Fast and accurate determination of T1 values can be accomplished by Look-Locker type MRI sequences. Here, we formulate T1 mapping as a nonlinear optimization problem which we subsequently solve by the iteratively regularized Gauss Newton (IRGN) method. Our choice of the model parameterization allows to exploit smoothness of the spatial flip angle distribution as additional prior knownledge. This model-based reconstruction allows accurate and precise reconstruction of high resolution T1 maps from radial, highly undersampled data as validated in phantom studies and demonstrated in the human brain.
