Efficient parallel reconstruction for high resolution multishot spiral diffusion data with low rank constraint

Magn Reson Med. 2017 Mar;77(3):1359-1366. doi: 10.1002/mrm.26199. Epub 2016 Mar 10.


Purpose: To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging.

Theory and methods: The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ℓ1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets.

Results: It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions.

Conclusion: Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. Magn Reson Med 77:1359-1366, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Keywords: low rank; multishot DWI; parallel imaging; spiral data.

Publication types

  • Evaluation Study

MeSH terms

  • Algorithms*
  • Brain / anatomy & histology*
  • Diffusion Magnetic Resonance Imaging / methods*
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted