Jared Vicory, Christian Herz, Ye Han, David Allemang, Maura Flynn, Alana Cianciulli, Hannah H Nam, Patricia Sabin, Andras Lasso, Matthew A Jolley, Beatriz Paniagua
{"title":"Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.","authors":"Jared Vicory, Christian Herz, Ye Han, David Allemang, Maura Flynn, Alana Cianciulli, Hannah H Nam, Patricia Sabin, Andras Lasso, Matthew A Jolley, Beatriz Paniagua","doi":"10.1007/978-3-031-23443-9_24","DOIUrl":null,"url":null,"abstract":"<p><p>Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Many HLHS patients develop tricuspid regurgitation and right ventricle enlargement which is associated with heart failure and death without surgical intervention on the valve. Understanding the connections between the geometry of the TV and its function remains extremely challenging and hinders TV repair planning. Traditional analysis methods rely on simple anatomical measures which do not capture information about valve geometry in detail. Recently, surface-based shape representations such as SPHARM-PDM have been shown to be useful for tasks such as discriminating between valves with normal or poor function. In this work we propose to use skeletal representations (s-reps), a more feature-rich geometric representation, for modeling the leaflets of the tricuspid valve. We propose an extension to previous s-rep fitting approaches to incorporate application-specific anatomical landmarks and population information to improve correspondence. We use several traditional statistical shape analysis techniques to evaluate the efficiency of this representation: using principal component analysis (PCA) we observe that it takes fewer modes of variation compared to boundary-based approaches to represent 90% of the population variation, while distance-weighted discrimination (DWD) shows that s-reps provide for more significant classification between valves with less regurgitation and those with more. These results show the power of using s-reps for modeling the relationship between structure and function of the tricuspid valve.</p>","PeriodicalId":74866,"journal":{"name":"Statistical atlases and computational models of the heart. STACOM (Workshop)","volume":"13593 ","pages":"258-268"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949511/pdf/nihms-1869944.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical atlases and computational models of the heart. STACOM (Workshop)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-031-23443-9_24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Many HLHS patients develop tricuspid regurgitation and right ventricle enlargement which is associated with heart failure and death without surgical intervention on the valve. Understanding the connections between the geometry of the TV and its function remains extremely challenging and hinders TV repair planning. Traditional analysis methods rely on simple anatomical measures which do not capture information about valve geometry in detail. Recently, surface-based shape representations such as SPHARM-PDM have been shown to be useful for tasks such as discriminating between valves with normal or poor function. In this work we propose to use skeletal representations (s-reps), a more feature-rich geometric representation, for modeling the leaflets of the tricuspid valve. We propose an extension to previous s-rep fitting approaches to incorporate application-specific anatomical landmarks and population information to improve correspondence. We use several traditional statistical shape analysis techniques to evaluate the efficiency of this representation: using principal component analysis (PCA) we observe that it takes fewer modes of variation compared to boundary-based approaches to represent 90% of the population variation, while distance-weighted discrimination (DWD) shows that s-reps provide for more significant classification between valves with less regurgitation and those with more. These results show the power of using s-reps for modeling the relationship between structure and function of the tricuspid valve.