Self‐calibrating wave‐encoded 3D turbo spin echo imaging using subspace model based autofocusing

Purpose To develop a self‐calibrating approach for the estimation of wave point spread function (PSF) and coil sensitivities from the subsampled wave‐encoded k‐space, and evaluate its performance for wave‐encoded 3D turbo spin echo (TSE) imaging. Methods A low rank subspace parametric model was demonstrated in simulation to improve the representation for practical wave encoding k‐space trajectories with aperiodicity, and an autofocus metric for the entire imaging volume was used to calibrate the wave PSF in a 2‐stage manner from coarse to refined estimation. The coil sensitivities can be extracted from the shifted central region of wave PSF corrected subsampled k‐space, and further used with wave PSF for wave‐encoded parallel imaging (PI) reconstruction. The wave encoding gradients were integrated into the 3D TSE sequence considering eddy current reduction aspects and maintaining of the Carr‐Purcell‐Meiboom‐Gill condition. Phantom and in vivo brain experiments were performed to evaluate the accuracy of wave PSF self‐calibration and to compare the PI reconstruction performance between wave and Cartesian encoding scheme. Results The self‐calibrated wave PSF, estimated from different k‐space undersampling patterns can robustly correct the wave encoding induced image artifacts given sufficient central autocalibration data. The self‐calibrating wave‐encoded PI reconstruction has demonstrated its improved performance in reduced aliasing artifacts and noise amplification in comparison to the Cartesian‐encoded PI reconstruction results for 3D TSE imaging. Conclusion The proposed self‐calibrating wave‐encoded method allows robust calibration of wave PSF and coil sensitivities from the subsampled k‐space, and improves the overall image quality for accelerated 3D TSE imaging.