Model-based reconstruction#
Quantitative MRI estimates tissue parameters such as \(T_1\) and \(T_2\) from a series of images acquired at different contrasts. Reconstructing each contrast separately and fitting a signal model afterwards ignores the relation between the contrasts that the signal model states. A model-based reconstruction places that relation in the forward operator, so that every contrast constrains the same unknowns. This section treats the two standard formulations: a linear subspace model, in which inversion-recovery signal curves are represented by a few temporal basis functions and \(T_1\) is fitted to the coefficient maps, and a nonlinear signal model, through which \(T_2\) maps are estimated directly from multi-echo k-space. Nonlinear forward models compares the two with reconstruction followed by a voxel-wise fit. The last lesson fits a signal model to magnitude images read from DICOM, as a scanner exports them, and writes the map back.
