Speaker
Description
The longitudinal relaxation time $T_1$ is an important biomarker in clinical cardiac MRI, e.g. for myocardial fibrosis. Conventional $T_1$ mapping is time-consuming and requires multiple breath holds. Therefore, we developed a sequence with a continuous radial readout which allows us to quantify $T_1$ within seconds. For reconstruction of the $T_1$ maps, the physical signal model of the MRI measurement sequence is incorporated as a constraint, which enables reconstruction with good image quality even from highly undersampled acquisition.
For a computationally efficient reconstruction, we approximate signal evolutions of the underlying nonlinear $T_1$ encoding model in a linear subspace spanned by four basis functions. A nonlinear forward model is set up, which maps the subspace coefficients and coil sensitivities to the acquired k-space data. Parallel imaging as nonlinear inversion is solved by the Iteratively Regularized Gauss Newton Method. Finally, the physical parameters of the signal model are pixel-wisely fitted to the coefficient maps in the subspace to obtain the $T_1$ map.
