Recent theoretical advances in the field of compressive sampling-also referred to as compressed sensing (CS)-hold considerable promise for practical applications in MRI, but the fundamental condition of sparsity required in the CS framework is usually not fulfilled in MR images. However, in dynamic imaging, data sparsity can readily be introduced by applying the Fourier transformation along the temporal dimension assuming that only parts of the field-of-view (FOV) change at a high temporal rate while other parts remain stationary or change slowly. The second condition for CS, random sampling, can easily be realized by randomly skipping phase-encoding lines in each dynamic frame. In this work, the feasibility of the CS framework for accelerated dynamic MRI is assessed. Simulated datasets are used to compare the reconstruction results for different reduction factors, noise, and sparsity levels. In vivo cardiac cine data and Fourier-encoded velocity data of the carotid artery are used to test the reconstruction performance relative to k-t broad-use linear acquisition speed-up technique (k-t BLAST) reconstructions. Given sufficient data sparsity and base signal-to-noise ratio (SNR), CS is demonstrated to result in improved temporal fidelity compared to k-t BLAST reconstructions for the example data sets used in this work.