Near-isotropic high-resolution magnetic resonance imaging (MRI) of the knee is beneficial for reducing partial volume effects and allowing multi-planar image analysis. However, previous methods exploring isotropic resolutions, typically compromised in-plane resolution for thin slices, due to intrinsic signal-to-noise ratio (SNR) limitations. Even computer-vision-based super-resolution methods have been rarely been used in medical imaging due to limited resolution improvements. In this study, we utilize deep-learning-based 3D super-resolution for rapidly generating high-resolution thin-slice knee MRI from slices originally 2-8 times thicker. Through quantitative image quality metrics and a reader study, we demonstrate superior performance to both conventionally utilized and state-of-the-art super-resolution methods.