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

Citation

Contijoch, Francisco (2020), Closed-loop control of k-space sampling via physiologic feedback for cine MRI, Dryad, Dataset, https://doi.org/10.6076/D1159C

Abstract

This dataset accompanies the manuscript outlining a method for closed-loop sampling of k-space in response to physiologic changes. 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.

Briefly, 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 to increase sampling uniformity.

The dataset includes both the algorithm and the data used in our manuscript. Our 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.

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.

This data is shared with Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication

Methods

ECGs in 10 normal subjects were recorded while in an MRI scanner for evaluation of the approach.

Imaging data using the method was acquired in 4 normal subjects. 

Usage Notes

The code utilizes Matlab to do the analysis of ECG data. Image reconstruction utilizes other publically available tools

 

Links to publications that cite or use the data:

The manuscript for this data is:
Contijoch et al. Closed-loop control of k-space sampling via physiologic feedback for cine MRI. https://www.medrxiv.org/content/10.1101/2020.06.22.20137638v1

Please cite the dataset as:
Contijoch, Francisco (2020), Closed-loop control of k-space sampling via physiologic feedback for cine MRI, Dryad, Dataset, https://doi.org/10.6076/D1159C

DATA & FILE OVERVIEW

File List: The set consists of 9 zip files.
code.zip - Matlab files for closed-loop ECG sampling based control of MRI k-space sampling
Simulation_Data.zip  - ECG recordings of 10 humans for simulation of MRI sampling with ARKS
Simulation_Analysis.zip - Code which performs simulation experiments
Simulation_Results.zip - Results of simulation used for publication

Human_Data.zip  - Data from acquisitions with ARKS on clinical MRI scanner
Human_Analysis.zip - Code which analyzes in-vivo experiments
Human_Results.zip - Results of human imaging used for publication

Funding

National Heart, Lung, and Blood Institute, Award: HL120580

National Heart, Lung, and Blood Institute, Award: HL108157

National Heart, Lung, and Blood Institute, Award: HL963954