DWI using navigated interleaved multishot EPI with realigned GRAPPA reconstruction.

PURPOSE

A novel k-space reconstruction method is proposed for generating diffusion-weighted imaging (DWI) using navigated interleaved multishot EPI (msEPI).

THEORY AND METHODS

In interleaved msEPI, each shot of data acquired from one coil channel is a subset of the full k-space of that channel. All the k-space subsets of one channel can be treated as an undersampled dataset of a virtual multichannel data, which can be reconstructed by the GRAPPA algorithm after k-space realignment. The intershot phase variations are directly compensated using navigator echoes as the auto-calibrating data in GRAPPA reconstruction. In cases of multichannel msEPI data, all the virtual channels and actual channels can be integrated into a single GRAPPA reconstruction step. The proposed method is tested using both simulation and in-vivo data. The simulation results produced by the proposed method and a SENSE-based method are compared.

RESULTS

The simulated images generated by the proposed method exhibit less relative error compared with those generated by the SENSE method. Inconsistent shot-to-shot phase variation is naturally resolved by GRAPPA calibration without additional phase map processing. High-quality brain DWI with submillimeter resolution is obtained using our proposed reconstruction method.

CONCLUSION

A novel k-space msEPI reconstruction method has been developed for generating high-quality diffusion imaging.