Kristoffersen Anders
MR Center, St. Olavs Hospital HF, Trondheim, Norway.
J Magn Reson Imaging. 2009 Jan;29(1):237-41. doi: 10.1002/jmri.21589.
To compare an unbiased method for estimation of the diffusion coefficient to the quick, but biased, log-linear (LL) method in the presence of noisy magnitude data.
The magnitude operation changes the signal distribution in magnetic resonance (MR) images from Gaussian to Rician. If not properly taken into account, this will introduce a bias in the estimated diffusion coefficient. We compare two methods by means of Monte Carlo simulations. The first one applies least-squares fitting of the measured signal to the median (MD) value of the probability density function. The second method is uncorrected LL estimation. We also perform a high-resolution diffusion tensor experiment.
The uncorrected LL estimator is heavily biased at low signal-to-noise ratios. The bias has a significant effect on image quality. The MD estimator is accurate and produces images with excellent contrast.
In the presence of noisy magnitude data, unbiased estimation is essential in diffusion measurements and diffusion tensor imaging.
在存在噪声幅度数据的情况下，将一种用于估计扩散系数的无偏方法与快速但有偏的对数线性（LL）方法进行比较。
幅度运算会使磁共振（MR）图像中的信号分布从高斯分布变为莱斯分布。如果没有适当考虑，这将在估计的扩散系数中引入偏差。我们通过蒙特卡罗模拟比较两种方法。第一种方法是将测量信号进行最小二乘拟合到概率密度函数的中值（MD）。第二种方法是未校正的LL估计。我们还进行了高分辨率扩散张量实验。
未校正的LL估计器在低信噪比时存在严重偏差。该偏差对图像质量有显著影响。MD估计器准确且能产生具有出色对比度的图像。
在存在噪声幅度数据的情况下，无偏估计在扩散测量和扩散张量成像中至关重要。
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