Using sequences of coronary angiograms to quantify the severity of stenosis

Automatic processing of images from coronary X-ray angiographies using deep learning techniques has been explored, but the ability to estimate accurately physiological indexes such as the instantaneous wave-free ratio (iFR) and/or the Fractional Flow Reserve (FFR) has not yet been demonstrated.. The objective of this dissertation is to develop a methodology that can estimate the value of the iFR from sequences of angiographies that has sufficient precision to avoid the need for invasive measurement methods, such as the commonly used insertion of a guidewire with a pressure sensor inserted through a coronary catheter. The approach that will be used in the application of deep learning techniques to frame sequences, using additional information from the sequence that cannot be obtained from single frame analysis.