Accelerated isotropic sub-millimeter whole-heart coronary MRI: compressed sensing versus parallel imaging

Abstract

Purpose: To enable accelerated isotropic sub-millimeter whole-heart coronary MRI within a 6-min acquisition and to compare this with a current state-of-the-art accelerated imaging technique at acceleration rates beyond what is used clinically.

Methods: Coronary MRI still faces major challenges, including lengthy acquisition time, low signal-to-noise-ratio (SNR), and suboptimal spatial resolution. Higher spatial resolution in the sub-millimeter range is desirable, but this results in increased acquisition time and lower SNR, hindering its clinical implementation. In this study, we sought to use an advanced B1-weighted compressed sensing technique for highly accelerated sub-millimeter whole-heart coronary MRI, and to compare the results to parallel imaging, the current-state-of-the-art, where both techniques were used at acceleration rates beyond what is used clinically. Two whole-heart coronary MRI datasets were acquired in seven healthy adult subjects (30.3 ± 12.1 years; 3 men), using prospective 6-fold acceleration, with random undersampling for the proposed compressed sensing technique and with uniform undersampling for sensitivity encoding reconstruction. Reconstructed images were qualitatively compared in terms of image scores and perceived SNR on a four-point scale (1 = poor, 4 = excellent) by an experienced blinded reader.

Results: The proposed technique resulted in images with clear visualization of all coronary branches. Overall image quality and perceived SNR of the compressed sensing images were significantly higher than those of parallel imaging (P = 0.03 for both), which suffered from noise amplification artifacts due to the reduced SNR.

Conclusion: The proposed compressed sensing-based reconstruction and acquisition technique for sub-millimeter whole-heart coronary MRI provides 6-fold acceleration, where it outperforms parallel imaging with uniform undersampling.

Copyright © 2013 Wiley Periodicals, Inc.

Figures

Figure 1

Schematic for a single iteration of the B1-weighted LOST algorithm used in this study. At every iteration, the current image estimate is mapped to individual coil images by voxel-wise multiplication with sensitivity maps. Data consistency is enforced by replacing the acquired k-space lines. A combined image is generated by summing the voxel-wise product of data-consistent coil images and conjugate of coil sensitivity maps across the coil dimension. This image is then thresholded using LOST. The implementation details of LOST thresholding are given in the Appendix.

Figure 2

An example coronal slice (top) and reformatted coronal image (bottom) of a subject using B1-weighted LOST with 6-fold random undersampling (left) and SENSE with 6-fold uniform undersampling. A cross-section of the LAD is visualized clearly with the proposed technique, whereas noise amplification is apparent in the SENSE-reconstructed image (RCA: right coronary artery, LAD: left anterior descending, LCX: left circumflex).

Figure 3

Example axial reformats using B1-weighted LOST with 6-fold random undersampling (left) and SENSE with 6-fold uniform undersampling. Noise amplification and SENSE folding artifacts in the z-direction (anterior-posterior) are apparent in both of the slices depicting the proximal RCA and the proximal LAD. Similar artifacts are not visible in the proposed LOST technique.

Figure 4

An example coronal slice (top) containing a cross-section of the left main (LM) shows that SENSE images with 6-fold uniform undersampling (right) suffers from noise amplification, whereas the LM is clearly visualized using B1-weighted LOST with 6-fold random undersampling (left). In the reformatted coronal images (bottom), the proximal LCX cannot be tracked due to the high noise level in the SENSE reconstruction, but RCA and LCX branches are avisualized with the LOST technique.