Standard methods for partial Fourier (PF) reconstruction do not perform well in the presence of significant phase variations. In this study, we propose a deep-learning-based approach for PF reconstruction (DPFR) to mitigate this issue. We compare DPFR results against standard methods (Homodyne, POCS) for in vivo images of the foot, leg, and abdomen. We demonstrate that DPFR achieves superior reconstruction quality, especially near phase boundaries, across a range of partial sampling parameters. Ultimately this may extend the applicability of partial Fourier reconstruction to instances where it is not commonly used.