A novel approach for noise reduction in Magnetic Resonance Image field is proposed. The methodology adopts a Maximum A Posteriori estimator and exploits Markov Random Field theory for adapting the filter to the local nature of the image. Differently from other widely adopted filters, the proposed algorithm works in the complex domain, i.e., real and imaginary components of the acquired images are jointly processed and regularized. First results on a clinical dataset are reported, showing the interesting performances of the methodology.

Bayesian mri noise filtering in complex domain

SORRISO, ANTONIETTA;Baselice, Fabio;Ferraioli, Giampaolo;Pascazio, Vito
2017-01-01

Abstract

A novel approach for noise reduction in Magnetic Resonance Image field is proposed. The methodology adopts a Maximum A Posteriori estimator and exploits Markov Random Field theory for adapting the filter to the local nature of the image. Differently from other widely adopted filters, the proposed algorithm works in the complex domain, i.e., real and imaginary components of the acquired images are jointly processed and regularized. First results on a clinical dataset are reported, showing the interesting performances of the methodology.
9781509016426
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/65300
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