Respiratory self-gated four-dimensional coronary MR angiography: a feasibility study.
The four-dimensional (4D) coronary MR angiography (MRA) approach has been developed to eliminate the need for accurate determination of the acquisition window and trigger delay time. Diaphragm navigator (NAV) has been the conventional respiratory gating method for free-breathing coronary MRA. However, NAV echo acquisition interrupts the continuous radiofrequency pulse application required for 4D steady-state free precession coronary MRA. The objective of this work was to investigate the feasibility of a respiratory self-gating (RSG) technique for 4D coronary MRA and its effectiveness by comparing with retrospective NAV gating. Data were acquired continuously throughout the cardiac cycle and retrospectively remapped to cardiac phases based on the electrocardiogram signal simultaneously recorded. An RSG signal extracted from a direct measurement of the heart position was used for retrospective respiratory gating and motion correction. In seven healthy volunteers, 4D MRA images were reconstructed, allowing retrospective assessment of the cardiac motion of the coronary artery and selection of the images with the best vessel delineation. Statistical analysis shows that 4D RSG provides coronary artery delineation comparable to mid-diastole images acquired using NAV. Respiratory self-gating is an effective method for eliminating respiratory motion artifacts and allows 4D coronary MRA during free breathing.
Department of Radiology, Northwestern University, Chicago, Illinois 60611, USA.
Peng
P
Lai
Andrew C
AC
Larson
Jaeseok
J
Park
James C
JC
Carr
Debiao
D
Li
D000328:Adult; D017023:Coronary Angiography / Q000379:methods*; D003326:Coronary Circulation / Q000502:physiology*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D012119:Respiration; D012189:Retrospective Studies; D018709:Statistics, Nonparametric