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Publication Detail
Noise estimation from averaged diffusion weighted images: can unbiased quantitative decay parameters assist cancer evaluation?
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    Dikaios N, Punwani S, Hamy V, Purpura P, Rice S, Forster M, Mendes R, Taylor S, Atkinson D
  • Publisher:
  • Publication date:
  • Pagination:
    2105, 2117
  • Journal:
    Magnetic Resonance in Medicine
  • Volume:
  • Issue:
  • Status:
  • Print ISSN:
  • Keywords:
    Diffusion weighted magnetic resonance imaging, noise estimation, IVIM
Purpose: Multiexponential decay parameters are estimated from diffusion-weighted-imaging (DWI) that generally have inherently low signal-to-noise-ratio and non-normal noise-distributions, especially at high b-values. Conventional non-linear-regression (NR) algorithms assume normally-distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify tumours. This work aims to accurately estimate noise of averaged DWI, correct the noise induced bias and assess the effect upon cancer classification. Methods: A new adaptation of the median-absolute-deviation (MAD) technique in the wavelet-domain, using a closed-form-approximation of convolved probability-distribution-functions, is proposed to estimate noise. Non-linear-regression algorithms that account for the underlying noise (MP) fit the bi-exponential/stretched-exponential decay models to the DW signal. A logistic-regression model was built from the decay parameters to discriminate benign from metastatic neck lymph-nodes on 40 patients. Results: The adapted MAD method accurately predicted the noise of simulated (R2=0.96) and neck DWI (averaged once or four-times). MP recovers the true apparent-diffusion-coefficient of the simulated data better than NR (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 dataset, though simulations show benefit for lower signal-to-noise-ratio acquisitions.
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