Highly-accelerated Bloch-Siegert |B1+| mapping using joint autocalibrated parallel image reconstruction

Magn Reson Med. 2014 Apr;71(4):1470-7. doi: 10.1002/mrm.24804. Epub 2013 Jul 1.

Abstract

Purpose: To reconstruct accurate single- and multichannel Bloch-Siegert transmit radiofrequency (|B(1)(+)|) field maps from highly accelerated data.

Theory and methods: The approach is based on the fact that the |B(1)(+)|-to-phase encoding pulse for each transmit coil and off-resonance frequency applies a unique phase shift to the same underlying image. This enables joint reconstruction of all images in a Bloch-Siegert acquisition from an augmented set of virtual receive coils, using any autocalibrated parallel imaging reconstruction method.

Results: Simulations with an eight channel transmit/receive array head coil at 7T show that accurate |B(1)(+)| maps can be produced at acceleration factors of 16× and 6× for Cartesian and spiral sampling, respectively. A phantom experiment with a six channel transverse electromagnetic (TEM) transceive array coil allowed accurate reconstruction at 16× acceleration. 7T in vivo experiments performed using 32 channel receive and two-channel transmit coils further demonstrate the proposed method's ability to produce high-quality |B(1)(+)| maps at accelerations of 32× and 8× for Cartesian and spiral trajectories, respectively. Reconstruction accuracy is improved using disjoint k-space sampling patterns between acquisitions.

Conclusion: The proposed approach allows high acceleration factors in Bloch-Siegert |B(1)(+)| mapping and can significantly reduce the scan time requirements for mapping the |B(1)(+)| fields of transmit arrays.

Keywords: Bloch-Siegert acquisition; auto-calibrated; disjoint sampling; image reconstruction; parallel transmission.

MeSH terms

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