title: A maximum-likelihood method to estimate a single ADC value of lesions using diffusion MRI.
abstract: Design a statistically rigorous procedure to estimate a single apparent diffusion coefficient (ADC) of lesion from the mean lesion signal intensity in diffusion MRI.
  
  
  
  A rigorous maximum-likelihood technique that incorporated the statistics of the mean lesion intensity and accounted for lesion heterogeneity was derived to estimate the ADC value. Performance evaluation included comparison with the conventionally used linear-regression and a statistically rigorous state-of-the-art ADC-map technique using realistic and clinically relevant simulation studies conducted with assistance of patient data for homogeneous and heterogeneous lesion models.
  
  
  
  The proposed technique outperformed the linear-regression and ADC-map approaches over a large spectrum of signal-to-noise ratio, ADC, lesion size, image-misalignment parameters, including at no image misalignment, and different amounts of lesion heterogeneity. The method was also superior at different sets of b values and in studies from specific patient-image-derived data. The technique took less than a second to execute.
  
  
  
  A rigorous, computationally fast, easy-to-implement, and convenient-to-use maximum-likelihood technique was proposed to estimate a single ADC value of the lesion. Results provide strong evidence in support of the method. Magn Reson Med 76:1919-1931, 2016. © 2016 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Division of Medical Imaging Physics, Department of Radiology and Radiological Sciences, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA.
authors.forename: Abhinav K
authors.initials: AK
authors.lastname: Jha
authors.affiliation: Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona, USA.
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Rodríguez
authors.affiliation: Department of Medicine, Stony Brook Cancer Center, SUNY Stony Brook, Stony Brook, New York, USA.
authors.forename: Alison T
authors.initials: AT
authors.lastname: Stopeck
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: ADC estimation; maximum-likelihood method; motion misalignment; single ADC value; statistics of Rician-distributed random variables