Kavli Affiliate: Jeremias Sulam
| Authors: Helge J Zollner, Christopher B. Davies-Jenkins, Dunja B. Simicic, Assaf Tal, Jeremias Sulam and Georg Oeltzschner
| Summary:
Purpose: The interest in application and modeling of dynamic MRS has recently grown. Therefore, it is imperative to systematically investigate accuracy, precision, and uncertainty estimation of 2D modeling algorithms. 2D modeling yields advantages for precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similar as 1D modeling of the averaged spectrum. Methods: Monte Carlo simulations of ideal synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multi-transient LCM with conventional 1D LCM of the average. 2,500 datasets per condition with different noise representations of a 64-transient MRS experiment with 6 signal-to-noise levels for two spin systems (scyllo-inositol and GABA) were analyzed. Additional datasets with different level of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by standard deviations and Cramer-Rao Lower Bounds (CRLB). Results: Amplitude estimates for 1D- and 2D-LCM agreed well and showed similar level of bias compared to the ground truth. Estimated CRLBs agreed well between both models and with true CRLBs. For correlated noise the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. Conclusion: Our results indicate that the model performance of 2D multi-transient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as necessary basis for further applications of 2D modeling.