X-ray CT is widely used, both clinically and pre-clinically, for fast, high-resolution, anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, temporally-resolved CT data can detail cardiac motion and blood flow dynamics for one-stop cardiovascular CT imaging procedures. In previous work, we demonstrated efficient, low-dose projection acquisition and reconstruction strategies for cardiac micro-CT imaging and for multiple-injection micro-CT perfusion imaging. Here, we extend this previous work with regularization based on rank-sparse kernel regression and on filtration with the Karhunen-Loeve transform. Using a dual source, prospectively gated sampling strategy which produces an approximately uniform distribution of projections, we apply this revised algorithm to the assessment of both myocardial perfusion and cardiac functional metrics from the same set of projection data. We test the algorithm in simulations using a modified version of the MOBY mouse phantom which contains realistic perfusion and cardiac dynamics. The proposed algorithm reduces the reconstruction error by 81% relative to unregularized, algebraic reconstruction. The results confirm our ability to simultaneously solve for cardiac temporal motion and perfusion dynamics. In future work, we will apply the algorithm and sampling protocol to small animal cardiac studies.