Versatile, robust, and efficient tractography with constrained higher-order tensor fODFs
Versatile, robust, and efficient tractography with constrained higher-order tensor fODFs
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DOI：
10.1007/s11548-017-1593-6
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发表时间：
2017-08-01
影响因子：
3
通讯作者：
Schultz, Thomas
中科院分区：
文献类型：
--
作者：
Ankele, Michael;Lim, Lek-Heng;Schultz, Thomas
目的开发一种多纤维纤维束成像方法，该方法基于来自广泛的扩散MRI协议的输入数据，包括高角分辨率扩散成像，多壳成像和临床扩散光谱成像（DSI）方法，产生快速和稳健的结果。在不同类型的扩散MRI协议的统一反卷积框架中，我们将纤维取向分布函数表示为高阶张量。它允许使用一种新的正确定性约束（H-psd），使噪声输入的估计更加稳健。所得方向用于带分支的确定性光纤跟踪。结果我们量化了模拟数据的准确性，以及临床数据的条件数和计算次数。我们定性地调查了处理次优数据时的好处，并展示了与几种最先进技术的直接比较。结论该方法比现有的方法运行速度更快，在已知地面真值的模拟数据上获得更高的角度分辨率，在临床数据上获得可信的结果。除了使用与以前的多组织反褶积方法相同的数据外，它还支持DSI数据。
Purpose Develop a multi-fiber tractography method that produces fast and robust results based on input data from a wide range of diffusion MRI protocols, including high angular resolution diffusion imaging, multi-shell imaging, and clinical diffusion spectrum imaging (DSI)Methods In a unified deconvolution framework for different types of diffusion MRI protocols, we represent fiber orientation distribution functions as higher-order tensors, which permits use of a novel positive definiteness constraint (H-psd) that makes estimation from noisy input more robust. The resulting directions are used for deterministic fiber tracking with branching.Results We quantify accuracy on simulated data, as well as condition numbers and computation times on clinical data. We qualitatively investigate the benefits when processing suboptimal data, and show direct comparisons to several state-of-the-art techniques.Conclusion The proposed method works faster than state-of-the-art approaches, achieves higher angular resolution on simulated data with known ground truth, and plausible results on clinical data. In addition to working with the same data as previous methods for multi-tissue deconvolution, it also supports DSI data.