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: 8832164
• 负责人: hangyi jiang
• 金额: $ 52.48万
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8832164
• 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; Image; Image Analysis; Imagery; 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; cost; flexibility; improved; novel; parallel processing; phase 1 study; phase 2 study; programs; public health relevance; 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，我们将开发一个基于MRI的脑解剖自动分析的商业资源。本产品基于全脑分割算法，具有以下独特之处。首先，它基于一种尖端的多图谱方法，在这种方法中，我们将结合来自约翰霍普金斯大学（JHU） Mori博士实验室的丰富图谱资源。其次，我们的多图谱方法基于JHU的Miller博士最近开发的先进的微分纯图像变换和多图谱概率融合。这些cpu密集型算法，加上庞大的地图集库存，需要高度并行化的计算资源。因此，我们将充分发展