Transformer Enabled Half Z‑spectrum Sampling B₀ Inhomogeneity Correction for GluCEST and NOE MRI

Purpose. Chemical Exchange Saturation Transfer (CEST) MRI relies on multiple saturation offsets to correct B₀ inhomogeneity-induced quantification errors at the cost of a prolonged scan time. We previously developed a deep learning-based method for B₀ inhomogeneity correction in Glutamate-weighted CEST (GluCEST) using a parsimonious number of Z-spectrum offset images, which can significantly reduce scan time and provide better correction quality. In this study, we propose a Transformer-based model that achieves B₀ correction using only downfield Z-spectrum offset images, further reducing scan time by about 50%. Methods. B₀ correction was performed separately for the positive and negative sides of the Z-spectrum using reduced saturation offset acquisitions. We constructed distinct Swin transformer networks for each side, training them to learn the nonlinear mapping from a limited number of GluCEST images at various frequencies on the positive side to the specific 3 ppm points where GluCEST peaks. A similar methodology was applied to NOE CEST imaging, optimizing each network to effectively handle the unique characteristics of each spectrum side. Results. The Transformer-based models significantly outperformed the previous deep learning methods both visually and quantitatively. By limiting inputs to only the positive Z-spectrum offsets, we achieved a 50% reduction in data acquisition time compared with previous deep learning approaches while maintaining B₀ inhomogeneity correction accuracy. Conclusion. Efficient B₀ inhomogeneity correction in GluCEST and NOE MRI can be achieved by using a select number of offset images from the downfield Z-spectrum, reducing the acquisition time by over 80%. The proposed transformer-based model demonstrates superior performance over traditional convolutional neural networks, offering a robust and efficient solution for performing CEST MRI in clinical practice.