Deep learning to reconstruct quasi-steady-state chemical exchange saturation transfer from non-steady-state experiment.

The insufficiently long RF saturation duration and relaxation delay in chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) experiments may result in underestimation of CEST measurements. To maintain the CEST effect without prolonging the saturation duration and reach quasi-steady-state (QUASS), a deep learning method was developed to reconstruct a QUASS CEST image pixel-by-pixel from non-steady-state CEST acquired in experiments. In this work, we established a tumor-bearing rat model on a 7T horizontal bore small animal MRI scanner, allowing ground-truth generation; after which a bidirectional long short-term memory network was formulated and trained on simulated CEST Z-spectra to reconstruct the QUASS CEST; finally the ground-truth yielded by experiments were used to evaluate the performance of the reconstruction model by comparing the estimates with the ground-truth. For quantitation evaluation, linear regression analysis, structural similarity index (SSIM) and peak signal-to-noise ratio (peak-SNR) were used to assess the proposed model in the QUASS CEST reconstruction. In the linear regression analysis of in vivo data, the coefficient of determination for six different representative frequency offsets was at least R 2 = 0.9521. Using the SSIM and peak-SNR as evaluation metrics, the reconstruction accuracies of in vivo QUASS CEST were found as 0.9991 and 46.7076, respectively. Experimental results demonstrate that the proposed model provides a robust and accurate solution for QUASS CEST reconstruction using a deep learning mechanism.