Anatomically Guided Deep Learning Reconstruction of Accelerated Snapshot CEST MRI

Abstract

Abstract Purpose: To determine whether structural MRI information can improve reconstruction of highly accelerated 3D snapshot chemical exchange saturation transfer (CEST) MRI. Methods: Fully sampled brain CEST data were retrospectively undersampled at acceleration factors (AFs) of 4, 6, 8, 10, and 12. We compared reconstruction without structural information with population-level pretraining using T1-weighted (T1w), T2-weighted (T2w), or combined T1w-T2w images, as well as direct conditioning using a co-registered subject-specific T1w image. Performance was evaluated in two held-out healthy subjects using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) across frequency offsets. Z-spectrum fidelity and derived magnetization transfer ratio asymmetry (MTRasym) maps were additionally evaluated within three-dimensional gray- and white-matter masks. Results: Subject-specific T1w conditioning consistently achieved the highest PSNR and SSIM and the lowest MAE across AFs, with greater improvements at higher acceleration. It also improved Z-spectrum agreement, particularly in white matter and combined gray- and white-matter regions across most AFs. Improvements in MTRasym were more modest and varied across tissue types and acceleration factors. Conclusion: Incorporating subject-specific structural information can improve reconstruction of highly accelerated snapshot CEST MRI, particularly at higher acceleration factors. However, improvements in reconstructed source images did not consistently translate into better preservation of derived CEST contrast, highlighting the need for reconstruction strategies that more directly incorporate CEST spectral information.