  This study presents a comparison of quantitative MRI methods based on an
efficiency metric that quantifies their intrinsic ability to extract
information about tissue parameters. Under a regime of unbiased parameter
estimates, an intrinsic efficiency metric $\eta$ was derived for fully-sampled
experiments which can be used to both optimize and compare sequences. Here we
optimize and compare several steady-state and transient gradient-echo based
qMRI methods, such as magnetic resonance fingerprinting (MRF), for joint T1 and
T2 mapping. The impact of undersampling was also evaluated, assuming incoherent
aliasing that is treated as noise by parameter estimation. In-vivo validation
of the efficiency metric was also performed. Transient methods such as MRF can
be up to 3.5 times more efficient than steady-state methods, when spatial
undersampling is ignored. If incoherent aliasing is treated as noise during
least-squares parameter estimation, the efficiency is reduced in proportion to
the SNR of the data, with reduction factors of 5 often seen for practical SNR
levels. In-vivo validation showed a very good agreement between the theoretical
and experimentally predicted efficiency. This work presents and validates an
efficiency metric to optimize and compare the performance of qMRI methods.
Transient methods were found to be intrinsically more efficient than
steady-state methods, however the effect of spatial undersampling can
significantly erode this advantage.
