Because of the intrinsic low signal-to-noise ratio in diffusion-weighted imaging (DWI), magnitude processing often causes an overestimation of the signal's amplitude. This results in low-estimation accuracy of diffusion models and reduced contrast because of a superposition of the image signal and the noise floor. We adopt a new phase correction (PC) technique that yields real valued diffusion data while maintaining a Gaussian noise distribution.We conduct simulations of the noise propagation in the echo-planar imaging reconstruction chain to determine the spatial noise correlation in the image. Using the correlation pattern, optimized filter kernels are derived to estimate the true phase of the signal in each voxel. Furthermore, we adopt an outlier detection technique to replace the real value by the magnitude in case of substantial signal loss resulting from incorrect PC.The benefits of our method are demonstrated on Monte Carlo simulations, DWI data acquired from healthy volunteer experiments, estimated parameters of the diffusion kurtosis imaging model, and the model-free diffusion spectrum imaging technique. The improved PC approach significantly reduces the noise bias and only slightly increases the sensitivity to local phase variations.PC can enhance the usefulness of higher b-values, allowing deeper insights into tissue microstructure. Magn Reson Med 77:559-570, 2017. © 2016 International Society for Magnetic Resonance in Medicine.     «

Because of the intrinsic low signal-to-noise ratio in diffusion-weighted imaging (DWI), magnitude processing often causes an overestimation of the signal's amplitude. This results in low-estimation accuracy of diffusion models and reduced contrast because of a superposition of the image signal and the noise floor. We adopt a new phase correction (PC) technique that yields real valued diffusion data while maintaining a Gaussian noise distribution.We conduct simulations of the noise propagation in...     »