基于odf集合的HARDI不确定性可视化。

Fangxiang Jiao, Jeff M Phillips, Yaniv Gur, Chris R Johnson
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引用次数: 37

摘要

本文提出了一种基于光纤取向分布函数(ODF)符号的不确定度分析和不确定度可视化新技术,并结合了高角分辨扩散成像(HARDI)技术。我们的可视化将体绘制技术应用于3D ODF字形的集合,我们称之为扩散形状的SIP函数,以捕获它们由于潜在不确定性而产生的可变性。这个渲染说明了这些形状复杂的异方差结构变化。此外,我们通过测量这些形状的体积分数来量化这种变化的程度,这在所有噪音水平下都是一致的,一定的体积比。然后将我们的不确定性分析和可视化框架应用于合成数据以及HARDI人脑数据,以研究各种图像采集参数和背景噪声水平对扩散形状的影响。
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Uncertainty Visualization in HARDI based on Ensembles of ODFs.

In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.

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