According to reports published by WHO, vascular diseases are leading cause of death and it is very widespread in both developing and developed countries. Therefore, diagnosis of coronary artery diseases plays an important role on health of the whole world. Although there exist many different imaging modalities used for coronary artery imaging like CTA, DSA and MRI, the most commonly used imaging modality in clinics is XRA. Using XRA images, interventional cardiologists give a decision about the treatment planning by investigating anatomic characteristics of stenotic coronary artery. Most of the clinicians do not have QCA tools to quantify the degree of stenosis automatically and they have to inspect the stenosis visually depending on their experience. Since visual inspection of a stenosis depends on the experience of the clinicians, clinicians are not able to agree on the severity of the same stenosis. This phenomenon is called as subjective interpretation and causes wrong decisions about the treatment planning. We propose a remedy to this phenomenon with a novel semi-automatic 2D QCA system which quantifies the stenosis severity by using the anatomical properties of the stenotic region. QCA system we have proposed is based on the deformable splines and their optimization using dynamic programming. Finally, 2D lesion characteristics are compared with the FFR which is a gold standard technique on the determination of functional severity of a stenosis. In this way, correlation between 2D lesion characteristics and functional severity of a stenosis is investigated.
