Highly accelerated aortic 4D flow MR imaging with variable-density random undersampling.
OBJECTIVE
To investigate an effective time-resolved variable-density random undersampling scheme combined with an efficient parallel image reconstruction method for highly accelerated aortic 4D flow MR imaging with high reconstruction accuracy.


METHODS
Variable-density Poisson-disk sampling (vPDS) was applied in both the phase-slice encoding plane and the temporal domain to accelerate the time-resolved 3D Cartesian acquisition of flow imaging. In order to generate an improved initial solution for the iterative self-consistent parallel imaging method (SPIRiT), a sample-selective view sharing reconstruction for time-resolved random undersampling (STIRRUP) was introduced. The performance of different undersampling and image reconstruction schemes were evaluated by retrospectively applying those to fully sampled data sets obtained from three healthy subjects and a flow phantom.


RESULTS
Undersampling pattern based on the combination of time-resolved vPDS, the temporal sharing scheme STIRRUP, and parallel imaging SPIRiT, were able to achieve 6-fold accelerated 4D flow MRI with high accuracy using a small number of coils (N=5). The normalized root mean square error between aorta flow waveforms obtained with the acceleration method and the fully sampled data in three healthy subjects was 0.04±0.02, and the difference in peak-systolic mean velocity was -0.29±2.56cm/s.


CONCLUSIONS
Qualitative and quantitative evaluation of our preliminary results demonstrate that time-resolved variable-density random sampling is efficient for highly accelerating 4D flow imaging while maintaining image reconstruction accuracy.
Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States. Electronic address: jing.liu@ucsf.edu.
Jing
J
Liu
Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States; Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Petter
P
Dyverfeldt
Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.
Gabriel
G
Acevedo-Bolton
Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.
Michael
M
Hope
Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States; Radiology Service, VA Medical Center, San Francisco, CA, United States.
David
D
Saloner
D000328:Adult; D001011:Aorta / Q000473:pathology*; D001783:Blood Flow Velocity; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods; D008297:Male; D019047:Phantoms, Imaging; D016012:Poisson Distribution; D011446:Prospective Studies; D015203:Reproducibility of Results; D012189:Retrospective Studies; D013599:Systole; D013997:Time Factors
Flow; Parallel imaging; Random; Time-resolved; Undersampling; View sharing