Transformer Enabled Half Z-spectrum Sampling B 0 Inhomogeneity Correction for GluCEST and NOE MRI

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High Resolution Image Download MS PowerPoint Slide Purpose . Chemical Exchange Saturation Transfer (CEST) MRI relies on multiple saturation offsets to correct B 0 inhomogeneity-induced quantification errors at the cost of a prolonged scan time. We previously developed a deep learning-based method for B 0 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 B 0 correction using only downfield Z-spectrum offset images, further reducing scan time by about 50%. Methods . B 0 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 B 0 inhomogeneity correction accuracy. Conclusion . Efficient B 0 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.