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Four deconvolution algorithms\u2014Fermi function modeling, deconvolution using B\u2010spline basis, deconvolution using exponential basis, and autoregressive moving average modeling \u2014were tested to calculate voxel\u2010wise perfusion estimates. The algorithms were developed on synthetic data and validated against a true gold\u2010standard using a hardware perfusion phantom. The accuracy of each method was assessed for different levels of spatial averaging and perfusion rate. Finally, voxel\u2010wise analysis was used to generate high resolution perfusion maps on real data acquired from five patients with suspected coronary artery disease and two healthy volunteers. On both synthetic and perfusion phantom data, the B\u2010spline method had the highest error in estimation of myocardial blood flow. The autoregressive moving average modeling and exponential methods gave accurate estimates of myocardial blood flow. The Fermi model was the most robust method to noise. 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