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Self‐calibrated interpolation of non‐Cartesian data with GRAPPA in parallel imaging
Author(s) -
Chieh SengWei,
Kaveh Mostafa,
Akçakaya Mehmet,
Moeller Steen
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28033
Subject(s) - interpolation (computer graphics) , computer science , imaging phantom , calibration , cartesian coordinate system , conjugate gradient method , computer vision , artificial intelligence , iterative reconstruction , algorithm , image quality , kernel (algebra) , mathematics , physics , optics , geometry , image (mathematics) , statistics , combinatorics
Purpose To develop a non‐Cartesian k‐space reconstruction method using self‐calibrated region‐specific interpolation kernels for highly accelerated acquisitions. Methods In conventional non‐Cartesian GRAPPA with through‐time GRAPPA (TT‐GRAPPA), the use of region‐specific interpolation kernels has demonstrated improved reconstruction quality in dynamic imaging for highly accelerated acquisitions. However, TT‐GRAPPA requires the acquisition of a large number of separate calibration scans. To reduce the overall imaging time, we propose Self‐calibrated Interpolation of Non‐Cartesian data with GRAPPA (SING) to self‐calibrate region‐specific interpolation kernels from dynamic undersampled measurements. The SING method synthesizes calibration data to adapt to the distinct shape of each region‐specific interpolation kernel geometry, and uses a novel local k‐space regularization through an extension of TT‐GRAPPA. This calibration approach is used to reconstruct non‐Cartesian images at high acceleration rates while mitigating noise amplification. The reconstruction quality of SING is compared with conjugate‐gradient SENSE and TT‐GRAPPA in numerical phantoms and in vivo cine data sets. Results In both numerical phantom and in vivo cine data sets, SING offers visually and quantitatively similar reconstruction quality to TT‐GRAPPA, and provides improved reconstruction quality over conjugate‐gradient SENSE. Furthermore, temporal fidelity in SING and TT‐GRAPPA is similar for the same acceleration rates. G‐factor evaluation over the heart shows that SING and TT‐GRAPPA provide similar noise amplification at moderate and high rates. Conclusion The proposed SING reconstruction enables significant improvement of acquisition efficiency for calibration data, while matching the reconstruction performance of TT‐GRAPPA.