There is a strong need for an accurate and easily available technique for myocardial blood flow (MBF) estimation to aid in the diagnosis and treatment of coronary artery disease (CAD). Dynamic CT would provide a quick and widely available technique to do so. However, its biggest limitation is the dose imparted to the patient. We are exploring techniques to reduce the patient dose by either reducing the tube current or by reducing the number of temporal frames in the dynamic CT sequence. Both of these dose reduction techniques result in very noisy data. In order to extract the myocardial blood flow information from the noisy sinograms, we have been looking at several data-domain smoothing techniques. In our previous work,1 we explored the sinogram restoration technique in both the spatial and temporal domain. In this work, we explore the use of Karhunen-Loeve (KL) transform to provide temporal smoothing in the sinogram domain. This technique has been applied previously to dynamic image sequences in PET.2, 3 We find that the cluster-based KL transform method yields noticeable improvement in the smoothness of time attenuation curves (TAC). We make use of a quantitative blood flow model to estimate MBF from these TACs and determine which smoothing method provides the most accurate MBF estimates.