Dynamic distortion correction of ME-EPI using learning-based single-scan multi-echo blip up-down acquisition (ME-BUDA)

Motivation: Multi-echo (ME) fMRI approaches based on ME-EPI acquisition achieve higher BOLD sensitivity and reproducibility than traditional EPI. However, ME-EPI suffers from severe image distortion. Goal(s): To develop a technique to correct ME-EPI distortion artifacts in single scan. Approach: We developed a deep learning-based technique to correct ME-EPI distortion artifacts using single-scan multi-echo blip up-down acquisition (ME-BUDA). The proposed method was suitable for both spin- and gradient-echo-based EPI and was validated in simulation, human brain and additional dynamic imaging experiments. Results: For simulation experiment, the PSNR and SSIM were 33.31 and 0.98, respectively. For in vivo dynamic imaging, the temporal SNR increased by 75%. Impact: The ME-BUDA method can reliably correct the geometric distortion of dynamic ME-EPI images without additional information, ensuring the distortion-free, real-time, and high-quality ME-fMRI analysis of important function regions.