Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques
Flow-based fiber tracking with diffusion tensor and q-ball data: Validation and comparison to principal diffusion direction techniques
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DOI：
10.1016/j.neuroimage.2005.05.014
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发表时间：
2005-10-01
期刊：
影响因子：
5.7
通讯作者：
Pike, GB
中科院分区：
文献类型：
--
作者：
Campbell, JSW;Siddiqi, K;Pike, GB
在这项研究中，我们评估了一种基于流动的表面演化纤维跟踪算法的性能，该算法通过一个具有已知连接性的物理各向异性扩散模型来实现。我们引入了一种新的表面演化速度函数，它是由扩散张量(DT)数据、高角分辨率扩散(HARD)数据或DT-HARD混合方法得出的。我们使用无模型Q球成像(QBI)方法进行硬重建。各向异性扩散体模允许我们在存在真实成像伪影、噪声和纤维方向的亚像素部分体积平均的情况下比较和评估不同的纤维跟踪方法的性能。将使用全扩散张量而不是仅使用主扩散方向(PDD)的表面演化方法与基于PDD的线传播光纤跟踪进行了比较。此外，对于纤维跟踪，DT重建与硬重建进行了比较，两者都使用了表面演化。我们展示了使用全扩散张量来映射纤维方向的亚体素部分体积平均区域中的连接的表面演化的潜力，这可能是基于PDD的方法难以映射的。结果表明，在扩散张量模型较差的情况下，通过对扩散取向分布函数进行高角度分辨率的重建，可以改善纤维跟踪的结果。(C)2005 Elsevier Inc.保留所有权利。
In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly. (C) 2005 Elsevier Inc. All rights reserved.