Evaluation of optimized b-value sampling schemas for diffusion kurtosis imaging with an application to stroke patient data

Xu Yan, Minxiong Zhou, Lingfang Ying, Dazhi Yin, Mingxia Fan, Guang Yang, Yongdi Zhou, Fan Song, Dongrong Xu

Research output: Contribution to journalArticlepeer-review

Abstract

Diffusion kurtosis imaging (DKI) is a new method of magnetic resonance imaging (MRI) that provides non-Gaussian information that is not available in conventional diffusion tensor imaging (DTI). DKI requires data acquisition at multiple b-values for parameter estimation; this process is usually time-consuming. Therefore, fewer b-values are preferable to expedite acquisition. In this study, we carefully evaluated various acquisition schemas using different numbers and combinations of b-values. Acquisition schemas that sampled b-values that were distributed to two ends were optimized. Compared to conventional schemas using equally spaced b-values (ESB), optimized schemas require fewer b-values to minimize fitting errors in parameter estimation and may thus significantly reduce scanning time. Following a ranked list of optimized schemas resulted from the evaluation, we recommend the 3b schema based on its estimation accuracy and time efficiency, which needs data from only 3 b-values at 0, around 800 and around 2600s/mm2, respectively. Analyses using voxel-based analysis (VBA) and region-of-interest (ROI) analysis with human DKI datasets support the use of the optimized 3b (0, 1000, 2500s/mm2) DKI schema in practical clinical applications.

Original languageEnglish (US)
Pages (from-to)272-280
Number of pages9
JournalComputerized Medical Imaging and Graphics
Volume37
Issue number4
DOIs
StatePublished - Jun 2013
Externally publishedYes

Keywords

  • B-value sampling
  • Diffusion kurtosis imaging
  • Diffusion tensor imaging
  • Non-Gaussian diffusion
  • Optimized b-value

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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