Magnitude-based Asymmetric Fourier Imaging (MagAFI)

Magn Reson Med Sci. 2016;15(1):94-104. doi: 10.2463/mrms.2014-0147. Epub 2015 Sep 4.

Abstract

Purpose: We propose and assess 2 novel asymmetric Fourier imaging (AFI) techniques, magnitude-based AFI (MagAFI) and MagAFI combined with projection on to convex sets (POCS) (MagAFI+POCS). MagAFI does not require phase information because it uses only the magnitude image with zero-filling. MagAFI+POCS requires phase information but further reduces image errors.

Materials and methods: We initially compared phase maps obtained using asymmetrically sampled data for the whole of the k-space and symmetrically sampled data for the low frequency part of the k-space. We used one-dimensional simulation data and 3-dimensional gradient echo data for 2 different echo times (TEs) of the brains of volunteers and assessed the differences between the image reconstructed from the full k-space data and AFI images reconstructed from truncated k-space data. We generated AFI images in this study using the zero-filling, Margosian (homodyne), Margosian+POCS (standard POCS), MagAFI, and MagAFI+POCS techniques.

Results: We confirmed the assumption of smaller phase errors for the full k-space data than for the symmetric low frequency k-space data. Our proposed MagAFI technique provides images with smaller phase-induced errors than those obtained using conventional methods, including standard POCS methods, which have been regarded as the best methods. MagAFI+POCS improves image quality as well as robustness.

Conclusion: Our proposed MagAFI technique achieves a practical balance of image quality and simplicity to perform better than conventional methods using only the 0-filled magnitude image. Combined with POCS, this technique can produce images of even better quality.

Publication types

  • Comparative Study

MeSH terms

  • Algorithms
  • Brain / anatomy & histology
  • Computer Simulation
  • Fourier Analysis
  • Humans
  • Image Enhancement / methods
  • Image Processing, Computer-Assisted / statistics & numerical data*
  • Imaging, Three-Dimensional / statistics & numerical data
  • Magnetic Resonance Imaging / statistics & numerical data*
  • Models, Theoretical