance Imaging; Dimensions; Equipment and supply inventories; Fees; Health; Image; Image Analysis; Location; Magnetic Resonance Imaging; Manuals; Modeling; Morus; Online Systems; Ontology; Operating System; Pathology; Patients; Performance; Phase; Phenotype; Picture Archiving and Communication System; Population; Probability; Protocols documentation; Reproducibility; Research; Resolution; Resources; Running; Science; Services; Source; Structure; System; Technology; Testing; Time; Universities; Vendor; Weight; base; clinical practice; cloud based; computer cluster; computerized data processing; computing resources; contrast imaging; cost; data visualization; flexibility; improved; novel; parallel processing; phase 1 study; phase 2 study; programs; software development; tool; web based interface; web interface

DESCRIPTION (provided by applicant): In this project, we will develop a commercial resource for the automated analysis of brain anatomy, based on MRI. This product is based on the whole-brain parcellation algorithm with the following unique features. First, it is based on a cutting-edge multi-atlas approach, in which we will incorporate rich atlas resources from Dr. Mori's lab at the Johns Hopkins University (JHU). Second, our multi-atlas approach is based on advanced diffeomorphic image transformation and multi-atlas probability fusion, recently developed by Dr. Miller at JHU. These CPU-intensive algorithms, combined with a large atlas inventory, require highly parallelized computational resources. We, therefore, will develop a fully portable and scalable cloud-based architecture, such that many users can have access at minimum costs. Third, we will develop a flexible architecture to define brain structures with multiple anatomical criteria, providing a very unique multi-granularity analysis, which provides an anatomy-centric and intuitive interface for clinical use. Fourth, we extend the analysis to diffusion tensor imaging (DTI) by incorporating a unique approach to multi-contrast image transformation and probability fusion. Last but not least, these algorithms can convert a set of multiple MR images to a quantitative and standardized Anatomical Matrix, which allows us to perform image data structurization, searching, and individualized analysis of anatomical phenotypes. Aim 1: To establish a cloud-based servicing architecture: We will develop a scalable and portable architecture for cloud-based computation. Parallel processing is required to achieve fast computation for the multi-atlas calculations. The algorithms accept DICOM data from four major vendors and apply a parcellation tool that identifies 254 brain structures. Aim 2: To establish a web-based interface for non-corporate users: To make our advanced image analysis tools widely available for research communities, we will create a web-based interface and provide the service at a minimum cost ($20/data). Aim 3: To implement a data visualization interface with ontology-based multi-granularity analysis: Our image analysis pipeline is a departure from conventional voxel-based automated analysis. Our structure-based analysis reduces the anatomical dimension to much lower scales. However, there are multiple ways to perform the structure-based information reduction. The ontology-based analysis provides a novel way to perform hierarchical anatomical interpretation of the structure-based analysis. Aim 4: To increase the number of atlases and cases in the database for interpretation support: Through the collaboration with JHU, we have access to a large inventory of research and clinical data, including controls and various patient groups. To create reference data, we will process these data and establish a background database, against which users can compare and interpret their data.

描述（由申请人提供）：在这个项目中，我们将开发一个商业资源，用于基于MRI的脑解剖结构的自动分析。该产品基于全脑分割算法，具有以下独特功能。首先，它是基于一个先进的多图集的方法，其中我们将纳入丰富的图集资源，从森博士的实验室在约翰霍普金斯大学（JHU）。其次，我们的多图集方法是基于先进的同构图像变换和多图集概率融合，最近开发的米勒博士在JHU。这些CPU密集型算法与大型图集库存相结合，需要高度并行化的计算资源。因此，我们将全面发展 便携式和可扩展的基于云的架构，使许多用户可以以最低的成本访问。第三，我们将开发一种灵活的架构，以多种解剖标准定义大脑结构，提供非常独特的多粒度分析，为临床使用提供以解剖为中心的直观界面。第四，我们将分析扩展到扩散张量成像（DTI），采用独特的方法进行多对比度图像变换和概率融合。最后但并非最不重要的是，这些算法可以将一组多个MR图像转换为定量和标准化的解剖矩阵，这使我们能够执行图像数据结构化，搜索和解剖表型的个性化分析。目标1：建立基于云的服务架构：我们将为基于云的计算开发一个可扩展和可移植的架构。需要并行处理来实现多图谱计算的快速计算。该算法接受来自四家主要供应商的DICOM数据，并应用一种可识别254个大脑结构的分割工具。目标二：为非公司用户建立基于网络的界面：为了使我们的先进图像分析工具广泛用于研究社区，我们将创建一个基于网络的界面，并以最低成本（20美元/数据）提供服务。目标3：为了实现基于本体的多粒度分析的数据可视化接口：我们的图像分析管道是从传统的基于体素的自动化分析出发。我们基于结构的分析将解剖维度降低到更低的尺度。然而，有多种方式来执行基于结构的信息缩减。基于本体的分析提供了一种新的方式来执行基于结构的分析的层次解剖解释。目标4：为了增加数据库中用于解释支持的图谱和病例数量：通过与JHU的合作，我们可以访问大量的研究和临床数据，包括对照组和各种患者组。为了创建参考数据，我们将处理这些数据并建立一个后台数据库，用户可以根据该数据库比较和解释他们的数据。