Carman and Ann Adams Department of Pediatrics, Children's Hospital of Michigan, Detroit, Michigan, USA.
Magn Reson Med. 2013 Aug;70(2):441-53. doi: 10.1002/mrm.24487. Epub 2012 Sep 21.
The independent component analysis (ICA) tractography method has improved the ability to isolate intravoxel crossing fibers; however, the accuracy of ICA is limited in cases with voxels in local clusters lacking sufficient numbers of fibers with the same orientations. To overcome this limitation, the ICA was combined with a ball-stick model (BSM) ["ICA+BSM"]. An ICA approach is applied to identify crossing fiber components in voxels of small cluster, which are maximally independent in orientation. The eigenvectors of these components are numerically optimized via the subsequent BSM procedure. Simulation studies for two or three crossing fibers demonstrate that ICA+BSM overcomes the limitation of the original ICA method by refining regional ICA solutions in diffusion measurement of a single voxel. It shows 2°-5° of angular errors to isolate two or three fibers, providing a better recovery of simulated fibers compared with ICA alone. Human studies show that ICA+BSM achieves high anatomical correspondence of corticospinal tracts compared with postmortem corticospinal histology, yielding 92.2% true positive detection including both lateral and medial projections, compared with 84.1% for ICA alone. This study demonstrates that the intravoxel crossing fiber problem in clinical diffusion MRI may be sorted out more efficiently by combining ICA with BSM.
独立成分分析(ICA)轨迹追踪方法提高了分离体素内交叉纤维的能力;然而,在局部聚类的体素中缺乏具有相同方向的足够数量纤维的情况下,ICA 的准确性受到限制。为了克服这一限制,将 ICA 与球棒模型(BSM)相结合[ICA+BSM]。ICA 方法用于识别小簇体素中交叉纤维成分,这些成分在方向上是最大程度独立的。通过随后的 BSM 过程对这些成分的特征向量进行数值优化。对于两个或三个交叉纤维的仿真研究表明,ICA+BSM 通过细化单个体素扩散测量中的局部 ICA 解,克服了原始 ICA 方法的局限性。与单独使用 ICA 相比,它可以将两个或三个纤维的角度误差控制在 2°-5°,从而更好地恢复模拟纤维。人体研究表明,与死后皮质脊髓组织学相比,ICA+BSM 实现了皮质脊髓束的高解剖对应性,包括外侧和内侧投射在内的真实阳性检出率为 92.2%,而单独使用 ICA 为 84.1%。这项研究表明,通过将 ICA 与 BSM 相结合,可能更有效地解决临床扩散 MRI 中的体素内交叉纤维问题。