Gridifying a Diffusion Tensor Imaging Analysis Pipeline

M. Caan, F. Vos, A. V. Kampen, S. Olabarriaga, L. Vliet
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引用次数: 8

Abstract

Diffusion Tensor MRI (DTI) is a rather recent image acquisition modality that can help identify disease processes in nerve bundles in the brain. Due to the large and complex nature of such data, its analysis requires new and sophisticated pipelines that are more efficiently executed within a grid environment. We present our progress over the past four years in the development and porting of the DTI analysis pipeline to grids. Starting with simple jobs submitted from the command-line, we moved towards a workflow-based implementation and finally into a web service that can be accessed via web browsers by end-users. The analysis algorithms evolved from basic to state-of-the-art, currently enabling the automatic calculation of a population-specific ‘atlas’ where even complex brain regions are described in an anatomically correct way. Performance statistics show a clear improvement over the years, representing a mutual benefit from both a technology push and application pull.
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扩散张量成像分析管道的网格化
弥散张量MRI (DTI)是一种较新的图像采集方式,可以帮助识别大脑神经束的疾病过程。由于此类数据的庞大和复杂性质,其分析需要在网格环境中更有效地执行的新的和复杂的管道。我们介绍了过去四年在开发和移植DTI分析管道到电网方面的进展。从从命令行提交的简单作业开始,我们转向了基于工作流的实现,最后进入了最终用户可以通过web浏览器访问的web服务。分析算法从基本的发展到最先进的技术,目前能够自动计算特定人群的“图谱”,即使是复杂的大脑区域也能以解剖学上正确的方式描述。性能统计数据显示多年来有了明显的改善,这代表了技术推动和应用程序拉动的共同利益。
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