Purpose To increase the effectiveness of respiratory gating in radial stack‐of‐stars MRI, particularly when imaging at high spatial resolutions or with multiple echoes. Methods Free induction decay (FID) navigators were integrated into a three‐dimensional gradient echo radial stack‐of‐stars pulse sequence. These navigators provided a motion signal with a high temporal resolution, which allowed single‐spoke binning (SSB): each spoke at each phase encode step was sorted individually to the corresponding motion state of the respiratory signal. SSB was compared with spoke‐angle binning (SAB), in which all phase encode steps of one projection angle were sorted without the use of additional navigator data. To illustrate the benefit of SSB over SAB, images of a motion phantom and of six free‐breathing volunteers were reconstructed after motion‐gating using either method. Image sharpness was quantitatively compared using image gradient entropies. Results The proposed method resulted in sharper images of the motion phantom and free‐breathing volunteers. Differences in gradient entropy were statistically significant (p = 0.03) in favor of SSB. The increased accuracy of motion‐gating led to a decrease of streaking artifacts in motion‐gated four‐dimensional reconstructions. To consistently estimate respiratory signals from the FID‐navigator data, specific types of gradient spoiler waveforms were required. Conclusion SSB allowed high‐resolution motion‐corrected MR imaging, even when acquiring multiple gradient echo signals or large acquisition matrices, without sacrificing accuracy of motion‐gating. SSB thus relieves restrictions on the choice of pulse sequence parameters, enabling the use of motion‐gated radial stack‐of‐stars MRI in a broader domain of clinical applications.
Motion-compensated images can be created from motion-binned undersampled radial stack-of-stars data through compressed sensing and image registration. However, for long repetition times or for many partitions, the acquisition time for one radial projection with all phase-encode steps becomes too long to sample the motion via self-gating, which leads to motion artifacts. Therefore, we estimate motion from FID-navigators and perform binning on a single-readout level to gain higher spatiotemporal resolutions. Our methods are tested on a motion phantom and volunteer with gridding and motion-compensated reconstructions. Our results show accurate detection of the motion signal and reduced motion blur in reconstructions.
Undersampled k-space data reconstruction results in aliasing artifacts. Compressed sensing theory enables image reconstruction by using a priori knowledge in the form of regularization. Increasingly, Machine Learning methods are used to learn the regularization from data itself, but these methods can result in unstable reconstructions. We propose a translation equivariant single-layer neural network for reconstruction of radially measured k-space data. By exploiting translation symmetry, it can learn from randomly simulated data while still being applicable to in-vivo measurements. We tested robustness to small perturbations and reliability of the reconstruction of unexpected objects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.