Dikaios, N;
Punwani, S;
Hamy, V;
Purpura, P;
Rice, S;
Forster, M;
Mendes, R;
... Atkinson, D; + view all
(2013)
Noise Estimation from Averaged Diffusion Weighted Images: Can Unbiased Quantitative Decay Parameters Assist Cancer Evaluation?
Magnetic Resonance in Medicine
, 71
(6)
pp. 2105-2117.
10.1002/mrm.24877.
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Abstract
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 (R2 = 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.
| Type: | Article |
|---|---|
| Title: | Noise Estimation from Averaged Diffusion Weighted Images: Can Unbiased Quantitative Decay Parameters Assist Cancer Evaluation? |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/mrm.24877 |
| Publisher version: | https://doi.org/10.1002/mrm.24877 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Radiology, Nuclear Medicine & Medical Imaging, diffusion weighted magnetic resonance imaging, noise estimation, IVIM, INTRAVOXEL INCOHERENT MOTION, RICIAN DISTRIBUTION, MRI, COEFFICIENT, TUMORS, HEAD, NECK |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > UCL Medical School |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1397028 |
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