Encoding capability prediction of acquisition schedules in CEST MR fingerprinting for pH quantification

Purpose: To identify a reliable metric for predicting the encoding capability of CEST MR fingerprinting acquisition schedules for pH quantification, which may facilitate CEST MR fingerprinting protocol optimization.

Methods: Numerical simulations and Cr phantom MRI experiments were conducted at 3 Tesla under representative CEST MR fingerprinting sampling scenarios, including the pseudorandomization of imaging parameters (e.g., saturation power B₁ , saturation frequency offset, saturation time, and relaxation time), and variation of the maximum saturation power B_1max , B₁ number, and sampling pattern. The CEST effect at 2 ppm was measured using asymmetry analysis and matched to a predefined dictionary to determine the pH. The pH quantification error was assessed using RMSE. Three metrics, namely the Cramer-Rao bound, dot product, and Euclidean distance, were calculated for each sampling scenario, and their relationships with the pH RMSE were investigated to examine their effectiveness for predicting the encoding capability of sampling schedules for pH quantification.

Results: Both simulation and phantom studies revealed that the Cramer-Rao bound metric consistently exhibited superior performance for predicting the pH quantification error. Although dot product exhibited good encoding capability prediction in most sampling scenarios, it failed in the scenario with varied B₁ numbers. In contrast, Euclidean distance exhibited the worst performance among the 3 metrics in all scenarios.

Conclusion: Superior over dot product and Euclidean distance, the Cramer-Rao bound metric may reliably predicting the encoding capability of CEST MR fingerprinting sampling strategies and may be useful for guiding CEST MRI protocol optimization.