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Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule
Agata Wiktorowicz1, Adrian Wit2, Artur Dziewierz1
12nd Department of Cardiology, Institute of Cardiology, Jagiellonian University Medical College, 31-501 Kopernika St. 17, Krakow, Poland.
Insights
This study introduces a new quantitative tool for analyzing aortic valve calcifications (AVC) in patients with aortic valve stenosis (AS). The tool provides detailed pattern analysis and 3D models, improving calcium assessment for better treatment insights.
Area of Science:
- Cardiovascular Imaging
- Medical Physics
- Biomedical Engineering
Background:
- Aortic valve calcifications (AVC) progression is linked to aortic valve stenosis (AS) severity, influencing treatment and outcomes.
- Current AVC assessment methods lack accurate quantitative evaluation of calcium distribution and deposition.
- Accurate, repeatable AVC analysis is crucial for understanding AS progression.
Purpose of the Study:
- To develop a reliable tool for detailed AVC pattern analysis using quantitative parameters.
- To enable accurate and repeatable assessment of calcium in degenerated aortic valves.
- To provide novel insights into the anatomy of stenotic aortic valves.
Main Methods:
- Analysis of computed tomography (CT) scans from fifty patients with severe AS.
- Utilized dedicated software (MATLAB, ImageJ with BoneJ plugin) and a self-developed algorithm.
- Employed semi-automated and repeatable methods for parameter derivation from CT images.
Main Results:
- A unique set of parameters describing AVC was identified.
- 3D AVC models with color-coded calcium layer thickness were generated.
- Parameters were categorized into morphometric, topological, and textural types.
Conclusions:
- Quantitative parameters for assessing degenerated aortic valves were successfully obtained.
- The defined parameters may offer crucial information about stenotic aortic valve anatomy.
- Further research is needed to determine if these parameters can predict long-term treatment outcomes.
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.
