Compressed Sensing (CS) is a recently emerged technique for reconstructing signals from data sampled under the Nyquist rate. It takes advantage of the signal sparsity in a transformed domain to reconstruct high-resolution signals from reduced data. This paper presents a CS imaging method for dynamic magnetic resonance imaging. Specifically, a difference operator is applied to the temporal data frames to enhance the spatial signal sparsity for CS reconstruction. The new algorithm method was assessed using simulated and in-vivo dynamic imaging data. The result shows that the new method can obtain higher resolution than zero-padded Fourier reconstruction and the Keyhole method, and it results in reduced artifacts and noise than conventional CS reconstruction where no temporal information is used. It also shows that the new CS dynamic imaging method does not suffer substantial signal-to-noise loss.