Cardiac real-time cine imaging is valuable for patients who cannot hold their breath or have irregular heart rhythms. Spiral acquisitions, which provide high acquisition efficiency, make high-resolution cardiac cine real-time imaging feasible. However, the reconstruction for under-sampled non-Cartesian real-time imaging is time-consuming, and hence cannot provide rapid feedback. We sought to develop a DEep learning-based rapid Spiral Image REconstruction technique (DESIRE) for spiral real-time cardiac cine imaging with free-breathing, to provide fast and high-quality image reconstruction and make rapid online reconstruction feasible. High image quality was demonstrated using the proposed technique for healthy volunteers and patients.