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Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation?
Author(s) -
Dikaios Nikolaos,
Punwani Shonit,
Hamy Valentin,
Purpura Pierpaolo,
Rice Scott,
Forster Martin,
Mendes Ruheena,
Taylor Stuart,
Atkinson David
Publication year - 2014
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.24877
Subject(s) - noise (video) , nonlinear regression , standard deviation , statistics , mathematics , diffusion , regression analysis , computer science , physics , artificial intelligence , image (mathematics) , thermodynamics
Purpose Multiexponential decay parameters are estimated from diffusion‐weighted‐imaging that generally have inherently low signal‐to‐noise ratio and non‐normal noise distributions, especially at high b ‐values. Conventional nonlinear regression algorithms assume normally distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify tumors. This study aims to accurately estimate noise of averaged diffusion‐weighted‐imaging, to correct the noise induced bias, and to assess the effect upon cancer classification. Methods A new adaptation of the median‐absolute‐deviation technique in the wavelet‐domain, using a closed form approximation of convolved probability‐distribution‐functions, is proposed to estimate noise. Nonlinear regression algorithms that account for the underlying noise (maximum probability) fit the biexponential/stretched exponential decay models to the diffusion‐weighted signal. A logistic‐regression model was built from the decay parameters to discriminate benign from metastatic neck lymph nodes in 40 patients. Results The adapted median‐absolute‐deviation method accurately predicted the noise of simulated ( R 2 = 0.96) and neck diffusion‐weighted‐imaging (averaged once or four times). Maximum probability recovers the true apparent‐diffusion‐coefficient of the simulated data better than nonlinear regression (up to 40%), whereas no apparent differences were found for the other decay parameters. Conclusions Perfusion‐related parameters were best at cancer classification. Noise‐corrected decay parameters did not significantly improve classification for the clinical data set though simulations show benefit for lower signal‐to‐noise ratio acquisitions. Magn Reson Med 71:2105–2117, 2014. © 2013 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance.