Feiyu Chen^{1}, Valentina Taviani^{2}, Joseph Y. Cheng^{3}, John M. Pauly^{1}, and Shreyas S. Vasanawala^{3}

In this work, a self-calibrating wave-encoded 3D FSE technique was proposed with self-refocusing gradient waveforms and autocalibrated estimation of wave-encoding point-spread-function and coil sensitivity maps. Compared to conventional Cartesian approach at the same acceleration factor, the proposed method achieves reduced artifacts and better anatomical delineation for highly undersampled abdominal imaging. It enables 10-fold acceleration for 3D FSE scans of the abdomen, allowing 3D FSE sequences to be less sensitive to subject motion.

Introduction

3D fast spin echo (FSE) imaging with variable refocusing flip angles (CUBE/SPACE/VISTA) enables isotropic imaging with high spatial resolution and high signal-to-noise ratio (SNR). However, compared to 2D fast imaging techniquesMethods

Self-refocusing
wave-encoding gradients with 6 cycles/readout and 9.0 mT/m amplitude were
designed to ensure refocusing of the k-space signal at the center of the
calibration region in both phase-encoding directions (y, z) by reducing the
area of the first and last G_{y} gradient lobes by 50% (Fig. 1a). A variable-density
poisson-disk under-sampling pattern was introduced
to reduce the number of acquired readouts by a factor of 10 (Fig. 1b). A 24×32 fully
sampled calibration region was used for self-calibration of the wave-encoding PSF and coil sensitivity estimation. The
wave-encoding PSF was calibrated in both phase-encoding directions based on the
under-sampled wave-encoded k-space^{5} (Fig. 2). Self-calibration of the wave-encoding PSF
was developed by maximizing the square of the normalized image gradient iteratively^{5,6}. Auto-calibrated estimation
of coil sensitivity maps^{7} using ESPIRiT^{8}, and CS-SENSE
image reconstruction^{9} with L1-wavelet regularization were performed
using a combination of Python and C++ (BART toolbox^{10}).

g-Factor maps of wave-encoded and Cartesian acquisitions were estimated theoretically for uniform under-sampling of 3 (PE in y) × 2 (PE in z) using sensitivity maps obtained from the abdominal region of a healthy subject using a 32-channel torso coil (NeoCoil, Pewaukee, WI). Coronal phantom and in-vivo scans were performed with 10-fold acceleration on a 3T scanner (GE MR750, Waukesha, WI) using a 32-channel torso coil with FE in the superior-inferior direction. Conventional Cartesian acquisitions with the same variable-density poisson-disk sampling pattern and reconstruction method were performed for comparison in these studies.

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5. Chen F, Taviani V, Tamir JI, Cheng JY, Zhang T, Song Q, Hargreaves BA, Pauly JM, Vasanawala SS. Self‐Calibrating Wave‐Encoded Variable‐Density Single‐Shot Fast Spin Echo Imaging. Journal of Magnetic Resonance Imaging. 2017 Sep 14. doi:10.1002/jmri.25853

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7. Chen F, Zhang T, Cheng JY, Shi X, Pauly JM, Vasanawala SS. Autocalibrating motion‐corrected wave‐encoding for highly accelerated free‐breathing abdominal MRI. Magnetic resonance in medicine. 2017 Nov 1;78(5):1757-66.

8. Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly J, Vasanawala SS, Lustig M. ESPIRiT - An Eigenvalue Approach to Autocalibrating Parallel MRI: Where SENSE meets GRAPPA. Magn Reson Med 2014; 71:990-1001.

9. Cauley SF, Setsompop K, Bilgic B, Bhat H, Gagoski B, Wald LL. Autocalibrated wave‐CAIPI reconstruction; Joint optimization of k‐space trajectory and parallel imaging reconstruction. Magnetic Resonance in Medicine. 2016; Epub ahead of print.

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