Optimization of spin-lock times in T1ρ mapping of knee cartilage: Cramer-Rao bounds versus matched sampling-fitting

Purpose: To compare different optimization approaches for choosing the spin-lock times (TSLs), in spin-lattice relaxation time in the rotating frame (T-1 rho) mapping. Methods: Optimization criteria for TSLs based on Cramer-Rao lower bounds (CRLB) are compared with matched sampling-fitting (MSF) approaches for T-1 rho mapping on synthetic data, model phantoms, and knee cartilage. The MSF approaches are optimized using robust methods for noisy cost functions. The MSF approaches assume that optimal TSLs depend on the chosen fitting method. An iterative non-linear least squares (NLS) and artificial neural networks (ANN) are tested as two possible T-1 rho fitting methods for MSF approaches. Results: All optimized criteria were better than non-optimized ones. However, we observe that a modified CRLB and an MSF based on the mean of the normalized absolute error (MNAE) were more robust optimization approaches, performing well in all tested cases. The optimized TSLs obtained the best performance with synthetic data (3.5-8.0% error), model phantoms (1.5-2.8% error), and healthy volunteers (7.7-21.1% error), showing stable and improved quality results, comparing to non-optimized approaches (4.2-13.3% error on synthetic data, 2.1-6.2% error on model phantoms, 9.8-27.8% error on healthy volunteers). Conclusion: A modified CRLB and the MSF based on MNAE are robust optimization approaches for choosing TSLs in T-1 rho mapping. All optimized criteria allowed good results even using rapid scans with two TSLs when a complex-valued fitting is done with iterative NLS or ANN.

Automated summary

By comparing optimization criteria, it was found that the modified CRLB and MSF based on MNAE are robust optimization approaches for choosing TSLs in T-1 rho mapping, performing well in different test cases.