In this work we evaluate the performance and the robustness of an algorithm for the reconstruction of B1+-maps out of highly accelerated Bloch-Siegert data. The algorithm is based on variational modeling with a problem specific regularization approach. We evaluate the influence of different sampling patterns on the achievable accuracy and sampling efficiency and the influence of both regularization parameters on the final result. All results are compared to the fully-sampled reference using conventional reconstruction. We can show the general robustness of our algorithm and the most effective sampling pattern for this purpose.