Fractional flow reserve (FFR) has become an important quantitative-index for defining the physiologic significance of coronary stenosis. Pressure-wire based FFR is being increasingly applied to guide treatment and medical therapy in patients with coronary artery disease (CAD). The aim of this study is to develop a new wire-free approach of quantifying FFR using only 2-dimensional fluoroscopic imaging from coronary angiography. FFR is defined as the ratio of maximum myocardial blood flow in the presence of a stenosis to the theoretical maximum flow assumed if absent stenosis. Under the acquisition of maximum coronary vasodilatation, the proposed fluoroscopic FFR is calculated from a sequence of fluoroscopic images that record the dynamic contrast agent in the coronary vessels. Considering that the inflow rate of contrast agent at the proximal arterial segment reflects the theoretical blood flow, we estimate the fluoroscopic FFR as the ratio of blood flows obtained from the measurements of image ROIs respectively located in the proximal artery and in the dependent myocardium. Measured time-density-curve (TDC) of contrast signals is modeled as a gamma-variate function. A healthy coronary branch is also included in the image measurements as a reference. Since the introduced reference branch has the known FFR of unity, fluoroscopic FFR is finally derived, depending only on the time-to-peak (TTP) parameters of the measured TDCs. Evaluation is implemented to seven swine models with a moderate to severe stenosis either in the left anterior descending (LAD) or left circumflex artery (LCx). Image data were acquired at hyperemic condition, and pressure-wire based FFR was recorded as the gold standard for comparison. The average difference of fluoroscopic FFRs and wire-FFRs is ±0.04, and the two measures correlated well with r = 0.982. In summary, a novel method of quantifying FFR is proposed using coronary angiography alone. It is simple and efficient. Since only TTP parameters are used, we may not be confounded about the linearity of image pixels and the foreshortening commonly associated with 2D imaging.