Why the study?
Existing methods for aortic valve calcification assessment do not provide accurate quantitative evaluation and repetitive analysis of calcium distribution and deposition.
Observational (n=50)
A novel semi-automated algorithm using CT imaging can successfully extract detailed quantitative morphometric, topological, and textural parameters of aortic valve calcification in severe aortic stenosis.
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.
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Wiktorowicz et al. (2019) conducted an observational in Severe aortic stenosis (n=50). Semi-automated CT image analysis algorithm was evaluated on Quantitative parameters for calcium assessment.
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