Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI.
Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI.
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DOI：
10.1109/cvpr.2014.390
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Vemuri BC
中科院分区：
文献类型：
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作者：
Wang Y;Salehian H;Cheng G;Vemuri BC
纤维束成像是指从体内或体外获得的扩散磁共振图像 (dMRI) 数据中追踪出神经纤维束的过程。纤维束成像是扩散 MRI 分析领域的一个成熟的研究课题，然而，由于问题尚未完全解决，一些新方法正在定期提出，从而证明了这一需求的合理性。纤维束成像通常应用于从采集的数据重建的模型（用于表示扩散 MR 信号或导出量）。先前表明，这些模型的分离形状和方向可以在普遍存在的插值问题中大致保留扩散各向异性（一种有用的生物标记）。然而，迄今为止，文献中还没有进一步利用该框架的内在几何特性。在本文中，我们提出了一种关于形状和方向的乘积流形的新的内在递归滤波器。递归滤波器被称为 IUKFPro，是无迹卡尔曼滤波器 (UKF) 对此乘积流形的推广。这项工作的显着贡献是：（1）一种用于形状和方向的乘积流形的新内在 UKF。 (2)乘积流形黎曼几何的推导。 (3) IUKFPro 在来自各种纤维束成像挑战赛的合成数据集和真实数据集上进行了测试。从实验结果来看，很明显，IUKFPro 在比赛中使用的一些误差测量方面比文献中的几个竞争方案表现得更好，并且相对于其他方案具有竞争力。
Tractography refers to the process of tracing out the nerve fiber bundles from diffusion Magnetic Resonance Images (dMRI) data acquired either in vivo or ex-vivo. Tractography is a mature research topic within the field of diffusion MRI analysis, nevertheless, several new methods are being proposed on a regular basis thereby justifying the need, as the problem is not fully solved. Tractography is usually applied to the model (used to represent the diffusion MR signal or a derived quantity) reconstructed from the acquired data. Separating shape and orientation of these models was previously shown to approximately preserve diffusion anisotropy (a useful bio-marker) in the ubiquitous problem of interpolation. However, no further intrinsic geometric properties of this framework were exploited to date in literature. In this paper, we propose a new intrinsic recursive filter on the product manifold of shape and orientation. The recursive filter, dubbed IUKFPro, is a generalization of the unscented Kalman filter (UKF) to this product manifold. The salient contributions of this work are: (1) A new intrinsic UKF for the product manifold of shape and orientation. (2) Derivation of the Riemannian geometry of the product manifold. (3) IUKFPro is tested on synthetic and real data sets from various tractography challenge competitions. From the experimental results, it is evident that IUKFPro performs better than several competing schemes in literature with regards to some of the error measures used in the competitions and is competitive with respect to others.