Liu Xiaozheng, Yuan Zhenming, Guo Zhongwei, Xu Dongrong
Center for Cognition and Brain Disorders and Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou 310015, China.
Department of Information Science and Engineering, Hangzhou Normal University, Hangzhou 310012, China.
Med Phys. 2015 May;42(5):2524-39. doi: 10.1118/1.4917082.
Diffusion tensor imaging is widely used for studying neural fiber trajectories in white matter and for quantifying changes in tissue using diffusion properties at each voxel in the brain. To better model the nature of crossing fibers within complex architectures, rather than using a simplified tensor model that assumes only a single fiber direction at each image voxel, a model mixing multiple diffusion tensors is used to profile diffusion signals from high angular resolution diffusion imaging (HARDI) data. Based on the HARDI signal and a multiple tensors model, spherical deconvolution methods have been developed to overcome the limitations of the diffusion tensor model when resolving crossing fibers. The Richardson-Lucy algorithm is a popular spherical deconvolution method used in previous work. However, it is based on a Gaussian distribution, while HARDI data are always very noisy, and the distribution of HARDI data follows a Rician distribution. This current work aims to present a novel solution to address these issues.
By simultaneously considering both the Rician bias and neighbor correlation in HARDI data, the authors propose a localized Richardson-Lucy (LRL) algorithm to estimate fiber orientations for HARDI data. The proposed method can simultaneously reduce noise and correct the Rician bias.
Mean angular error (MAE) between the estimated Fiber orientation distribution (FOD) field and the reference FOD field was computed to examine whether the proposed LRL algorithm offered any advantage over the conventional RL algorithm at various levels of noise. Normalized mean squared error (NMSE) was also computed to measure the similarity between the true FOD field and the estimated FOD filed. For MAE comparisons, the proposed LRL approach obtained the best results in most of the cases at different levels of SNR and b-values. For NMSE comparisons, the proposed LRL approach obtained the best results in most of the cases at b-value = 3000 s/mm(2), which is the recommended schema for HARDI data acquisition. In addition, the FOD fields estimated by the proposed LRL approach in regions of fiber crossing regions using real data sets also showed similar fiber structures which agreed with common acknowledge in these regions.
The novel spherical deconvolution method for improved accuracy in investigating crossing fibers can simultaneously reduce noise and correct Rician bias. With the noise smoothed and bias corrected, this algorithm is especially suitable for estimation of fiber orientations in HARDI data. Experimental results using both synthetic and real imaging data demonstrated the success and effectiveness of the proposed LRL algorithm.
扩散张量成像被广泛用于研究白质中的神经纤维轨迹以及利用大脑中每个体素的扩散特性来量化组织变化。为了更好地对复杂结构内交叉纤维的性质进行建模，不是使用在每个图像体素仅假设单一纤维方向的简化张量模型，而是使用混合多个扩散张量的模型来分析来自高角分辨率扩散成像（HARDI）数据的扩散信号。基于HARDI信号和多张量模型，已经开发出球形反卷积方法来克服扩散张量模型在解析交叉纤维时的局限性。理查森 - 露西算法是先前工作中常用的一种球形反卷积方法。然而，它基于高斯分布，而HARDI数据总是噪声很大，并且HARDI数据的分布遵循莱斯分布。当前这项工作旨在提出一种新颖的解决方案来解决这些问题。
通过同时考虑HARDI数据中的莱斯偏差和邻域相关性，作者提出一种局部理查森 - 露西（LRL）算法来估计HARDI数据的纤维方向。所提出的方法可以同时降低噪声并校正莱斯偏差。
计算估计的纤维方向分布（FOD）场与参考FOD场之间的平均角误差（MAE），以检验所提出的LRL算法在不同噪声水平下是否比传统的RL算法具有任何优势。还计算归一化均方误差（NMSE）以测量真实FOD场与估计的FOD场之间的相似度。对于MAE比较，在不同信噪比（SNR）和b值水平下，所提出的LRL方法在大多数情况下都获得了最佳结果。对于NMSE比较，在b值 = 3000 s/mm²时，所提出的LRL方法在大多数情况下获得了最佳结果，这是HARDI数据采集的推荐模式。此外，使用真实数据集在所提出的LRL方法估计的纤维交叉区域的FOD场也显示出与这些区域的普遍认知相符的类似纤维结构。
用于提高交叉纤维研究准确性的新型球形反卷积方法可以同时降低噪声并校正莱斯偏差。随着噪声被平滑和偏差被校正，该算法特别适用于估计HARDI数据中的纤维方向。使用合成和真实成像数据的实验结果证明了所提出的LRL算法的成功和有效性。
