Echo Planar Time-Resolved Imaging (EPTI) with Subspace Reconstruction and Optimized Spatiotemporal Encoding

TL;DR

This paper introduces a subspace reconstruction framework and optimized spatiotemporal encoding for Echo Planar Time-resolved Imaging (EPTI), enabling faster, high-resolution multi-contrast quantitative imaging with reduced artifacts and improved accuracy.

Contribution

The work develops a novel subspace reconstruction method and a temporal-variant CAIPI encoding for EPTI, enhancing reconstruction accuracy and acceleration capabilities.

Findings

Achieved higher accuracy than conventional B0-informed GRAPPA in 2D EPTI.

Demonstrated up to 72x acceleration with minimal error in 3D EPTI.

Successfully acquired high-quality whole-brain T2* and QSM maps in 52 seconds.

Abstract

Purpose: To develop new encoding and reconstruction techniques for fast multi-contrast quantitative imaging. Methods: The recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion- and blurring-free multi-contrast quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B0-update algorithm and a shot-to-shot B0 variation correction method are developed to enable the reconstruction of high-resolution tissue phase images and to mitigate artifacts from shot-to-shot phase variations. Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a new temporal-variant of CAIPI encoding is proposed to further improve performance. Results: The effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B0-informed GRAPPA. For 3D GE-EPTI, a retrospective undersampling experiment demonstrates that the new temporal-variant CAIPI encoding can achieve up to 72x acceleration with close to 2x reduction in reconstruction error when compared to conventional spatiotemporal-CAIPI encoding. In a prospective undersampling experiment, high-quality whole-brain T2* and QSM maps at 1 mm isotropic resolution was acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI encoding. Conclusion: The proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.