Assessment of a clinically feasible Bayesian fitting algorithm using a simplified description of Chemical Exchange Saturation Transfer (CEST) imaging

Fitting a model based on the Bloch-McConnell (BM) equations to Chemical Exchange Saturation Transfer (CEST) spectra allows for the quantification of metabolite concentration and exchange rate as well as simultaneous correction of field inhomogeneity, direct water saturation and magnetization transfer. Employing a Bayesian fitting approach permits the integration of prior information into the analysis to incorporate expected parameter distributions and to prevent over-fitting. However, the analysis can be time consuming if a general numerical solution of the BM equations is applied. In this study, we combined a Bayesian fitting algorithm with approximate analytical solutions of the BM equations to achieve feasible computational times. To evaluate the accuracy and speed of the suggested approach, phantoms including Iodipamide, Taurine and Creatine were tested in addition to simulated data with continuous-wave (CW) and pulsed saturation with Gaussian pulses. A significant reduction of computational time was achieved when fitting CW data (about 50-fold) and pulsed saturation data (more than 100-fold) with the analytical model while the estimated parameters were largely consistent with the parameters from the general numerical solution. The increased speed of the algorithm facilitates the Bayesian analysis of CEST data within clinically feasible processing times. Other analytical models valid for different parameter regimes may be employed to extend the applicability to a wider range of CEST agents.