روشی نوین در کاهش نوفه رایسین از مقدار بزرگی سیگنال دیفیوژن در تصویربرداری تشدید مغناطیسی (MRI)

The true MR signal intensity extracted from noisy MR magnitude images is biased with the Rician noise caused by noise rectification in the magnitude calculation for low intensity pixels. This noise is more problematic when a quantitative analysis is performed based on the magnitude images with low SNR(<3.0). In such cases, the received signal for both the real and imaginary components will fluctuate around a low level (e.g. zero) often producing negative values. The magnitude calculation on such signals will rectify all negative values to produce only positive magnitudes, thereby artificially raising the average level of these pixels. The signal thus will be biased by the rectified noise. Diffusion MRI using high b-values (using strong magnetic gradients) is one the most important cases of biased Rician noise.  A technique for removing this bias from individual pixels of magnitude MR images is presented in this study. This method provides a bias correction for individual pixels using a linear equation with the correction term separated from the term to be corrected (i.e. the pixel intensity). The correction is exact when the mean and variance of the pixel intensity probability density functions are known. When accurate mean values are not available, a nearest neighbor average is used to approximate the mean in the calculation of the linear correction term. With a nine pixel nearest neighbor average (i.e. one layer of nearest neighbors) the bias correction for individual pixel intensities is accurate to within 10% error for signal to noise ratios SNR=1.0. Several different noise correction schemes from the literature are presented and compared. The new Rician bias correction presented in this work represents a significant improvement over previously published techniques. The proposed approach substantially removes the Rician noise bias from diffusion MR signal decay over an extended range of b-values from zero to very high b-values.