A fast schema for parameter estimation in diffusion kurtosis imaging

Xu Yan, Minxiong Zhou, Lingfang Ying, Wei Liu, Guang Yang, Dongmei Wu, Yongdi Zhou, Bradley S. Peterson, Dongrong Xu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Diffusion kurtosis imaging (DKI) is a new model in magnetic resonance imaging (MRI) characterizing restricted diffusion of water molecules in living tissues. We propose a method for fast estimation of the DKI parameters. These parameters - apparent diffusion coefficient (ADC) and apparent kurtosis coefficient (AKC) - are evaluated using an alternative iteration schema (AIS). This schema first roughly estimates a pair of ADC and AKC values from a subset of the DKI data acquired at 3 b-values. It then iteratively and alternately updates the ADC and AKC until they are converged. This approach employs the technique of linear least square fitting to minimize estimation error in each iteration. In addition to the common physical and biological constrains that set the upper and lower boundaries of the ADC and AKC values, we use a smoothing procedure to ensure that estimation is robust. Quantitative comparisons between our AIS methods and the conventional methods of unconstrained nonlinear least square (UNLS) using both synthetic and real data showed that our unconstrained AIS method can significantly accelerate the estimation procedure without compromising its accuracy, with the computational time for a DKI dataset successfully reduced to only 1 or 2. min. Moreover, the incorporation of the smoothing procedure using one of our AIS methods can significantly enhance the contrast of AKC maps and greatly improve the visibility of details in fine structures.

Original languageEnglish (US)
Pages (from-to)469-480
Number of pages12
JournalComputerized Medical Imaging and Graphics
Issue number6
StatePublished - 2014
Externally publishedYes


  • Diffusion kurtosis imaging
  • Diffusion tensor imaging
  • Magnetic resonance imaging
  • Parameter estimation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition


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