Computational anatomy as a driver of understanding structural and functional cardiac remodeling
We present a statistical shape analysis framework to identify cardiac shape remodelling while accounting for individual´s natural variability and apply it in two clinical applications: comparing triathletes with controls, and comparing individuals who were born small-for-their-gestational-age (SGA)...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/668213 |
| Acceso en línea: | http://hdl.handle.net/10803/668213 |
| Access Level: | acceso abierto |
| Palabra clave: | Computational anatomy Statistical shape analysis Medical image understanding Cardiac remodelling Anatomía computacional Análisis estadístico de forma Comprensión de imágenes medicas Remodelado cardiaco 62 |
| Sumario: | We present a statistical shape analysis framework to identify cardiac shape remodelling while accounting for individual´s natural variability and apply it in two clinical applications: comparing triathletes with controls, and comparing individuals who were born small-for-their-gestational-age (SGA) and controls. We were able to identify the shape remodelling due to the practice of endurance sport: it consisted a dilation of the left ventricle and an increase of the left ventricular myocardial mass. In the right ventricle (RV), the increase of volume was concentrated in the outflow. This changes in shape correlated with a better performance during exercise. In SGA, we found subtle differences in the RV that correlated with worse performance during exercise. These differences were bigger when SGA condition was combined with cardiovascular risk factors: smoking and overweight. Finally, we present a geometry processing technique for parcellating the RV cavity in 3 subvolumes for regional analysis without point-to-point correspondence. |
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