A model-based reconstruction approach is presented that allows time efficient encoding of radial data for high resolution 3D T1 quantification e.g. (256x256x52, 1mm3, tscan=96s) by exploiting structural similarities in 3D and between different parameter maps. The proposed method employs a 3D TGV-Frobenius regularization to achieve high quality parameter maps from 3D VFA and 3D IRLL data. A dedicated reconstruction framework, consisting of an iteratively-regularized Gauss-Newton algorithm combined with a Primal-Dual splitting is employed to solve the optimization problem. Reconstructed parameter maps exhibit high SNR and no residual streaking artifacts while reconstructed T1 values agree well with reported values from literature.