title: Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation?
abstract: OBJECTIVE
  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.
authors.affiliation: 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.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Dikaios
authors.forename: Shonit
authors.initials: S
authors.lastname: Punwani
authors.forename: Valentin
authors.initials: V
authors.lastname: Hamy
authors.forename: Pierpaolo
authors.initials: P
authors.lastname: Purpura
authors.forename: Scott
authors.initials: S
authors.lastname: Rice
authors.forename: Martin
authors.initials: M
authors.lastname: Forster
authors.forename: Ruheena
authors.initials: R
authors.lastname: Mendes
authors.forename: Stuart
authors.initials: S
authors.lastname: Taylor
authors.forename: David
authors.initials: D
authors.lastname: Atkinson
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006258:Head and Neck Neoplasms / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008207:Lymphatic Metastasis; D008875:Middle Aged; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio
keywords: IVIM; diffusion weighted magnetic resonance imaging; noise estimation
references.citation: Magn Reson Med. 2002 Jun;47(6):1202-10
references.pmid: 12111967
references.citation: Neoplasia. 2009 Feb;11(2):102-25
references.pmid: 19186405
references.citation: Magn Reson Med. 1999 Nov;42(5):952-62
references.pmid: 10542355
references.citation: AJR Am J Roentgenol. 2011 Jun;196(6):1351-61
references.pmid: 21606299
references.citation: AJR Am J Roentgenol. 2013 Jan;200(1):35-43
references.pmid: 23255739
references.citation: Int J Radiat Oncol Biol Phys. 2010 Aug 1;77(5):1403-10
references.pmid: 19906496
references.citation: Magn Reson Med. 1995 Dec;34(6):910-4
references.pmid: 8598820
references.citation: J Magn Reson. 2006 Apr;179(2):317-22
references.pmid: 16488635
references.citation: Magn Reson Med. 1997 Nov;38(5):852-7
references.pmid: 9358462
references.citation: Radiology. 2007 Dec;245(3):848-54
references.pmid: 17951348
references.citation: Radiology. 1988 Aug;168(2):497-505
references.pmid: 3393671
references.citation: Med Phys. 1985 Mar-Apr;12(2):232-3
references.pmid: 4000083
references.citation: Magn Reson Imaging. 2011 Jul;29(6):766-76
references.pmid: 21549538
references.citation: Magn Reson Med. 2009 Aug;62(2):420-9
references.pmid: 19353661
references.citation: Med Image Anal. 2010 Aug;14(4):483-93
references.pmid: 20417148
references.citation: Eur J Nucl Med Mol Imaging. 2004 Aug;31(8):1064-72
references.pmid: 15014903
references.citation: Radiology. 1982 Apr;143(1):29-36
references.pmid: 7063747
references.citation: J Magn Reson. 2007 Aug;187(2):293-305
references.pmid: 17572124
references.citation: IEEE Trans Med Imaging. 1998 Jun;17(3):357-61
references.pmid: 9735899
references.citation: AJNR Am J Neuroradiol. 2003 Sep;24(8):1627-34
references.pmid: 13679283
references.citation: Magn Reson Med. 2003 Oct;50(4):727-34
references.pmid: 14523958
references.citation: Invest Radiol. 2011 Dec;46(12):751-8
references.pmid: 21691213
references.citation: Magn Reson Imaging. 2008 Jul;26(6):754-62
references.pmid: 18440746