首页> 外文会议>International conference on medical image computing and computer-assisted intervention;MICCAI 2010 >Model-Free, Regularized, Fast, and Robust Analytical Orientation Distribution Function Estimation
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Model-Free, Regularized, Fast, and Robust Analytical Orientation Distribution Function Estimation

机译:无模型,正则化,快速且鲁棒的分析方向分布函数估计

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High Angular Resolution Imaging (HARDI) can better explore the complex micro-structure of white matter compared to Diffusion Tensor Imaging (DTI). Orientation Distribution Function (ODF) in HARDI is used to describe the probability of the fiber direction. There are two type definitions of the ODF, which were respectively proposed in Q-Ball Imaging (QBI) and Diffusion Spectrum Imaging (DSI). Some analytical reconstructions methods have been proposed to estimate these two type of ODFs from single shell HARDI data. However they all have some assumptions and intrinsic modeling errors. In this article, we propose, almost without any assumption, a uniform analytical method to estimate these two ODFs from DWI signals in q space, which is based on Spherical Polar Fourier Expression (SPFE) of signals. The solution is analytical and is a linear transformation from the q-space signal to the ODF represented by Spherical Harmonics (SH). It can naturally combines the DWI signals in different Q-shells. Moreover It can avoid the intrinsic Funk-Radon Transform (FRT) blurring error in QBI and it does not need any assumption of the signals, such as the multiple tensor model and mono/multi-exponential decay. We validate our method using synthetic data, phantom data and real data. Our method works well in all experiments, especially for the data with low SNR, low anisotropy and non-exponential decay.
机译:与扩散张量成像(DTI)相比,高角度分辨率成像(HARDI)可以更好地探索白质的复杂微观结构。 HARDI中的方向分布函数(ODF)用于描述纤维方向的概率。 ODF有两种类型定义,分别在Q-Ball成像(QBI)和扩散光谱成像(DSI)中提出。已经提出了一些解析重建方法来从单层HARDI数据估计这两种类型的ODF。但是,它们都有一些假设和固有的建模错误。在本文中,我们几乎没有任何假设地提出了一种统一的分析方法,该方法基于信号的球极傅立叶表达式(SPFE)从q空间中的DWI信号估计这两个ODF。该解决方案是解析性的,是从q空间信号到以球谐(SH)表示的ODF的线性变换。它可以自然地将DWI信号组合在不同的Q壳中。此外,它可以避免QBI中的固有Funk-Radon变换(FRT)模糊误差,并且不需要对信号进行任何假设,例如多张量模型和单/多指数衰减。我们使用合成数据,幻像数据和真实数据验证我们的方法。我们的方法在所有实验中均能很好地工作,特别是对于具有低SNR,低各向异性和非指数衰减的数据。

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