A CVAE-based generative model for generalized B₁ inhomogeneity corrected chemical exchange saturation transfer MRI at...

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful tool to image endogenous or exogenous macromolecules. CEST contrast highly depends on radiofrequency irradiation B₁ level. Spatial inhomogeneity of B₁ field would bias CEST measurement. Conventional interpolation-based B₁ correction method required CEST dataset acquisition under multiple B₁ levels, substantially prolonging scan time. The recently proposed supervised deep learning approach reconstructed B₁ inhomogeneity corrected CEST effect at the identical B₁ as of the training data, hindering its generalization to other B₁ levels. In this study, we proposed a Conditional Variational Autoencoder (CVAE)-based generative model to generate B₁ inhomogeneity corrected Z spectra from single CEST acquisition. The model was trained from pixel-wise source-target paired Z spectra under multiple B₁ with target B₁ as a conditional variable. Numerical simulation and healthy human brain imaging at 5 T were respectively performed to evaluate the performance of proposed model in B₁ inhomogeneity corrected CEST MRI. Results showed that the generated B₁-corrected Z spectra agreed well with the reference averaged from regions with subtle B₁ inhomogeneity. Moreover, the performance of the proposed model in correcting B₁ inhomogeneity in APT CEST effect, as measured by both MTR_asym and [Formula: see text] at 3.5 ppm, were superior over conventional Z/contrast-B₁-interpolation and other deep learning methods, especially when target B₁ were not included in sampling or training dataset. In summary, the proposed model allows generalized B₁ inhomogeneity correction, benefiting quantitative CEST MRI in clinical routines.