title: Direct diffusion tensor estimation using a model-based method with spatial and parametric constraints.
abstract: OBJECTIVE
  To develop a new model-based method with spatial and parametric constraints (MB-SPC) aimed at accelerating diffusion tensor imaging (DTI) by directly estimating the diffusion tensor from highly undersampled k-space data.
  
  
  METHODS
  The MB-SPC method effectively incorporates the prior information on the joint sparsity of different diffusion-weighted images using an L1-L2 norm and the smoothness of the diffusion tensor using a total variation seminorm. The undersampled k-space datasets were obtained from fully sampled DTI datasets of a simulated phantom and an ex-vivo experimental rat heart with acceleration factors ranging from 2 to 4. The diffusion tensor was directly reconstructed by solving a minimization problem with a nonlinear conjugate gradient descent algorithm. The reconstruction performance was quantitatively assessed using the normalized root mean square error (nRMSE) of the DTI indices.
  
  
  RESULTS
  The MB-SPC method achieves acceptable DTI measures at an acceleration factor up to 4. Experimental results demonstrate that the proposed method can estimate the diffusion tensor more accurately than most existing methods operating at higher net acceleration factors.
  
  
  CONCLUSIONS
  The proposed method can significantly reduce artifact, particularly at higher acceleration factors or lower SNRs. This method can easily be adapted to MR relaxometry parameter mapping and is thus useful in the characterization of biological tissue such as nerves, muscle, and heart tissue.
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
authors.forename: Yanjie
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Peng
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
authors.forename: Yin
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong.
authors.forename: Ed X
authors.initials: EX
authors.lastname: Wu
authors.affiliation: Department of Electrical Engineering, Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA.
authors.forename: Leslie
authors.initials: L
authors.lastname: Ying
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Liu
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
authors.forename: Hairong
authors.initials: H
authors.lastname: Zheng
authors.affiliation: Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Liang
mesh_terms: D000818:Animals; D056324:Diffusion Tensor Imaging; D015233:Models, Statistical; D051381:Rats; D059629:Signal-To-Noise Ratio
keywords: diffusion tensor imaging; distributed compressed sensing; joint sparsity constraint; model-based method; smoothing constraint