Noise and filtration in magnetic resonance imaging.

By Elliot R. McVeigh, R. Henkelman, M. Bronskill. 1985

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An overview of deep learning in medical imaging focusing on MRI (2018, background): “Estimation of noise and image denoising in MRI has been an important field of research for many years [171,172], employing a plethora of methods.”
A survey on the magnetic resonance image denoising methods (2014, methodology): “The authors compared this algorithm with Gaussian smoothing filter [11], wavelet AMPT [53] and NLM [27] and the result shows that the performance of Dynamic NLM is better than all of the other algorithms.”
Measurement of signal‐to‐noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters (2007, background): “The original noise in the image raw data prior to its reconstruction with Fourier transformation typically has a normal (or Gaussian) distribution (30).”
MODEL-BASED IMAGE RECONSTRUCTION FOR MRI (2010, methodology): “The measurement errors εli are very well modeled by additive, complex, zero-mean, temporally white gaussian noise [23] [24] [25].”