Wave-CAIPI is a novel technique that enables accelerated acquisition with negligible g-factor penalty by using corkscrew readout trajectories, while LORAKS (LOw-RAnk modeling of local K-Space neighborhoods) is a powerful approach to constrained reconstruction that integrates sparse support, phase, and parallel imaging constraints into a unified linear prediction framework. In this work, we propose a new fast imaging technique called Wave-LORAKS, which combines Wave-CAIPI acquisition with LORAKS-based reconstruction. Retrospective undersampling experiments with 3D T1-weighted data show that Wave-LORAKS enables higher acceleration and more flexible sampling compared to traditional Wave-CAIPI, allowing up to 15-fold acceleration with similar quality as 9-fold accelerated Wave-CAIPI.
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