Magnitude-based Asymmetric Fourier Imaging (MagAFI).
OBJECTIVE
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.


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.


CONCLUSIONS
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.
Clinical Application Research and Development Department, Center for Medical Research and Development, Toshiba Medical Systems Corporation.
Tokunori
T
Kimura
Takashi
T
Shigeta
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D003198:Computer Simulation; D005583:Fourier Analysis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008962:Models, Theoretical