Parallel MRI can achieve increased spatiotemporal resolution in MRI by simultaneously sampling reduced k-space datawith multiple receiver coils. One requirement that different parallel MRI techniques have in common is the need to determine spatial sensitivity information for the coil array. This is often done by smoothing the raw sensitivities obtained from low-resolution calibration images, for example via polynomial fitting. However, this sensitivity post-processing can be both time-consuming and error-prone. Another important factor in Parallel MRI is noise amplification in the reconstruction, which is due to non-unity transformations in the image reconstruction associated with spatially correlated coil sensitivity profiles. Generally, regularization approaches, such as Tikhonov and SVD-based methods, are applied to reduce SNR loss, at the price of introducing residual aliasing. In this work, we present a regularization approach using in vivo coil sensitivities in parallel MRI to overcome these potential errors into the reconstruction. The mathematical background of the proposed method is explained, and the technique is demonstrated with phantom images. The effectiveness of the proposed method is then illustrated clinically in a whole-heart 3D cardiac MR acquisition within a single breath-hold. The proposed method can not only overcome the sensitivity calibration problem, but also suppress a substantial portion of reconstruction-related noise without noticeable introduction of residual aliasing artifacts.