Transformer Enabled Half Z‑spectrum Sampling B<sub>0</sub> Inhomogeneity Correction for GluCEST and NOE MRI.
Authors
Affiliations (3)
Affiliations (3)
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland 21021, United States.
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States.
Abstract
<i>Purpose</i>. Chemical Exchange Saturation Transfer (CEST) MRI relies on multiple saturation offsets to correct B<sub>0</sub> inhomogeneity-induced quantification errors at the cost of a prolonged scan time. We previously developed a deep learning-based method for B<sub>0</sub> 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<sub>0</sub> correction using only downfield Z-spectrum offset images, further reducing scan time by about 50%. <i>Methods</i>. B<sub>0</sub> 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. <i>Results</i>. 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<sub>0</sub> inhomogeneity correction accuracy. <i>Conclusion</i>. Efficient B<sub>0</sub> 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.