Optimizing spatiotemporal sampling for k‐t BLAST and k‐t SENSE: Application to high‐resolution real‐time cardiac steady‐state free precession

In k‐t BLAST and k‐t SENSE, data acquisition is accelerated by sparsely sampling k‐space over time. This undersampling in k‐t space causes the object signals to be convolved with a point spread function in x‐f space (x = spatial position, f = temporal frequency). The resulting aliasing is resolved by exploiting spatiotemporal correlations within the data. In general, reconstruction accuracy can be improved by controlling the k‐t sampling pattern to minimize signal overlap in x‐f space. In this work, we describe an approach to obtain generally favorable patterns for typical image series without specific knowledge of the image series itself. These optimized sampling patterns were applied to free‐breathing, untriggered (i.e., real‐time) cardiac imaging with steady‐state free precession (SSFP). Eddy‐current artifacts, which are otherwise increased drastically in SSFP by the undersampling, were minimized using alternating k‐space sweeps. With the synergistic combination of the k‐t approach with optimized sampling and SSFP with alternating k‐space sweeps, it was possible to achieve a high signal‐to‐noise ratio, high contrast, and high spatiotemporal resolutions, while achieving substantial immunity against eddy currents. Cardiac images are shown, demonstrating excellent image quality and an in‐plane resolution of ∼2.0 mm at >25 frames/s, using one or more receiver coils. Magn Reson Med 53:1372–1382, 2005. © 2005 Wiley‐Liss, Inc.

[1]  N. J. A. Sloane,et al.  Sphere Packings, Lattices and Groups , 1987, Grundlehren der mathematischen Wissenschaften.

[2]  A. Haase,et al.  Rapid NMR Imaging Using Low Flip-Angle Pulses , 2004 .

[3]  E. Fleck,et al.  Functional cardiac MR imaging with steady‐state free precession (SSFP) significantly improves endocardial border delineation without contrast agents , 2001, Journal of magnetic resonance imaging : JMRI.

[4]  Peter Boesiger,et al.  Accelerating cardiac cine 3D imaging using k‐t BLAST , 2004, Magnetic resonance in medicine.

[5]  Peter Boesiger,et al.  k‐t BLAST and k‐t SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations , 2003, Magnetic resonance in medicine.

[6]  N J Pelc,et al.  Unaliasing by Fourier‐encoding the overlaps using the temporal dimension (UNFOLD), applied to cardiac imaging and fMRI , 1999, Magnetic resonance in medicine.

[7]  Jeffrey Tsao,et al.  On the UNFOLD method , 2002, Magnetic resonance in medicine.

[8]  W. Edelstein,et al.  Spin warp NMR imaging and applications to human whole-body imaging. , 1980, Physics in medicine and biology.

[9]  R M Henkelman,et al.  K‐space description for MR imaging of dynamic objects , 1993, Magnetic resonance in medicine.

[10]  G. Pohost,et al.  Block Regional Interpolation Scheme for k‐Space (BRISK): A Rapid Cardiac Imaging Technique , 1995, Magnetic resonance in medicine.

[11]  R Pohmann,et al.  Theoretical evaluation and comparison of fast chemical shift imaging methods. , 1997, Journal of magnetic resonance.

[12]  K. Scheffler,et al.  Single‐breathhold 3D‐trueFISP cine cardiac imaging , 2002, Magnetic resonance in medicine.

[13]  Yoram Bresler,et al.  Lattice-theoretic analysis of time-sequential sampling of spatiotemporal signals: II. Large space-bandwidth product asymptotics , 1997, IEEE Trans. Inf. Theory.

[14]  Peter Boesiger,et al.  On the influence of training data quality in k‐t BLAST reconstruction , 2004, Magnetic resonance in medicine.

[15]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[16]  F H Epstein,et al.  Adaptive sensitivity encoding incorporating temporal filtering (TSENSE) † , 2001, Magnetic resonance in medicine.

[17]  Yoram Bresler,et al.  Lattice-theoretic analysis of time-sequential sampling of spatiotemporal signals: I , 1997, IEEE Trans. Inf. Theory.

[18]  M Fuderer,et al.  The information content of MR images. , 1988, IEEE transactions on medical imaging.