Abstract
Parallel MRI leads to magnitude data corrupted by noise described in most cases as following a Rician or a non central χ distribution. And yet, very few correction methods perform a non central χ noise removal. However, this correction step, adapted to the correct noise model, is of very much importance, especially when working with Diffusion Weighted MR data yielding a low SNR. We propose an extended Linear Minimum Mean Square Error estimator (LMMSE), which is adapted to deal with non central χ distributions. We demonstrate on simulated and real data that the extended LMMSE outperforms the original LMMSE on images corrupted by a non central χ noise.
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Keywords
- Noise Standard Deviation
- Rician Noise
- Parallel Magnetic Resonance Image
- Linear Minimum Mean Square Error Estimator
- Element Phase Array Antenna
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Brion, V. et al. (2011). Parallel MRI Noise Correction: An Extension of the LMMSE to Non Central χ Distributions. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_28
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DOI: https://doi.org/10.1007/978-3-642-23629-7_28
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