A CVAE-based generative model for generalized B inhomogeneity corrected chemical exchange saturation transfer MRI at 5 T.
Journal:
NeuroImage
Published Date:
Apr 21, 2025
Abstract
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 B level. Spatial inhomogeneity of B field would bias CEST measurement. Conventional interpolation-based B correction method required CEST dataset acquisition under multiple B levels, substantially prolonging scan time. The recently proposed supervised deep learning approach reconstructed B inhomogeneity corrected CEST effect at the identical B as of the training data, hindering its generalization to other B levels. In this study, we proposed a Conditional Variational Autoencoder (CVAE)-based generative model to generate B inhomogeneity corrected Z spectra from single CEST acquisition. The model was trained from pixel-wise source-target paired Z spectra under multiple B with target B 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 B inhomogeneity corrected CEST MRI. Results showed that the generated B-corrected Z spectra agreed well with the reference averaged from regions with subtle B inhomogeneity. Moreover, the performance of the proposed model in correcting B inhomogeneity in APT CEST effect, as measured by both MTR and [Formula: see text] at 3.5 ppm, were superior over conventional Z/contrast-B-interpolation and other deep learning methods, especially when target B were not included in sampling or training dataset. In summary, the proposed model allows generalized B inhomogeneity correction, benefiting quantitative CEST MRI in clinical routines.
