Sepehrband Farshid, Choupan Jeiran, Caruyer Emmanuel, Kurniawan Nyoman D, Gal Yaniv, Tieng Quang M, McMahon Katie L, Vegh Viktor, Reutens David C, Yang Zhengyi
Centre for Advanced Imaging, The University of Queensland , Brisbane, QLD , Australia ; Queensland Brain Institute, The University of Queensland , Brisbane, QLD , Australia.
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania , Philadelphia, PA , USA.
Front Neurol. 2015 Jan 12;5:290. doi: 10.3389/fneur.2014.00290. eCollection 2014.
We describe and evaluate a pre-processing method based on a periodic spiral sampling of diffusion-gradient directions for high angular resolution diffusion magnetic resonance imaging. Our pre-processing method incorporates prior knowledge about the acquired diffusion-weighted signal, facilitating noise reduction. Periodic spiral sampling of gradient direction encodings results in an acquired signal in each voxel that is pseudo-periodic with characteristics that allow separation of low-frequency signal from high frequency noise. Consequently, it enhances local reconstruction of the orientation distribution function used to define fiber tracks in the brain. Denoising with periodic spiral sampling was tested using synthetic data and in vivo human brain images. The level of improvement in signal-to-noise ratio and in the accuracy of local reconstruction of fiber tracks was significantly improved using our method.
我们描述并评估了一种基于扩散梯度方向的周期性螺旋采样的预处理方法，用于高角分辨率扩散磁共振成像。我们的预处理方法纳入了关于采集到的扩散加权信号的先验知识，有助于降噪。梯度方向编码的周期性螺旋采样导致每个体素中采集到的信号具有伪周期性，其特征允许将低频信号与高频噪声分离。因此，它增强了用于定义大脑中纤维束轨迹的方向分布函数的局部重建。使用合成数据和体内人脑图像测试了周期性螺旋采样的去噪效果。使用我们的方法，信噪比和纤维束轨迹局部重建的准确性的改善水平得到了显著提高。
