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.