Liu Feihong, Feng Jun, Chen Geng, Shen Dinggang, Yap Pew-Thian
School of Information Science and Technology, Northwest University, Xi'an, China; Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A.
School of Information Science and Technology, Northwest University, Xi'an, China; State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi'an, China.
Med Image Anal. 2021 Feb;68:101828. doi: 10.1016/j.media.2020.101828. Epub 2020 Oct 17.
Diffusion MRI magnitude data, typically Rician or noncentral χ distributed, is affected by the noise floor, which falsely elevates signal, reduces image contrast, and biases estimation of diffusion parameters. Noise floor can be avoided by extracting real-valued Gaussian-distributed data from complex diffusion-weighted images via phase correction, which is performed by rotating each complex diffusion-weighted image based on its phase so that the actual image content resides in the real part. The imaginary part can then be discarded, leaving only the real part to form a Gaussian-noise image that is not confounded by the noise floor. The effectiveness of phase correction depends on the estimation of the background phase associated with factors such as brain motion, cardiac pulsation, perfusion, and respiration. Most existing smoothing techniques, applied to the real and imaginary images for phase estimation, assume spatially-stationary noise. This assumption does not necessarily hold in real data. In this paper, we introduce an adaptive filtering approach, called multi-kernel filter (MKF), for image smoothing catering to spatially-varying noise. Inspired by the mechanisms of human vision, MKF employs a bilateral filter with spatially-varying kernels. Extensive experiments demonstrate that MKF significantly improves spatial adaptivity and outperforms various state-of-the-art filters in signal Gaussianization.
扩散磁共振成像幅度数据通常呈莱斯分布或非中心卡方分布，会受到本底噪声的影响，本底噪声会错误地提高信号、降低图像对比度并使扩散参数估计产生偏差。通过相位校正从复杂的扩散加权图像中提取实值高斯分布数据，可以避免本底噪声，相位校正通过基于其相位旋转每个复杂的扩散加权图像来执行，以使实际图像内容位于实部。然后可以丢弃虚部，仅保留实部以形成不受本底噪声干扰的高斯噪声图像。相位校正的有效性取决于与诸如脑运动、心脏搏动、灌注和呼吸等因素相关的背景相位估计。大多数现有的用于相位估计的平滑技术应用于实部和虚部图像时，都假设噪声在空间上是平稳的。这种假设在实际数据中不一定成立。在本文中，我们引入了一种自适应滤波方法，称为多核滤波器（MKF），用于适应空间变化噪声的图像平滑。受人类视觉机制的启发，MKF采用具有空间变化核的双边滤波器。大量实验表明，MKF显著提高了空间适应性，并且在信号高斯化方面优于各种先进滤波器。
