Multi-dimensional radial self-navigation with non-linear reconstruction for free-breathing coronary MRI

The main challenge for cardiac MRI is motion. To account for respiratory motion, k-space-based self- navigation approaches have recently been introduced [1]. We took self-navigation to the next level by exploiting an image-based beat-to-beat respiratory motion correction algorithm [2] for coronary MRI. For each heartbeat, under-sampled sub-images (sub-sets of data used for final image reconstruction) are reconstructed and multi-dimensional respiratory motion parameters extracted. Non-linear reconstruction (related to Compressed Sensing) is proposed to generate the motion-corrected sub-images and the results are compared to those of a more conventional approach [3]. These studies include computer simulations and initial 3T human in vivo data.

Self-Navigation with Compressed Sensing for 2D translational Motion Correction in Free-Breathing Coronary MRI: a Feasibility Study

Gabriele Vincenzo Bonanno, Davide Piccini, Gilles Puy, Ruud van Heeswijk, Yves Wiaux

Purpose Respiratory 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. Methods During 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. Results Sub-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. Conclusions CS-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.

Public Library of Science2014

Self-Navigation with Compressed Sensing for 2D translational Motion Correction in Free-Breathing Coronary MRI: a Feasibility Study

Gabriele Vincenzo Bonanno, Davide Piccini, Gilles Puy, Ruud van Heeswijk, Yves Wiaux

Public Library of Science2014