Partial Fourier (PF) is a widely used fast imaging scheme. Since phase information is crucial in many applications, such as SWI, it is necessary that PF can preserve phase well. Many PF methods cannot preserve phase well especially at locations with rapid phase change. DPA is a method can recover both magnitude and phase well, but suffers from low speed for two-directional PF acquisition. Considering recent advances in deep learning, we proposed a DNN-based framework for two-directional PF reconstruction. Preliminary experiments demonstrate that the proposed method is almost 50 times faster while restores magnitude and SWI even better than DPA.