  Quantitative MRI (qMRI) is becoming increasingly important for research and
clinical applications, however, state-of-the-art reconstruction methods for
qMRI are computationally prohibitive. We propose a temporal multiscale approach
to reduce computation times in qMRI. Instead of computing exact gradients of
the qMRI likelihood, we propose a novel approximation relying on the temporal
smoothness of the data. These gradients are then used in a coarse-to-fine (C2F)
approach, for example using coordinate descent. The C2F approach was also found
to improve the accuracy of solutions, compared to similar methods where no
multiscaling was used.
