Morphological analysis of degenerated aortic valve bioprostheses by CT scan images segmentation

The aim of the study was to assess the feasibility of CT based 3D analysis of degenerated aortic bioprostheses to make easier their morphological assessment. This could be helpful during regular follow-up and for case selection, improved planning and mapping of valve-in-valve procedure. The challenge was represented by leaflets enhancement because of highly noised CT images. Contrast-enhanced ECG-gated CT scan was performed in patients with degenerated aortic bioprostheses before reoperation (in-vivo images). Different methods for noise reduction were tested and proposed. 3D reconstruction of bioprostheses components was achieved using stick based region segmentation methods. After reoperation, segmentation methods were applied to CT images of the explanted prostheses (ex-vivo images). Noise reduction obtained by improved stick filter showed best results in terms of signal to noise ratio comparing to anisotropic diffusion filters. All segmentation methods applied to in-vivo images allowed 3D bioprosthetic leaflets reconstruction. Explanted bioprostheses CT images were also processed and used as reference. Qualitative analysis revealed a good concordance between the in-vivo images and the bioprostheses alterations. Results from different methods were compared by means of volumetric criteria and discussed. ECG-gated CT images of aortic bioprostheses need a preprocessing to reduce noise and artifacts in order to enhance prosthetic leaflets. Stick region based segmentation seems to provide an interesting approach for the morphological characterization of degenerated bioprostheses.

Data and Resources

Additional Info

Field Value
Source https://theses.hal.science/tel-00768495
Author Ruggieri, Vito Giovanni
Maintainer CCSD
Last Updated May 29, 2026, 13:52 (UTC)
Created May 29, 2026, 13:52 (UTC)
Identifier tel-00768495
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire Traitement du Signal et de l'Image (LTSI) ; Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)
creator Ruggieri, Vito Giovanni
date 2012-12-07T00:00:00
harvest_object_id dbf8bab1-6a2a-4041-ba3c-3e6b3c7793ed
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-08-12T00:00:00
set_spec type:THESE