Highly accelerated aortic 4D flow MR imaging with variable-density random undersampling

Abstract

Purpose: 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.

Materials and 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.

Conclusion: 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.

Keywords: Flow; Parallel imaging; Random; Time-resolved; Undersampling; View sharing.

Figure 1

Data sampling strategies. a) Variable-density Poisson-Disk sampling on k_y-k_z plane through time (each block is for one time frame; k-space center is fully sampled), b) the samples at each time frame selected with STIRRUP for generating a composite data at the time frame of interest, c) composite sampling patterns through time. The composite data sets are reconstructed as STIRRUP results, which are used for improving the initial solution for SPIRiT (iiSPIRiT).

Figure 2

Cross-sectional view of the Ascending Aorta (AA) and Descending Aorta (DA) for: a) Magnitude image, and b) velocity image. Normalized root-mean square errors (NRMSEs) versus length of sharing for: c) magnitude images; and d) through-plane mean velocities within AA. The group of curves with iiSPIRiT (c&d) corresponds to different numbers of iterations (1 to 20). NRMSEs versus number of SPIRiT iterations for: e) magnitude images; and f) through-plane mean velocities within AA. The identified optimal view sharing length of L=7 (15 time frames) was used in e&f.

Figure 3

4D data sets from the aorta. a) Magnitude and b–d) flow images reconstructed with the fully sampled data set and undersampled data sets with vPDS using STIRRUP, zfSPIRiT and iiSPIRiT reconstructions. Flow velocity profiles across the AA and DA (dashed line) are plotted through time (e).

Figure 4

Comparisons of per-pixel flow velocities in the AA and DA with different undersampling strategies. (a) ROIs through the AA and DA. (b–d) linear regression and Bland-Altman plots of the undersampled data with the fully sampled data of all subjects.

Figure 5

Comparisons of flow-waveforms in the AA and DA with different undersampling strategies.. a) Flow waveforms from all subjects. b–d) linear regression and Bland-Altman plots of the undersampled data compared to the reference. All acceleration methods have good correlations with the reference, especially the STIRRUP and iiSPIRIT methods. The bias data points were caused by the phase degradation due to artifacts with zfSPIRiT.

Figure 6

The NRMSEs of the AA and DA flow waveforms obtained from three subjects. The overall errors are 0.06 ± 0.04 (STIRRUP), 0.14 ± 0.10 (zfSPIRiT), 0.04 ± 0.02 (iiSPIRiT).

Figure 7

Pathline visualization of the aorta of three subjects (a–c). zfSPIRIT has more errors and shorter pathlines (arrows).

Figure 8

Evaluation in a carotid aneurysm model. a–f) Linear regression and Bland-Altman plots of per-pixel flow velocities with different acceleration methods compared to the fully sampled data. g–j) show pathline visualization with the different methods. Compared to the reference (g), STIRRUP (h) has a visually smaller region of high velocities (solid arrow) that corresponds to the reduced flow velocities (underestimation), and zfSPIRiT has flow discontinuities (i, hollow arrow), while the iiSPIRiT result is closest to the reference.