Quantitative Susceptibility Mapping (QSM) is an ill-posed inverse problem. Traditionally, it is solved by minimization of a functional. Regularization terms may be interpreted as a denoising process. Many state-of-the-art methods are based on Total Variation regularization terms, with great success. With the advent of Deep Learning, new regularization strategies have been derived from training datasets. Total Deep Variation (TDV) is a recently proposed technique, showing impressive results. We applied a pre-trained TDV network as a denoising step in an iterative QSM solver. Results show improved error metrics for synthetic brain phantoms and enhanced in-vivo reconstructions, compared to Total-Variation-based algorithms.