Compressed Sensing Diffusion Tensor Imaging ( DTI ) with Tensor and Phase Constraints

  • Li Y
  • Aggarwal M
  • Zhang J
 et al. 
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Abstract

To accelerate MR imaging, incomplete k space reconstruction techniques use multiple sources of prior knowledge and information redundancies. e.g. the smoothness of phase map in partial Fourier reconstruction and the transform sparsity in compressed sensing. They were designed for achieving a relatively high reconstruction accuracy from a reduced amount of data. In DTI, multiple (>=6) diffusion weighted images (DWIs) are acquired to estimate the six-parameter tensor model. This means that a large amount of data are collected to extract a relatively small amount of information. We propose to add a constraint to the compressed sensing method that measures the error between reconstructed DWIs and their tensor fitted values. Also, a phase constraint derived from the low resolution phase map estimated from fully sampled central part of k space was applied to the reconstruction. Testing result on high field mouse embryo DTI data shows reduction of reconstruction error by adding each of the proposed constraints and the joint improvement is even more significant

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Authors

  • Y Li

  • M Aggarwal

  • J Zhang

  • S Mori

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