Closed-loop control of k-space sampling via physiologic feedback for cine MRI

Abstract

Background: Segmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we developed and evaluated an autonomous and closed-loop control system for radial k-space sampling (ARKS) to increase sampling uniformity.

Methods: The closed-loop system autonomously selects radial k-space sampling trajectory during live segmented cine MRI and attempts to optimize angular sampling uniformity by selecting views in regions of k-space that were not previously well-sampled. Sampling uniformity and the ability to detect cardiac phase in vivo was assessed using ECG data acquired from 10 normal subjects in an MRI scanner. The approach was then implemented with a fast gradient echo sequence on a whole-body clinical MRI scanner and imaging was performed in 4 healthy volunteers. The closed-loop k-space trajectory was compared to random, uniformly distributed and golden angle view trajectories via measurement of k-space uniformity and the point spread function. Lastly, an arrhythmic dataset was used to evaluate a potential application of the approach.

Results: The autonomous trajectory increased k-space sampling uniformity by 15±7%, main lobe point spread function (PSF) signal intensity by 6±4%, and reduced ringing relative to golden angle sampling. When implemented, the autonomous pulse sequence prescribed radial view angles faster than the scan TR (0.98 ± 0.01 ms, maximum = 1.38 ms) and increased k-space sampling mean uniformity by 10±11%, decreased uniformity variability by 44±12%, and increased PSF signal ratio by 6±6% relative to golden angle sampling.

Conclusion: The closed-loop approach enables near-uniform radial sampling in a segmented acquisition approach which was higher than predetermined golden-angle radial sampling. This can be utilized to increase the sampling or decrease the temporal footprint of an acquisition and the closed-loop framework has the potential to be applied to patients with complex heart rhythms.

Fig 1. Segmented acquisition using an autonomous closed-loop system.

A) During the acquisition, the closed-loop system identifies prior, similar phases of the cardiac cycle using cross-correlation of the recorded ECG signal. For frame f_n, radial k-space lines from prior phases (red, blue, and teal) as well as the most recent views (yellow) are aggregated and the newest projection θ_n (green) is defined to bisect the largest angle, thus closing the gap in k-space. B) The calculated angle θ_n is provided to the bSSFP sequence in real-time such that the Gx and Gy gradients are updated. C) The closed loop nature of the physiologic signal (ECG), the angle calculator, scanner, and log of prior scan data.

Fig 2. Closed-loop radial sampling of scanning in a healthy volunteer.

Distribution of radial views and corresponding 2D real-time short axis images of closed-loop sampling at A) end-systole and B) end-diastole. Adaptive sampling results in near uniform radial distribution of views and thus high image quality. C) Cardiac motion is shown via projection through the left ventricle.

Fig 3. Point spread function and k-space sampling uniformity of autonomous, golden angle, random, and equispaced radial scanning.

A) Each row on the left shows the 2D point spread function for different segmentation strategies and each column shows autonomous (ARKS), golden or equispaced (Ideal) point spread functions. The fourth column shows a 1D profile for the circularly symmetric PSFs (autonomous shown in red). B) and C) K-space uniformity (Eq 7) and the PSF (Eq 4) for ten subjects with different combinations of shots and segments. The autonomous approach results in improved PSF images (left), signal uniformity (top right), and point spread function lobe ratio (bottom right) across all combinations of shots and segments. ARKS = autonomous radial k-space sampling, PSF = point spread function, N_θ = number of radial views, N_q = number of shots.

Fig 4. Utility of the proposed approach when imaging a patient with arrhythmia.

A) The cross-correlation based approach robustly identifies similar periods in the cardiac cycle in the setting of arrhythmia. B) A high-quality short axis image can be generated with the closed-loop scheme. C) The temporal projection illustrates the effect of complex rhythm on wall motion. This suggests the close-loop approach could enable multi-shot imaging of patients with complex rhythms.