Abstract
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.
Keywords: Aortic stenosis, calcifications, computer modelling, computed tomography, quantification, calcium distribution.
[http://dx.doi.org/10.1038/nrdp.2016.6] [PMID: 27188578]
[http://dx.doi.org/10.1093/eurheartj/ehx391] [PMID: 29425605]
[http://dx.doi.org/10.5812/ircmj.16616]
[http://dx.doi.org/10.1016/j.ijcard.2017.07.020] [PMID: 29249440]
[http://dx.doi.org/10.5114/aic.2018.74359] [PMID: 29743908]
[http://dx.doi.org/10.1038/s41568-018-0016-5] [PMID: 29777175]
[http://dx.doi.org/10.1186/s13244-019-0764-0] [PMID: 31468205]
[http://dx.doi.org/10.1055/s-0039-1677945] [PMID: 31419831]
[http://dx.doi.org/10.15265/IY-2015-026] [PMID: 26293864]
[http://dx.doi.org/10.1093/eurheartj/ehz127] [PMID: 30977787]
[http://dx.doi.org/10.1097/SCS.0000000000005541] [PMID: 31188247]
[http://dx.doi.org/10.1088/1361-6560/aad316] [PMID: 29999495]
[http://dx.doi.org/10.1088/1361-6560/aaa4b1] [PMID: 29293469]
[http://dx.doi.org/10.1016/j.ab.2019.03.017] [PMID: 30930199]
[http://dx.doi.org/10.1016/j.jtbi.2019.03.011] [PMID: 30880183]
[http://dx.doi.org/10.1007/s00438-019-01570-y] [PMID: 31055655]
[http://dx.doi.org/10.1016/j.jtbi.2019.02.007] [PMID: 30768975]
[http://dx.doi.org/10.1016/j.ygeno.2019.02.006] [PMID: 30779939]
[http://dx.doi.org/10.1016/j.commatsci.2018.04.031]
[http://dx.doi.org/10.23736/S0026-4725.18.04793-X] [PMID: 30226030]
[http://dx.doi.org/10.1016/j.bone.2010.08.023] [PMID: 20817052]
[http://dx.doi.org/10.1016/j.amjmed.2016.10.005] [PMID: 27810479]
[http://dx.doi.org/10.1016/j.carrev.2019.05.024] [PMID: 31383557]
[http://dx.doi.org/10.1007/s00595-019-01848-z] [PMID: 31342159]
[http://dx.doi.org/10.1016/j.amjcard.2011.07.007] [PMID: 21855831]
[http://dx.doi.org/10.1093/icvts/ivu413] [PMID: 25487234]
[http://dx.doi.org/10.1016/j.ijcard.2009.01.021] [PMID: 19195722]
[http://dx.doi.org/10.1016/0735-1097(90)90282-T] [PMID: 2407762]
[http://dx.doi.org/10.1097/RCT.0000000000000480] [PMID: 27680414]
[http://dx.doi.org/10.1186/1476-7120-12-43] [PMID: 25352208]
[http://dx.doi.org/10.1042/bj1870829] [PMID: 7188428]
[http://dx.doi.org/10.1139/v81-107]
[http://dx.doi.org/10.1042/bj2220169] [PMID: 6477507]
[PMID: 2745429]
[PMID: 7681060]
[http://dx.doi.org/10.1016/0301-4622(90)80056-D] [PMID: 2183882]
[http://dx.doi.org/10.1016/j.ygeno.2019.05.027] [PMID: 31175975]