title: Accelerated MR diffusion tensor imaging using distributed compressed sensing.
abstract: OBJECTIVE
  Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion-weighted images.
  
  
  METHODS
  Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k-space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion-weighted images were reconstructed by solving an l2-l1 norm minimization problem. Reconstruction performance with varied signal-to-noise ratio and acceleration factors were evaluated by root-mean-square error and maps of reconstructed DTI indices.
  
  
  RESULTS
  Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal-to-noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4.
  
  
  CONCLUSIONS
  Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically.
authors.affiliation: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
authors.forename: Yin
authors.initials: Y
authors.lastname: Wu
authors.forename: Yan-Jie
authors.initials: YJ
authors.lastname: Zhu
authors.forename: Qiu-Yang
authors.initials: QY
authors.lastname: Tang
authors.forename: Chao
authors.initials: C
authors.lastname: Zou
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Rui-Bin
authors.initials: RB
authors.lastname: Dai
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.forename: Ed X
authors.initials: EX
authors.lastname: Wu
authors.forename: Leslie
authors.initials: L
authors.lastname: Ying
authors.forename: Dong
authors.initials: D
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000295:instrumentation / Q000379:methods*; D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity