Abstract
PurposeRespiratory motion correction remains a challenge in coronary magnetic resonance imaging (MRI) and current techniques, such as navigator gating, suffer from sub-optimal scan efficiency and ease-of-use. To overcome these limitations, an image-based self-navigation technique is proposed that uses “sub-images” and compressed sensing (CS) to obtain translational motion correction in 2D. The method was preliminarily implemented as a 2D technique and tested for feasibility for targeted coronary imaging.MethodsDuring a 2D segmented radial k-space data acquisition, heavily undersampled sub-images were reconstructed from the readouts collected during each cardiac cycle. These sub-images may then be used for respiratory self-navigation. Alternatively, a CS reconstruction may be used to create these sub-images, so as to partially compensate for the heavy undersampling. Both approaches were quantitatively assessed using simulations and in vivo studies, and the resulting self-navigation strategies were then compared to conventional navigator gating.ResultsSub-images reconstructed using CS showed a lower artifact level than sub-images reconstructed without CS. As a result, the final image quality was significantly better when using CS-assisted self-navigation as opposed to the non-CS approach. Moreover, while both self-navigation techniques led to a 69% scan time reduction (as compared to navigator gating), there was no significant difference in image quality between the CS-assisted self-navigation technique and conventional navigator gating, despite the significant decrease in scan time.ConclusionsCS-assisted self-navigation using 2D translational motion correction demonstrated feasibility of producing coronary MRA data with image quality comparable to that obtained with conventional navigator gating, and does so without the use of additional acquisitions or motion modeling, while still allowing for 100% scan efficiency and an improved ease-of-use. In conclusion, compressed sensing may become a critical adjunct for 2D translational motion correction in free-breathing cardiac imaging with high spatial resolution. An expansion to modern 3D approaches is now warranted.
Highlights
One of the major challenges in free-breathing coronary magnetic resonance imaging (MRI) is the compensation of respiratory motion
An interleaved 2D radial acquisition strategy is used for imaging, since aliasing in undersampled radial sub-images leads to incoherent noise-like artifacts that may be reduced with a nonlinear iterative reconstruction [15]
When using non-linear reconstruction of the sub-images, a further improvement in the visual delineation of the right coronary artery (RCA) is obtained (Fig. 4-c) and the image quality approaches that of the navigator-gated images (Fig. 4-d)
Summary
One of the major challenges in free-breathing coronary magnetic resonance imaging (MRI) is the compensation of respiratory motion. CS is used to reconstruct heavily undersampled images ( referred to as subimages) from the radial k-space readouts acquired during each cardiac cycle To test this hypothesis, an image-based selfnavigation technique that incorporates CS reconstruction, and allows for two-dimensional motion correction, was first implemented as a 2D imaging technique. For testing the feasibility of such an approach, targeted free-breathing in vivo coronary MRA scans were acquired with an interleaved 2D radial sampling scheme where undersampled sub-images were reconstructed either with or without CS from each data segment (interleave) In these sub-images, in-plane translational motion parameters were extracted for each cardiac cycle and motion correction was performed in k-space for each individual subimage. The image quality from both the CS-assisted and the non-CS assisted self-navigation techniques was quantitatively assessed and compared to that from conventional navigator gating
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