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Probabilistic tractography using Q-ball imaging and particle filtering: Application to adult and in-utero fetal brain studies

机译:使用Q球成像和粒子过滤的概率性体检术:在成人和子宫内胎儿脑研究中的应用

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摘要

By assuming that orientation information of brain white matter fibers can be inferred from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) measurements, tractography algorithms provide an estimation of the brain connectivity in vivo. The two key ingredients of tractography are the diffusion model (tensor, high-order tensor, Q-ball, etc.) and the means to deal with uncertainty during the tracking process (deterministic vs probabilistic mathematical framework). In this paper, we investigate the use of an analytical Q-ball model for the diffusion data within a well-formalized particle filtering framework. The proposed method is validated and compared to other tracking algorithms on the MICCAI'09 contest Fiber Cup phantom. Tractographies of in vivo adult and fetal brain Diffusion-Weighted Images (DWIs) are also shown to illustrate the robustness of the algorithm.
机译:通过假设可以从弥散加权磁共振成像(DW-MRI)测量中推断出脑白质纤维的取向信息,射线照相术算法可以估算体内的大脑连通性。弹力学的两个关键要素是扩散模型(张量,高阶张量,Q球等)和在跟踪过程中处理不确定性的手段(确定性与概率性数学框架)。在本文中,我们调查了在形式化良好的粒子过滤框架内对扩散数据使用解析Q球模型的情况。对该方法进行了验证,并与MICCAI'09竞赛纤维杯模型上的其他跟踪算法进行了比较。还显示了体内成人和胎儿脑弥散加权图像(DWI)的术式,以说明该算法的鲁棒性。

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