Landman Bennett A, Bazin Pierre-Louis, Prince Jerry L
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Magn Reson Imaging. 2009 Jul;27(6):741-51. doi: 10.1016/j.mri.2009.01.001. Epub 2009 Feb 28.
Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically realized as a spatially invariant estimate of the noise distribution. This is not an ideal practice in diffusion tensor imaging because the noise distribution is usually spatially varying due to the use of fast imaging and noise suppression techniques. A new estimation approach for spatially varying noise fields (NFs) is proposed in this article. The approach is based on a noise invariance property in scenarios in which more than one image, each with potentially different signal levels, is acquired on each slice, as in diffusion-weighted MRI. This technique leads to improved NF estimates in simulations, phantom experiments and in vivo studies when compared to traditional NF estimators that use regional variability or background intensity histograms. The proposed method reduces the NF estimation error by a factor of 100 in simulations, shows a strong linear correlation (R(2)=0.99) between theoretical and estimated noise changes in phantoms and demonstrates consistent (<5% variability) NF estimates in vivo. The advantages of spatially varying NF estimation are demonstrated for power analysis, outlier detection and tensor estimation.
磁共振图像内容的最佳解读通常需要对潜在的图像噪声进行估计，这通常表现为对噪声分布的空间不变估计。在扩散张量成像中，这并非理想做法，因为由于使用快速成像和噪声抑制技术，噪声分布通常在空间上是变化的。本文提出了一种针对空间变化噪声场（NFs）的新估计方法。该方法基于一种噪声不变性属性，即在每个切片上采集多个图像（每个图像的信号水平可能不同）的情况下，如在扩散加权磁共振成像中。与使用区域变异性或背景强度直方图的传统NF估计器相比，该技术在模拟、体模实验和体内研究中能带来更好的NF估计。在模拟中，所提出的方法将NF估计误差降低了100倍，在体模中理论噪声变化与估计噪声变化之间显示出很强的线性相关性（R(2)=0.99），并在体内证明了一致的（<5%变异性）NF估计。空间变化NF估计的优势在功效分析、异常值检测和张量估计中得到了证明。
