Parallel acquisition techniques in cardiac cine magnetic resonance imaging using TrueFISP sequences: comparison of image quality and artifacts.
OBJECTIVE
To compare image quality, artifacts, and signal-to-noise ratio (SNR) in cardiac cine TrueFISP magnetic resonance imaging (MRI) with and without parallel acquisition techniques (PAT).


METHODS
MRI was performed in 16 subjects with a TrueFISP sequence (1.5 T; Magnetom Sonata, Siemens): TR, 3.0 msec; TE, 1.5 msec; flip angle (FA), 60 degrees. Three axes were scanned without PAT (no PAT) and using the generalized autocalibrating partially parallel acquisition (GRAPPA) and modified sensitivity encoding (mSENSE) reconstruction algorithms with an autocalibration mode to reduce scan time. A conventional spine array and a body flex array were used. Artifacts, image noise, and overall image quality were classified on a 4-point scale by an observer blinded to the implemented technique; for quantitative comparison, SNR was measured.


RESULTS
With a PAT factor of two, acquisition time could be reduced by 39%. No PAT did not show artifacts, and GRAPPA revealed fewer artifacts than mSENSE. PAT provided inferior-quality scores concerning image noise and overall image quality. In quantitative measurements, GRAPPA and mSENSE (20.1 +/- 6.2 and 15.6 +/- 6.2, respectively) yielded lower SNR than no PAT (30.6 +/- 20.1; P < 0.05) and P < 0.001).


CONCLUSIONS
Time savings in PAT are accompanied by artifacts and an increase in image noise. The GRAPPA algorithm was superior to mSENSE concerning image quality, noise, and SNR.
Hunold
Peter
P
Department of Diagnostic and Interventional Radiology, University Hospital, Essen, Germany. peter.hunold@uni-essen.de
Maderwald
Stefan
S
Ladd
Mark E
ME
Jellus
Vladimir
V
Barkhausen
Jörg
J
D000328:Adult; D000465:Algorithms; D016477:Artifacts; D003327:Coronary Disease / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008297:Male; D008875:Middle Aged; D018709:Statistics, Nonparametric