Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) can significantly reduce imaging time in MRI, the former by utilizing multiple channel receivers and the latter by utilizing the sparsity of MR images in a transformed domain. In this work, pMRI and CS are integrated to take advantages of the sensitivity information from multiple coils and sparsity characteristics of MR images. Specifically, CS is used as a regularization method for the inverse problem raised by pMRI based on the L1 norm and a Total Variation (TV) term. We test the new method with a set of 8-channel, in-vivo brain MRI data at reduction factors from 2 to 8. Reconstruction results show that the proposed method outperforms several other regularized parallel MRI reconstruction such as the truncated Singular Value Decomposition (SVD) and Tikhonov regularization methods, in terms of residual artifacts and SNR, especially at reduction factors larger than 4.