Keywords: CEST / APT / NOE, CEST & MT, Transformer, DL-based imputation, Accelerated MRI
Motivation: Chemical exchange saturation transfer (CEST) MRI provides metabolic information in vivo with high spatial specificity. However, acquisition of the canonical Z-spectrum and multiple contrasts is time-consuming and thus hinders rapid clinical translation.
Goal(s): Develop a novel method using a deep neural network to accelerate CEST acquisitions across offsets and saturation powers.
Approach: A state-of-the-art transformer-based network is used to recover densely sampled Z-spectra from sparsely sampled offsets across multiple saturation powers, allowing for an accelerated multi-B1 CEST acquisition.
Results: The neural network performs well in recovering sparsely sampled Z-spectra across multiple B1s, with low RMSE ad high image fidelity.
Impact: A state-of-the-art transformer-based network, SAITS, successfully recovers sparsely sampled Z-spectrum offsets and B1s, allowing for multi-contrast CEST MRI and B1 inhomogeneity correction. Given low reconstruction error and high image fidelity, this method facilitates rapid clinical translation of Z-spectrum acquisitions.
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