Tractography from HARDI using an intrinsic unscented Kalman filter.
Tractography from HARDI using an intrinsic unscented Kalman filter.
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DOI：
10.1109/tmi.2014.2355138
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发表时间：
2015-01
影响因子：
10.6
通讯作者：
Vemuri BC
中科院分区：
文献类型：
--
作者：
Cheng G;Salehian H;Forder JR;Vemuri BC
最近有文献介绍了一种新的无气味卡尔曼滤波器（UKF），用于同时进行多张量估计和弥散MRI纤维束成像。由于UKF同时估计扩散张量并沿着最一致的方向传播，因此在计算效率方面，该技术比其他轨迹成像方法具有优势。然而，这个UKF及其变体在后来的文献中并不是扩散张量空间固有的。缺乏这一关键特性可能导致多张量估计和牵引成像的不准确性。本文在对称正定矩阵扩散张量空间中提出了一种新的本征无嗅卡尔曼滤波器（IUKF），该滤波器可用于同时递推估计多张量和传播方向信息。除了更加准确之外，IUKF还保留了UKF的所有优点。我们通过公开的纤维杯挑战实验（MICCAI 2009）和从人脑和大鼠脊髓获得的弥散加权磁共振扫描数据，证明了所提出方法的准确性和有效性。
A novel adaptation of the unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multi-tensor estimation and fiber tractography from diffusion MRI. This technique has the advantage over other tractography methods in terms of computational efficiency, due to the fact that the UKF simultaneously estimates the diffusion tensors and propagates the most consistent direction to track along. This UKF and its variants reported later in literature however are not intrinsic to the space of diffusion tensors. Lack of this key property can possibly lead to inaccuracies in the multi-tensor estimation as well as in the tractography. In this paper, we propose a novel intrinsic unscented Kalman filter (IUKF) in the space of diffusion tensors which are symmetric positive definite matrices, that can be used for simultaneous recursive estimation of multi-tensors and propagation of directional information for use in fiber tractography from diffusion weighted MR data. In addition to being more accurate, IUKF retains all the advantages of UKF mentioned above. We demonstrate the accuracy and effectiveness of the proposed method via experiments publicly available phantom data from the fiber cup-challenge (MICCAI 2009) and diffusion weighted MR scans acquired from human brains and rat spinal cords.