  Purpose: To develop a model-based deep neural network for high-quality image
reconstruction of undersampled multi-coil chemical exchange saturation transfer
(CEST) data. Theory and Methods: Inspired by the variational network, the CEST
image reconstruction equation is unrolled into a deep neural network (CEST-VN)
with a k-space data-sharing block that takes advantage of the inherent
redundancy in adjacent CEST frames and 3D spatial-frequential convolution
kernels that exploit correlations in the x-{\omega} domain. Additionally, a new
pipeline based on multiple-pool Bloch-McConnell simulations is devised to
synthesize multi-coil CEST data from publicly available anatomical MRI data.
The proposed neural network is trained on simulated data with a CEST-specific
loss function that jointly measures the structural and CEST contrast. The
performance of CEST-VN was evaluated on three healthy volunteers and five brain
tumor patients using retrospectively undersampled data with various
acceleration factors, and compared with other state-of-the-art reconstruction
methods. Results: The proposed CEST-VN method generated high-quality CEST
source images and APT-weighted (APTw) maps in healthy and brain tumor subjects,
consistently outperforming GRAPPA, blind compressed sensing, and the original
variational network. With the acceleration factors increasing from 3 to 6,
CEST-VN with the same hyperparameters yielded similar and accurate
reconstruction without apparent loss of details or increase of artifacts. The
ablation studies confirmed the effectiveness of the joint CEST-specific loss
function and data-sharing block used. Conclusions: The proposed CEST-VN method
can offer high-quality CEST source images and APTw maps from highly
undersampled multi-coil data by integrating the deep-learning prior and
multi-coil sensitivity encoding model.
