Fast Chemical Exchange Saturation Transfer Imaging Based on PROPELLER Acquisition and Deep Neural Network Reconstruction

Purpose: To develop a method for fast chemical exchange saturation transfer (CEST) imaging.

Methods: The periodically rotated overlapping parallel lines enhanced reconstruction (PROPELLER) sampling scheme was introduced to shorten the acquisition time. Deep neural network was employed to reconstruct CEST contrast images. Numerical simulation and experiments on a creatine phantom, hen egg, and in vivo tumor rat brain were performed to test the feasibility of this method.

Results: The results from numerical simulation and experiments show that there is no significant difference between reference images and CEST-PROPELLER reconstructed images under an acceleration factor of 8.

Conclusion: Although the deep neural network is trained entirely on synthesized data, it works well on reconstructing experimental data. The proof of concept study demonstrates that the combination of the PROPELLER sampling scheme and the deep neural network enables considerable acceleration of saturated image acquisition and may find applications in CEST MRI.