Keywords: Machine Learning/Artificial Intelligence, fMRI
Motivation: Multi-echo fMRI holds the promise of more potential applications, however it suffers from long readout lengths.
Goal(s): Explore the possibility of using deep learning(DL) reconstruction for highly under-sampled spiral multi-echo fMRI acquisition.
Approach: Multi-echo data from four subjects were collected for DL training. Multi-echo fMRI data from another subject was used for testing the DL model. The DL model has been designed and modified to enable the reconstruction of ten-fold under-sampled fMRI images for BOLD analysis.
Results: The DL model has not only reconstructed the multi-echo spiral fMRI with good image quality, but also preserved its BOLD sensitivity with the highly under-sampled data.
Impact: With the help of DL, multi-echo fMRI may become more versatile for clinical use and future studies.
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