The goal of low-field (64 mT) portable point-of-care (POC) MRI is to produce low cost, clinically acceptable MR images in reasonable scan times. However, non-ideal MRI behaviors make the image quality susceptible to artifacts from system imperfections and undersampling. In this work, a deep learning approach is proposed for fast reconstruction from hardware and sampling-associated imaging artifacts. The proposed approach outperforms the reference deep learning approaches for retrospectively undersampled data with simulated system imperfections. Furthermore, we demonstrate that it yields better image quality and faster reconstruction than compressed sensing approach for unseen, prospectively undersampled low-field POC MR images.