Meeting Banner
Abstract #0125

Simultaneous Multi-Slice Flyback Echo Planar Imaging with Auto-Calibration

Kangrong Zhu1, Adam Kerr1, John M. Pauly1

1Stanford University, Stanford, CA, United States

The Blipped-CAIPI technique performs simultaneous multi-slice EPI acquisition with reduced g-factor penalty, but usually requires external calibration scans for image reconstruction. In this work, a data acquisition scheme which does not need any external calibration scans is designed for simultaneous multi-slice EPI and is demonstrated in in vivo brain imaging. The internal auto-calibration in the proposed method minimizes the image artifacts introduced by the mismatch between the calibration data and the accelerated data. The reconstructed images of the proposed method can have higher SNR than the Blipped-CAIPI method if the internal auto-calibration signal is included in the final images.

Abstract PDF Presentation Video

Keywords

accelerated acme acquisition acquisitions adjusted advantage aliasing alternate among appears arise arrow arrows artifacts auto background blipped blips blue brain calibrating calibration caused central channel coil conditions conducted contain containing coronal correspond crosses decay degrade degrees develop dimension display displayed displays distortions dots eliminated excited external eyeball flow fold fully future ghosting gradient hamper head healthy helpful highlight hollow illustrated included inclusion indicate inset instead intensity interleaved interleaves internal introduced john kernel lead length limiting little match matched matrix mismatch mostly motion need negative overall parallel pattern patterns picked planar positive practice problem proposed qualities quality readout receiving reconstructed reconstruction reformatted reformulated related restricted restricting rotated sampled samples sampling scanner scheme select sensitivity sets shorten shot simultaneous simultaneously since slice slices slightly smooth solid sources space spacing step subject successive timings train twice unlike usually utilized variation views volunteer waveforms whole window yellow