Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Neuroinformatics. 2010 Mar;8(1):5-17. doi: 10.1007/s12021-009-9061-2.
Non-invasive neuroimaging techniques enable extraordinarily sensitive and specific in vivo study of the structure, functional response and connectivity of biological mechanisms. With these advanced methods comes a heavy reliance on computer-based processing, analysis and interpretation. While the neuroimaging community has produced many excellent academic and commercial tool packages, new tools are often required to interpret new modalities and paradigms. Developing custom tools and ensuring interoperability with existing tools is a significant hurdle. To address these limitations, we present a new framework for algorithm development that implicitly ensures tool interoperability, generates graphical user interfaces, provides advanced batch processing tools, and, most importantly, requires minimal additional programming or computational overhead. Java-based rapid prototyping with this system is an efficient and practical approach to evaluate new algorithms since the proposed system ensures that rapidly constructed prototypes are actually fully-functional processing modules with support for multiple GUI's, a broad range of file formats, and distributed computation. Herein, we demonstrate MRI image processing with the proposed system for cortical surface extraction in large cross-sectional cohorts, provide a system for fully automated diffusion tensor image analysis, and illustrate how the system can be used as a simulation framework for the development of a new image analysis method. The system is released as open source under the Lesser GNU Public License (LGPL) through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC).
非侵入性神经影像学技术使我们能够对生物机制的结构、功能反应和连接进行极其敏感和特异的活体研究。这些先进的方法对基于计算机的处理、分析和解释有很强的依赖性。虽然神经影像学领域已经产生了许多优秀的学术和商业工具包,但通常需要新的工具来解释新的模式和范例。开发定制工具并确保与现有工具的互操作性是一个重大的障碍。为了解决这些限制,我们提出了一个新的算法开发框架,该框架隐含地确保了工具的互操作性,生成图形用户界面,提供高级批处理工具,最重要的是,只需要很少的额外编程或计算开销。使用这个系统进行基于 Java 的快速原型设计是评估新算法的一种有效和实用的方法,因为所提出的系统确保了快速构建的原型实际上是具有多个图形用户界面、广泛的文件格式支持和分布式计算支持的全功能处理模块。在这里,我们展示了使用所提出的系统进行皮质表面提取的 MRI 图像处理,提供了一种用于全自动扩散张量图像分析的系统,并说明了如何将该系统用作开发新图像分析方法的模拟框架。该系统是根据较小的 GNU 公共许可证(LGPL)通过神经影像学信息学工具和资源知识库(NITRC)以开源形式发布的。