Quantitative comparison between a multiecho sequence and a single-echo sequence for susceptibility-weighted phase imaging

The aim of this study was to investigate the benefits arising from the use of a multiecho sequence for susceptibility-weighted phase imaging using a quantitative comparison with a standard single-echo acquisition. Four healthy adult volunteers were imaged on a clinical 3-T system using a protocol comprising two different three-dimensional susceptibility-weighted gradient-echo sequences: a standard single-echo sequence and a multiecho sequence. Both sequences were repeated twice in order to evaluate the local noise contribution by a subtraction of the two acquisitions. For the multiecho sequence, the phase information from each echo was independently unwrapped, and the background field contribution was removed using either homodyne filtering or the projection onto dipole fields method. The phase information from all echoes was then combined using a weighted linear regression. R2⁎ maps were also calculated from the multiecho acquisitions. The noise standard deviation in the reconstructed phase images was evaluated for six manually segmented regions of interest (frontal white matter, posterior white matter, globus pallidus, putamen, caudate nucleus and lateral ventricle). The use of the multiecho sequence for susceptibility-weighted phase imaging led to a reduction of the noise standard deviation for all subjects and all regions of interest investigated in comparison to the reference single-echo acquisition. On average, the noise reduction ranged from 18.4% for the globus pallidus to 47.9% for the lateral ventricle. In addition, the amount of noise reduction was found to be strongly inversely correlated to the estimated R2⁎ value (R=−0.92). In conclusion, the use of a multiecho sequence is an effective way to decrease the noise contribution in susceptibility-weighted phase images, while preserving both contrast and acquisition time. The proposed approach additionally permits the calculation of R2⁎ maps.