Wave-CAIPI for highly accelerated 3D imaging

Abstract

Purpose: To introduce the wave-CAIPI (controlled aliasing in parallel imaging) acquisition and reconstruction technique for highly accelerated 3D imaging with negligible g-factor and artifact penalties.

Methods: The wave-CAIPI 3D acquisition involves playing sinusoidal gy and gz gradients during the readout of each kx encoding line while modifying the 3D phase encoding strategy to incur interslice shifts as in 2D-CAIPI acquisitions. The resulting acquisition spreads the aliasing evenly in all spatial directions, thereby taking full advantage of 3D coil sensitivity distribution. By expressing the voxel spreading effect as a convolution in image space, an efficient reconstruction scheme that does not require data gridding is proposed. Rapid acquisition and high-quality image reconstruction with wave-CAIPI is demonstrated for high-resolution magnitude and phase imaging and quantitative susceptibility mapping.

Results: Wave-CAIPI enables full-brain gradient echo acquisition at 1 mm isotropic voxel size and R = 3 × 3 acceleration with maximum g-factors of 1.08 at 3T and 1.05 at 7T. Relative to the other advanced Cartesian encoding strategies (2D-CAIPI and bunched phase encoding) wave-CAIPI yields up to two-fold reduction in maximum g-factor for nine-fold acceleration at both field strengths.

Conclusion: Wave-CAIPI allows highly accelerated 3D acquisitions with low artifact and negligible g-factor penalties, and may facilitate clinical application of high-resolution volumetric imaging.

Keywords: CAIPIRINHA, quantitative susceptibility mapping; parallel imaging; phase imaging.

Fig. 1

Gradient waveforms and k-space trajectory for Wave-CAIPI imaging. Sinusoidal g_y and g_z gradients with a π/2 phase shift between the waveforms incur a corkscrew trajectory in k-space. The corkscrews are also staggered due to the 2D-CAIPI sampling strategy to create inter-slice shifts.

Fig. 2

Bunch encoding gradient waveforms g_y and g_z incur voxel spreading in the readout dimension (x-axis). The amount of this spreading is a function of y and z positions, respectively. 2D-CAIPI strategy creates inter-slice shifts in the phase encoding dimension (y-axis). Wave-CAIPI combines the effect of g_y and g_z gradients with slice shifting, and spreads out voxels in all three spatial dimensions.

Fig. 3

Ry=2-fold acceleration causes voxels that are FOVy/2 apart to collapse on each other. Since Wave-CAIPI spreads out the voxels in x-axis, the aliasing voxels are further apart from each other. This increases the variation in coil sensitivity profiles, and improves parallel imaging.

Fig. 4

R=3×3-fold retrospectively accelerated acquisitions. Wave-CAIPI and conventional gradient echo (GRE) reconstructions are compared at 3T (left) and 7T (right). Wave-CAIPI offers 2-fold improvement in artifact power and maximum g-factor at both field strengths.

Fig. 5

R=3×3-fold prospectively accelerated imaging at 3T. Wave-CAIPI, 2D-CAIPI and Bunched Phase Encoding reconstructions, 1/g-factor maps and their respective k-space sampling patterns are demonstrated.

Fig. 6

R=3×3-fold prospectively accelerated imaging at 7T. Wave-CAIPI, 2D-CAIPI and Bunched Phase Encoding reconstructions and 1/g-factor maps are compared.

Fig. 7

Tissue phase and quantitative susceptibility maps derived from Wave-CAIPI, 2D-CAIPI and Bunched Phase Encoding at 3T and 1 mm3 isotropic resolution are compared. Note the artifacts indicated by the arrows stemming from imperfect parallel imaging reconstruction.

Fig. 8

Tissue phase and susceptibility maps from Wave-CAIPI, 2D-CAIPI and Bunched Phase Encoding reconstructions at 7T and 1 mm³ isotropic resolution. Note the artifacts indicated by the arrows in the susceptibility map detail.

Fig. 9

The non-Cartesian k-space trajectory traversed by the Wave gradient can be explained as additional phase deposited on the Cartesian space. This leads to the point spread function (PSF) formalism that characterizes the voxel spreading effect over the whole FOV. Within each voxel itself, the Wave gradient also causes a differential phase modulation. The sidelobes of the intra-voxel PSF is 0.3% of the main lobe, thus incurring negligible spreading within each voxel.