Development and Dissemination of Robust Brain MRI Measurement Tools

强大的脑 MRI 测量工具的开发和传播

• 批准号: 7556497
• 负责人: Dinggang Shen
• 金额: $ 40.3万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-17 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7556497
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water diffusion; web-enabled; white matter

DESCRIPTION (provided by applicant): Development and Dissemination of Robust Brain MRI Measurement Tools Abstract: This application responds to RFA: PAR-07-249, "Collaborations with National Centers for Biomedical Computing". The goal of this project is to develop and widely distribute a software package for robust measurement of brain structure in MR images by using computational neuroanatomy methods. The project will collaborate with the National Alliance for Medical Image Computing (NA-MIC) to develop the software using the NA-MIC Software Engineering Process, leverage the NA-MIC engineering infrastructure, and integrate this software into the 3D Slicer, a well-architected application environment being developed in NA-MIC. The particular software package will include both a brain image registration and warping algorithm, called HAMMER, and an algorithm for the segmentation of white matter lesions (WMLs), which can arise from a variety of pathologies including vascular pathology and multiple sclerosis. HAMMER received 2006 Best Paper Award from IEEE Signal Processing Society. HAMMER has been successfully applied to many large clinical research studies and clinical trials involving over 5,000 MR brain images and has been downloaded by 318 users from 102 institutions in over 20 countries. The WML segmentation algorithm has been successfully applied to "Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes" (ACCORD-MIND) sub- study, with data acquired from 4 centers on 650 patients over a period of 8 years. Designing an easy-to-use, robust software package for these two algorithms and incorporating it into the 3D Slicer will benefit a large community of end-users that need access to advanced image analysis methods in various neuroimaging studies. To increase the robustness of the algorithms to the highly variable quality and characteristics of clinical image data, further algorithm development is necessary. To increase ease of use by non-experts in computer analysis methods and integrate this software into the Slicer platform, significant software engineering efforts are planned. Three aims will be investigated. The first aim is to further develop and extend novel image analysis methods aiming at improving the robustness and performance of HAMMER registration and WML segmentation algorithms, so that they can be easily applied to various clinical research studies. The second and third aims are to design separate software modules for these two algorithms, and to incorporate them into the 3D Slicer. These two modules will be designed (1) with consistent cross-platform interactive and scripted interfaces, (2) allowing end-users to interactively explore the suitable parameters for their data, (3) enabling developers to add new functions. The robustness of these two modules will be extensively tested and improved by both software engineering tools and various clinical research data (acquired from different centers). The final software will be freely available in both source code and pre-compiled programs. PUBLIC HEALTH REVELANCE: The goal of this project is to develop and widely distribute a software package for robust measurement of brain structure in MR images by using computational neuroanatomy methods.

描述（由申请人提供）：强大的大脑MRI测量工具的开发和传播摘要：此申请对RFA：PAR-07-249，“与国家生物医学计算中心的合作”。该项目的目的是通过使用计算神经解剖学方法来开发和广泛分配一个软件包，以稳健地测量MR图像中的大脑结构。该项目将与全国医学图像计算联盟（NA-MIC）合作，使用NA-MIC软件工程流程开发软件，利用NA-MIC工程基础架构，并将该软件集成到3D Slicer中，这是一种在NA-MIC中开发的良好构建的应用程序环境。特定的软件包将包括大脑图像登记和称为Hammer的扭曲算法，以及用于分割白质病变（WML）的算法，这些算法源于包括血管病理和多发性硬化症在内的多种病理。 Hammer获得了IEEE信号处理协会的2006年最佳纸张奖。 Hammer已成功应用于许多涉及5,000多名MR脑图像的大型临床研究和临床试验，并已由202个国家 /地区的102个机构的318位用户下载。 WML分割算法已成功地应用于“糖尿病中糖尿病记忆中心血管风险的行动”（ACCORC-MIND）子研究，并从4年的450名患者中获得了40名患者的数据。为这两种算法设计一个易于使用，强大的软件包并将其纳入3D切片机，将使在各种神经影像学研究中需要访问高级图像分析方法的大型最终用户社区受益。为了将算法的鲁棒性提高到临床图像数据的高度可变质量和特征，需要进一步的算法开发。为了提高非专家在计算机分析方法中的易用性并将该软件集成到切片平台中，计划了重要的软件工程工作。将调查三个目标。第一个目的是进一步开发和扩展旨在提高Hammer注册和WML分割算法的稳健性和性能的新型图像分析方法，以便它们可以轻松地应用于各种临床研究。第二和第三的目的是为这两种算法设计单独的软件模块，并将它们纳入3D切片机。这两个模块将被设计（1）具有一致的跨平台交互式和脚本界面，（2）允许最终用户交互式探索其数据的合适参数，（3）使开发人员能够添加新功能。这两个模块的鲁棒性将通过软件工程工具和各种临床研究数据（从不同中心获取）进行广泛测试和改进。最终软件将在源代码和预编译程序中免费提供。公共卫生的启示：该项目的目的是通过使用计算神经解剖学方法来开发和广泛分配一个软件包，以在MR图像中稳健测量大脑结构。