Chemical exchange saturation transfer (CEST) is an emerging molecular imaging technique that can detect various biomolecules in vivo. However, the routine clinical application of CEST MRI is hammered by its long scan time due to the multiple saturation frames acquired. Here, a novel deep neural network modified from the variational network (VN) by utilizing cross-domain regularization structures, dubbed CEST-VN, is proposed for accelerated CEST imaging. In conjunction with multi-coil sensitivity encoding, the CEST-VN method demonstrated superior performance to the conventional parallel imaging and the original VN methods in healthy volunteers and glioma patients.
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