Closed-loop control of k-space sampling via physiologic feedback for cine MRI
Published Web Locationhttps://www.medrxiv.org/content/medrxiv/early/2020/06/23/2020.06.22.20137638.full.pdf
AbstractBackgroundSegmented 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 to increase sampling uniformity.MethodsThe 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 robustness to arrhythmias 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.ResultsThe autonomous trajectory increased k-space sampling uniformity by 13±7%, main lobe point spread function (PSF) signal intensity by 14±6%, 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 ± 0.02 ms, maximum = 1.38 ms) and increased k-space sampling mean uniformity by 5±12%, decreased uniformity variability by 45±14%, and increased PSF signal ratio by 5±5% relative to golden angle sampling.ConclusionThe 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.