Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation?

Authors:
Dr Nikolaos Dikaios, PhD
Dr Nikolaos Dikaios, PhD
University of Surrey
Assistant Professor
Guildford, Surrey | United Kingdom
Shonit Punwani
Shonit Punwani
University College London
United Kingdom
Valentin Hamy
Valentin Hamy
University College London
United Kingdom
Pierpaolo Purpura
Pierpaolo Purpura
University College London
United Kingdom
Scott Rice
Scott Rice
Nanyang Technological University
Martin Forster
Martin Forster
University College London
United Kingdom
Ruheena Mendes
Ruheena Mendes
University College London
United Kingdom
Stuart Taylor
Stuart Taylor
University College London

Magn Reson Med 2014 Jun 1;71(6):2105-17. Epub 2013 Aug 1.

Centre for Medical Image Computing, Division Medical Physics and Bioengineering, University College London, London, UK; Centre for Medical Imaging, Division of Medicine, University College London, London, UK.

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.

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Source
http://dx.doi.org/10.1002/mrm.24877DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282362PMC
June 2014
31 Reads
1 PubMed Central Citation(source)
3.57 Impact Factor

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