Kumar Ritwik, Vemuri Baba C, Wang Fei, Syeda-Mahmood Tanveer, Carney Paul R, Mareci Thomas H
Dept. of CISE, University of Florida, Gainesville, FL, USA.
Inf Process Med Imaging. 2009;21:139-50. doi: 10.1007/978-3-642-02498-6_12.
Multi-fiber reconstruction has attracted immense attention lately in the field of diffusion weighted MRI analysis. Several mathematical models have been proposed in literature but there is still scope for improvement. The key issues of importance in multi-fiber reconstruction are, fiber detection accuracy, robustness to noise and computational efficiency. To this end, we propose a novel mathematical model for representing the MR signal attenuation in the presence of multiple fibers at a single voxel and estimate the parameters of this model given the diffusion weighted MRI data. Our model for the diffusion MR signal consists of a continuous mixture of Hyperspherical von Mises-Fisher distributions. Being a continuous mixture, our model does not require the specification of the number of mixture components. We present a closed form expression for this continuous mixture that leads to a computationally efficient implementation. To validate our model we present extensive results on both synthetic and real data (human and rat brain) and demonstrate that even in presence of noise, our model clearly outperforms the state-of-the-art methods in fiber orientation estimation while maintaining a substantial computational advantage.
多纤维重建最近在扩散加权磁共振成像（MRI）分析领域引起了极大关注。文献中已经提出了几种数学模型，但仍有改进的空间。多纤维重建中重要的关键问题包括纤维检测精度、对噪声的鲁棒性和计算效率。为此，我们提出了一种新颖的数学模型，用于表示在单个体素存在多根纤维时的磁共振信号衰减，并根据扩散加权MRI数据估计该模型的参数。我们的扩散磁共振信号模型由超球面冯·米塞斯-费希尔分布的连续混合组成。作为一种连续混合模型，我们的模型不需要指定混合成分的数量。我们给出了这种连续混合的封闭形式表达式，从而实现了计算效率高的实现。为了验证我们的模型，我们在合成数据和真实数据（人类和大鼠大脑）上都给出了广泛的结果，并证明即使在存在噪声的情况下，我们的模型在纤维方向估计方面明显优于现有方法，同时保持了显著的计算优势。
