Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain.
PLoS One. 2013 Sep 3;8(9):e73021. doi: 10.1371/journal.pone.0073021. eCollection 2013.
Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.
扩散加权图像（DWI）由于测量过程中的噪声而通常显示出低信噪比（SNR），这使得定量扩散参数的估计变得复杂和有偏差。在本文中，提出了一种新的去噪方法，该方法考虑了扩散成像中使用的多向 DWI 数据集的多分量性质。这种新的滤波器通过使用过完备的方法局部收缩不太重要的主分量来减少多分量 DWI 中的随机噪声。该方法与使用合成和真实临床 MR 图像的最新方法进行了比较，在去噪质量和扩散参数估计方面表现出了更好的性能。
