Segmented K-Space Blipped-Controlled Aliasing in Parallel Imaging (Skipped-CAIPI) for High Spatiotemporal Resolution Echo Planar Imaging

Author:

Stirnberg RüdigerORCID,Stöcker TonyORCID

Abstract

AbstractPurposeA segmented k-space blipped-CAIPI (skipped-CAIPI) sampling strategy for echo planar imaging (EPI) is proposed, which allows for a flexible choice of EPI factor and phase encode bandwidth independent of the controlled aliasing (CAIPIRINHA) pattern.Theory and MethodsWith previously proposed approaches, exactly two EPI trajectories were possible given a specific CAIPIRINHA pattern: either with slice gradient blips (blipped-CAIPI), or following a shot-selective approach (higher resolution). Recently, interleaved multi-shot segmentation along shot-selective CAIPI trajectories has been applied for high-resolution anatomical imaging. For more flexibility and a broader range of applications, we propose segmentation along any blipped-CAIPI trajectory. Thus, all EPI factors and phase encode bandwidths available with traditional segmented EPI can be combined with controlled aliasing.ResultsTemporal signal-to-noise ratios of moderate-to-high-resolution time series acquisitions at varying undersampling factors demonstrate beneficial sampling alternatives to blipped-CAIPI or shot-selective CAIPI. Rapid high-resolution scans furthermore demonstrate SNR-efficient and motion-robust structural imaging with almost arbitrary EPI factor and minimal noise penalty.ConclusionsSkipped-CAIPI sampling increases protocol flexibility for high spatiotemporal resolution EPI. In terms of signal-to-noise ratio and efficiency, high-resolution functional or structural scans benefit vastly from a free choice of the CAIPIRINHA pattern. Even at moderate resolutions, the independence of sampling pattern, echo time and image matrix size is valuable for optimized functional protocol design. Although demonstrated with 3D-EPI, skipped-CAIPI is also applicable with simultaneous multislice EPI.

Publisher

Cold Spring Harbor Laboratory

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1. Layer-dependent functional connectivity methods;Progress in Neurobiology;2021-12

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