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）：在此项目中，我们将基于MRI开发一种用于大脑解剖结构的自动分析的商业资源。该产品基于具有以下独特功能的整个脑拟合算法。首先，它基于一种尖端的多ATLA方法，在该方法中，我们将在约翰·霍普金斯大学（JHU）的莫里（Johns Hopkins）实验室（JHU）的莫里（Mori）实验室中纳入丰富的Atlas Resources。其次，我们的多ATLA方法基于高级二型图像转换和多ATLAS概率融合，该概率是JHU博士最近开发的。这些CPU密集型算法，结合大量Atlas库存，需要高度平行的计算资源。因此，我们将充分发展 便携式且可扩展的基于云的体系结构，因此许多用户可以以最低成本访问。第三，我们将开发一种灵活的体系结构来定义具有多个解剖标准的大脑结构，从而提供了非常独特的多粒性分析，该分析提供了以解剖为中心和直观的界面供临床使用。第四，我们通过结合了多对比度图像转换和概率融合的独特方法，将分析扩展到扩散张量成像（DTI）。最后但并非最不重要的一点是，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，这使我们能够对解剖表型进行图像数据结构化，搜索和个性化分析。 AIM 1：建立基于云的维修体系结构：我们将开发一个可扩展的便携式体系结构，用于基于云的计算。需要并行处理以实现多ATLAS计算的快速计算。该算法接受来自四个主要供应商的DICOM数据，并应用了识别254个大脑结构的分析工具。 AIM 2：为非公司用户建立基于Web的界面：为了使我们的高级图像分析工具可为研究社区广泛使用，我们将创建一个基于Web的界面，并以最低成本（20美元/数据）提供服务。 AIM 3：通过基于本体的多粒性分析实施数据可视化界面：我们的图像分析管道与传统的基于体素的自动化分析背道而驰。我们的基于结构的分析将解剖学维度降低到较低的尺度。但是，有多种方法可以减少基于结构的信息。基于本体的分析提供了一种对基于结构分析的层次解剖学解释的新方法。目标4：为了增加数据库中的地图集和案例的数量以进行解释支持：通过与JHU的合作，我们可以访问大量研究和临床数据的清单，包括控件和各种患者组。要创建参考数据，我们将处理这些数据并建立一个背景数据库，用户可以对其进行比较和解释其数据。