Authors: M. Fernandes, J. Festas, L.C. Sousa, J.M.R.S. Tavares, S.I.S. Pinto

The present work is a proof of concept that measures the ejection fraction (EF%) and quantifies the aortic valve calcification using Computed Tomography (CT) images of a patient flagged by medical doctors for presenting symptoms associated with cardiac issues. A computational framework that performs the image segmentation process was coded in Python language. Thresholding and flood fill methods were used to delineate the calcifications and the blood inside the ventricle, respectively. Along with DICOM information, the end-systole and end-diastole volumes (ESV and EDV) were measured, and as a result, EF% was calculated. The valvular calcified volume (CV) and Agatston score were also calculated. Results show that the EF% of the studied patient is in the normal levels (66.1%) with an average CV of 0.861 cm3. This proof of concept reinforces the potential that software methods applied to Medicine have on improving diagnosis and patient outcomes.

M. Fernandes ^1,2 | J. Festas^1,2 | L.C. Sousa^1,2 | J.M.R.S. Tavares^1,2 | S.I.S. Pinto^1,2
1 Faculdade de Engenharia, Universidade do Porto, Departamento de Engenharia Mecânica
2 Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial