[High-resolution functional cardiac MR imaging using density-weighted real-time acquisition and a combination of compressed sensing and parallel imaging for image reconstruction].
OBJECTIVE
The aim of this study was to perform high-resolution functional MR imaging using accelerated density-weighted real-time acquisition (DE) and a combination of compressed sensing (CO) and parallel imaging for image reconstruction.


METHODS
Measurements were performed on a 3 T whole-body system equipped with a dedicated 32-channel body array coil. A one-dimensional density-weighted spin warp technique was used, i. e. non-equidistant phase encoding steps were acquired. The two acceleration techniques, compressed sensing and parallel imaging, were performed subsequently. From a complete Cartesian k-space, a four-fold uniformly undersampled k-space was created. In addition, each undersampled time frame was further undersampled by an additional acceleration factor of 2.1 using an individual density-weighted undersampling pattern for each time frame. Simulations were performed using data of a conventional human in-vivo cine examination and in-vivo measurements of the human heart were carried out employing an adapted real-time sequence.


RESULTS
High-quality DECO real-time images using parallel acquisition of the function of the human heart could be acquired. An acceleration factor of 8.4 could be achieved making it possible to maintain the high spatial and temporal resolution without significant noise enhancement.


CONCLUSIONS
DECO parallel imaging facilitates high acceleration factors, which allows real-time MR acquisition of the heart dynamics and function with an image quality comparable to that conventionally achieved with clinically established triggered cine imaging.
Wech
T
T
Institut für Röntgendiagnostik, Universität Würzburg.
Gutberlet
M
M
Greiser
A
A
Stäb
D
D
Ritter
C O
CO
Beer
M
M
Hahn
D
D
Köstler
H
H
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis / Q000503:physiopathology; D016477:Artifacts; D001185:Artificial Intelligence; D003971:Diastole / Q000502:physiology; D004417:Dyspnea / Q000209:etiology / Q000503:physiopathology; D004867:Equipment Design; D006801:Humans; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000295:instrumentation / Q000379:methods*; D009200:Myocardial Contraction / Q000502:physiology*; D012680:Sensitivity and Specificity; D012984:Software; D013599:Systole / Q000502:physiology