Integral curves from noisy diffusion MRI data with closed-form uncertainty estimates
Integral curves from noisy diffusion MRI data with closed-form uncertainty estimates
复制标题
具有封闭形式不确定性估计的噪声扩散 MRI 数据的积分曲线
DOI：
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
L. Sakhanenko
中科院分区：
文献类型：
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作者：
Owen Carmichael;L. Sakhanenko
我们提出了一种通过扩散张量MRI（DTI）数据，提供理论上严格的轨迹不确定性估计轴突纤维的轨迹的方法。我们开发了一个三步估计过程的基础上的核估计的张量场的基础上的原始DTI测量，其次是一个插件估计的张量的领先特征向量，和插件估计的积分曲线通过所得的向量场。积分曲线估计量是渐近正态的;极限高斯过程的协方差允许我们为沿着曲线的固定点构造置信椭圆。通过在具有多个可行的前导特征向量方向的位置停止积分曲线追踪并沿着每个方向追踪新的曲线沿着来组装纤维的完整轨迹。不同于概率纤维束成像方法来解决这个问题，我们提供了一个严格的，理论上健全的模型，测量不确定性，因为它从原始MRI数据，张量场，向量场，积分曲线。此外，轨迹的不确定性估计在封闭的形式，而概率纤维束成像依赖于采样张量，矢量或曲线的空间。我们表明，我们的估计器提供了更现实的轨迹不确定性估计比一个更简化的封闭形式的轨迹不确定性估计，由于Koltchinskii等人（Ann Stat 35：1576-1607，2007）和流行的概率纤维束成像方法，由于Behrens等人（Magn Reson Med 50：1077-1088，2003）使用理论，模拟，和真实的DTI扫描。
We present a method for estimating the trajectories of axon fibers through diffusion tensor MRI (DTI) data that provides theoretically rigorous estimates of trajectory uncertainty. We develop a three-step estimation procedure based on a kernel estimator for a tensor field based on the raw DTI measurements, followed by a plug-in estimator for the leading eigenvectors of the tensors, and a plug-in estimator for integral curves through the resulting vector field. The integral curve estimator is asymptotically normal; the covariance of the limiting Gaussian process allows us to construct confidence ellipsoids for fixed points along the curve. Complete trajectories of fibers are assembled by stopping integral curve tracing at locations with multiple viable leading eigenvector directions and tracing a new curve along each direction. Unlike probabilistic tractography approaches to this problem, we provide a rigorous, theoretically sound model of measurement uncertainty as it propagates from the raw MRI data, to the tensor field, to the vector field, to the integral curves. In addition, trajectory uncertainty is estimated in closed form while probabilistic tractography relies on sampling the space of tensors, vectors, or curves. We show that our estimator provides more realistic trajectory uncertainty estimates than a more simplified prior approach for closed-form trajectory uncertainty estimation due to Koltchinskii et al. (Ann Stat 35:1576–1607, 2007) and a popular probabilistic tractography method due to Behrens et al. (Magn Reson Med 50:1077–1088, 2003) using theory, simulation, and real DTI scans.
DOI：
10.1016/j.mric.2009.01.011
发表时间：
2009-05
影响因子：
1.6
作者：
Bammer, Roland;Holdsworth, Samantha J.;Veldhuis, Wouter B.;Skare, Stefan T.
通讯作者：
Skare, Stefan T.