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Medical-image Analysis and Statistical Interpretation Lab

Estimation and application of spatially variable noise fields in diffusion tensor imaging.

Posted by on Saturday, February 28, 2009 in Diffusion Tensor Imaging, Noise Estimation.

B. A. Landman, P-L Bazin, and J. L. Prince, “Estimation and Application of Spatially Variable Noise Fields in Diffusion Tensor Imaging”, Magnetic Resonance Imaging, Volume 27, Issue 6, Pages 741-751. (2009) PMC2733233

Full text: https://www.ncbi.nlm.nih.gov/pubmed/19250784

Abstract

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.

Simulation results. In the idealized simulation (left), the background estimator (BE) method performed better than other methods both in terms of mean squared error (first row) and variability (second row). In the simulation incorporating spatial variability and artifacts (right), the proposed spatially regularized robust (SRR) method greatly exceeded the performance of the standard approaches.
Simulation results. In the idealized simulation (left), the background estimator (BE) method performed better than other methods both in terms of mean squared error (first row) and variability (second row). In the simulation incorporating spatial variability and artifacts (right), the proposed spatially regularized robust (SRR) method greatly exceeded the performance of the standard approaches.

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