RESUMO
BACKGROUND: Progression of aortic valve calcifications (AVC) leads to aortic valve stenosis (AS). Importantly, the AVC degree has a great impact on AS progression, treatment selection and outcomes. Methods of AVC assessment do not provide accurate quantitative evaluation and analysis of calcium distribution and deposition in a repetitive manner. OBJECTIVE: We aim to prepare a reliable tool for detailed AVC pattern analysis with quantitative parameters. METHODS: We analyzed computed tomography (CT) scans of fifty patients with severe AS using a dedicated software based on MATLAB version R2017a (MathWorks, Natick, MA, USA) and ImageJ version 1.51 (NIH, USA) with the BoneJ plugin version 1.4.2 with a self-developed algorithm. RESULTS: We listed unique parameters describing AVC and prepared 3D AVC models with color pointed calcium layer thickness in the stenotic aortic valve. These parameters were derived from CT-images in a semi-automated and repeatable manner. They were divided into morphometric, topological and textural parameters and may yield crucial information about the anatomy of the stenotic aortic valve. CONCLUSION: In our study, we were able to obtain and define quantitative parameters for calcium assessment of the degenerated aortic valves. Whether the defined parameters are able to predict potential long-term outcomes after treatment, requires further investigation.