Datensatz

Zusammenfassung

Isolated evaluation of multi-parametric in vivo CEST MRI often requires complex computational processing for both correction of B0 and B1 inhomogeneity and contrast generation. For that, sufficiently densely sampled Z-spectra need to be acquired. The list of acquired frequency offsets largely determines the total CEST acquisition time, while potentially representing redundant information. In this work, a linear projection-based multi-parametric CEST evaluation method is introduced that offers fast B0 and B1 inhomogeneity correction, contrast generation and feature selection for CEST data, enabling reduction of the overall measurement time. To that end, CEST data acquired at 7T in 6 healthy subjects and in one brain tumor patient were conventionally evaluated by interpolation-based inhomogeneity correction and Lorentzian curve fitting. Linear regression was used to obtain coefficient vectors that directly map uncorrected data to corrected Lorentzian target parameters. L1 regularization was applied to find subsets of the originally acquired CEST measurements that still allow for such a linear projection mapping. The linear projection method allows fast and interpretable mapping from acquired raw data to contrast parameters of interest, generalizing from healthy subject training data to unseen healthy test data and to the tumor patient dataset. The L1 regularization method shows that a fraction of the acquired CEST measurements is sufficient to preserve tissue contrasts, offering up to 2.8-fold reduction of scan time. Similar observations as for the 7T data can be made for data from a clinical 3T scanner. Being a fast and interpretable computation step, the proposed method is complementary to neural networks, which have been recently employed for similar purposes. The scan time acceleration offered by the L1 regularization ('CEST-LASSO') constitutes a step towards better applicability of multi-parametric CEST protocols in clinical context.