Figure 1. Typical current independent fitting of dynamic data vs. proposed dynamic fitting.

The typically used approach in fitting a model to dynamic MRS data (top) is to model the changing parameters after an independent spectral fitting stage (where each spectrum is treated independently). The proposed approach (and as examined by Tal³⁴) is to simultaneously fit a spectral and dynamic model. This is known as dynamic, “2D,” or spectral-temporal fitting. This approach reduces the number of parameters to fit by allowing estimation of shared model parameters at once. This shared estimation increases the amount of data used to estimate parameters that are expected to be static (or functionally linked) across transients, mitigating the effect of noise which would otherwise result in multiple, low precision estimates of the parameter. This results in a decrease in parameter uncertainty. N_Param: Total number of fitted parameters, N_Metab: number of metabolite concentration parameters, N_Nuisance: number of spectral fitting parameters not of direct interest (e.g., line broadening), N_Model: number of dynamic model parameters.