Cardiac MRI performed while the patient is breathing is typically achieved using non-real-time techniques such as ECG triggering with respiratory gating; however, modern dynamic imaging techniques are beginning to enable this type of imaging in real-time. One of these dynamic imaging techniques is based on forming a Partially Separable Function (PSF) model of the data, but the model fitting process is known to be sensitive even when truncated SVD regularization is used. As a result, physiologically meaningless artifacts can appear in the dynamic images when the total number of measurements is limited. To address this issue, the dynamic imaging problem is formulated as a generalized Tikhonov regularization problem with the PSF model as a component of the forward data model, and a penalty function is used to introduce spatial-spectral prior information. This new method both reduces data acquisition requirements and improves stability relative to the original PSF based method when applied to cardiac MRI.