Paper detail

Group-kernel auto-calibration and group-patch k-space reconstruction: Fast MRI with time-variant B0 kernels partitioned into time-invariant subsets

Purpose: Pushing MRI speed further demands more spatially-encoded information captured per unit time, e.g., by superimposing additional field modulations during oversampled readout. However, this can introduce calibration errors and increase reconstruction time. Thus, we propose a continuous field calibration approach and an efficient k-space reconstruction technique. Theory and Methods: Our auto-calibration generalizes GRAPPA kernels to explicitly extract continuous B0 modulation kernels, solving interpolation relationships between two ACS regions differing only in the extra field modulation. The k-space locations sharing the same instantaneous image-space modulation are grouped, so that subsets of time-invariant kernels can be separately estimated, as a generalized solution for Wave-CAIPI/FRONSAC-type scans. This view further inspires a k-space subregion-wise reconstruction technique, as an efficient alternative to conventional hybrid-space reconstruction. At 9.4T, FLASH accelerated by a local B0 coil array and Wave-CAIPI were tested with retrospective undersampling. Results: Artifact-free images were reconstructed, under diverse rapid B0 modulation schemes, reaching maximum acceleration factors of 8-fold in 2D and 14.6-fold in 3D. Some nonlinear gradients modulation schemes reach similar sampling efficiency as linear gradients modulation. The proposed reconstruction shows potentials in reducing reconstruction time. Conclusion: Rapid B0 modulations and widely-adopted parallel imaging can share a common mathematical framework, and consequently, achieve similarly-robust reconstructions. Specifically, for scans using dynamic B0 and static RF kernels, not only signal encoding, but also auto-calibration and reconstruction can be performed in k-space. This paves the way to robustly remove eddy currents, and explore more complex B0 modulation strategies towards ultimate MRI speed.