Estimation and Application of Spatially Variable Noise Fields in Diffusion Tensor Imaging - Landman, Bennett A. (2009)
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 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, are acquired on each slice, as in diffusion weighted MRI. This technique leads to improved noise field estimates in simulations, phantom experiments and in vivo studies when compared to traditional noise field estimators that use regional variability or background intensity histograms. The proposed method reduces the noise field 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) noise field estimates in vivo. The advantages of spatially varying noise field estimation are demonstrated for power analysis, outlier detection, and tensor estimation.