Removal of intensity bias in magnitude spin-echo MRI images by nonlinear diffusion filtering

Alexei A. Samsonov; Chris R. Johnson

doi:10.1117/12.535899

12 May 2004 Removal of intensity bias in magnitude spin-echo MRI images by nonlinear diffusion filtering

Alexei A. Samsonov, Chris R. Johnson

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535899
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

MRI data analysis is routinely done on the magnitude part of complex images. While both real and imaginary image channels contain Gaussian noise, magnitude MRI data are characterized by Rice distribution. However, conventional filtering methods often assume image noise to be zero mean and Gaussian distributed. Estimation of an underlying image using magnitude data produces biased result. The bias may lead to significant image errors, especially in areas of low signal-to-noise ratio (SNR). The incorporation of the Rice PDF into a noise filtering procedure can significantly complicate the method both algorithmically and computationally. In this paper, we demonstrate that inherent image phase smoothness of spin-echo MRI images could be utilized for separate filtering of real and imaginary complex image channels to achieve unbiased image denoising. The concept is demonstrated with a novel nonlinear diffusion filtering scheme developed for complex image filtering. In our proposed method, the separate diffusion processes are coupled through combined diffusion coefficients determined from the image magnitude. The new method has been validated with simulated and real MRI data. The new method has provided efficient denoising and bias removal in conventional and black-blood angiography MRI images obtained using fast spin echo acquisition protocols.

Citation Download Citation

Alexei A. Samsonov and Chris R. Johnson "Removal of intensity bias in magnitude spin-echo MRI images by nonlinear diffusion filtering", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535899

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