Tractography gone wild: Probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI
Tractography gone wild: Probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI
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DOI：
10.1109/tmi.2008.922191
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发表时间：
2008-09-01
影响因子：
10.6
通讯作者：
Jones, Derek K.
中科院分区：
文献类型：
--
作者：
Jones, Derek K.
扩散张量磁共振成像(DT-MRI)允许对组织微结构进行非侵入性评估，并结合纤维跟踪算法允许非侵入性重建白质束的三维轨迹。概率算法允许人们为给定的重建路径分配一个“置信度”--但通常依赖于对数据中不确定性来源的先验假设。Bootstrap方法已经被提出作为一种循环发泄这个问题的方法，从数据本身推导出不确定性-但是对于服从于精确和健壮的Bootstrap的数据的获取时间在临床上是令人望而却步的。通过将最近引入DT-MRI文献的野生自举与纤维束成像相结合，我们展示了如何使用常规自举所需时间的一小部分收集的数据来为重建的轨迹分配置信度。我们将活体内野生自举跟踪结果与常规跟踪结果进行了比较，结果表明两者具有可比性。因此，这种方法允许已经收集了用于确定性跟踪算法的数据集的用户，而不是那些专门为引导而设计的数据集的用户，能够应用引导分析，并且以最少的额外努力向其重建的轨迹追溯地赋予置信度。
Diffusion tensor magnetic resonance imaging (DT-MRI) permits the noninvasive assessment of tissue microstructure and, with fibre-tracking algorithms, allows for the 3-D trajectories of white matter fasciculi to be reconstructed noninvasively. Probabilistic algorithms allow one to assign a "confidence" to a given reconstructed pathway-but often rely on a priori assumptions about sources of uncertainty in the data. Bootstrap methods have been proposed as a way of circum venting this problem, deriving the uncertainty from the data themselves-but acquisition times for data amenable to precise and robust bootstrapping are clinically prohibitive. By combining the wild bootstrap, recently introduced to the DT-MRI literature, with tractography, we show how confidence can be assigned to reconstructed trajectories using data collected in a fraction of the time required for regular bootstrapping. We compare in vivo wild bootstrap tracking results with regular tracking results and show that results are comparable. This approach therefore allows users who have collected data sets for use with deterministic tracking algorithms, rather than those specifically designed for bootstrapping, to be able to apply bootstrap analyses and retrospectively assign confidence to their reconstructed trajectories with minimum additional effort.