Abstract
Chemical exchange saturation transfer (CEST) is an MRI method that provides insights on the metabolic level. Several metabolite effects appear in the CEST spectrum. These effects are isolated by Lorentzian curve fitting. The separation of CEST effects suffers from the inhomogeneity of the saturation field B1. This leads to inhomogeneities in the associated metabolic maps. Current B1 correction methods require at least two sets of CEST-spectra. This at least doubles the acquisition time. In this study, we investigated the use of an unsupervised physics-informed conditional autoencoder (PICAE) to efficiently correct B1 inhomogeneity and isolate metabolic maps while using a single CEST scan. The proposed approach integrates conventional Lorentzian model into the conditional autoencoder and performs voxel-wise B1 correction and Lorentzian line fitting. The method provides clear interpretation of each step and is inherently generative. Thus, CEST-spectra and fitted metabolic maps can be created at arbitrary B1 levels. This is important because the B1 dispersion contains information about the exchange rates and concentration of metabolite protons, paving the way for their quantification. The isolated maps for tumor data showed a robust B1 correction and more than 25% increase in structural similarity index (SSIM) with gadolinium reference image compared to the standard interpolation-based method and subsequent Lorentzian curve fitting. This efficient correction method directly results in at least 50% reduction in scan time.
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Rajput, J.R. et al. (2023). Physics-Informed Conditional Autoencoder Approach for Robust Metabolic CEST MRI at 7T. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_44
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DOI: https://doi.org/10.1007/978-3-031-43993-3_44
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