Golden-ratio rotated stack-of-stars acquisition for improved volumetric MRI.
OBJECTIVE
To develop and evaluate an improved stack-of-stars radial sampling strategy for reducing streaking artifacts.


METHODS
The conventional stack-of-stars sampling strategy collects the same radial angle for every partition (slice) encoding. In an undersampled acquisition, such an aligned acquisition generates coherent aliasing patterns and introduces strong streaking artifacts. We show that by rotating the radial spokes in a golden-angle manner along the partition-encoding direction, the aliasing pattern is modified, resulting in improved image quality for gridding and more advanced reconstruction methods. Computer simulations were performed and phantom as well as in vivo images for three different applications were acquired.


RESULTS
Simulation, phantom, and in vivo experiments confirmed that the proposed method was able to generate images with less streaking artifact and sharper structures based on undersampled acquisitions in comparison with the conventional aligned approach at the same acceleration factors. By combining parallel imaging and compressed sensing in the reconstruction, streaking artifacts were mostly removed with improved delineation of fine structures using the proposed strategy.


CONCLUSIONS
We present a simple method to reduce streaking artifacts and improve image quality in 3D stack-of-stars acquisitions by re-arranging the radial spoke angles in the 3D partition direction, which can be used for rapid volumetric imaging. Magn Reson Med 78:2290-2298, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.|Department of Bioengineering, University of California, Los Angeles, California, USA.
Ziwu
Z
Zhou
Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.|Department of Bioengineering, University of California, Los Angeles, California, USA.
Fei
F
Han
Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Department of Neurology, University of Southern California, Los Angeles, California, USA.
Lirong
L
Yan
Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.|Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Department of Neurology, University of Southern California, Los Angeles, California, USA.
Danny J J
DJJ
Wang
Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.|Biomedical Physics Interdepartmental Graduate Program, University of California, Los Angeles, California, USA.
Peng
P
Hu
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D064368:Healthy Volunteers; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D018810:Magnetic Resonance Angiography; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted
3D gridding; compressed sensing; golden ratio; parallel imaging; stack of stars