CT-scan images preprocessing and segmentation to improve bioprosthesis leaflets morphological analysis

Abstract

The visualization of bioprosthesis leaflet morphology might help to better understand the underlying mechanism of dysfunction in degenerated aortic bioprosthesis. Because today such visualization of bioprosthesis leaflet morphology is intricate to impossible with other imaging techniques, we hypothesized that the processing of multi-detector CT images would allow better visualization of the prosthetic valve leaflets after biological aortic valve replacement. The purpose of our study was to prospectively evaluate patients with a degenerated aortic bioprosthesis, waiting for reoperation, by using 64-slice CT to evaluate prosthetic leaflets morphology. A semi-automatic segmentation of pre-operative tomodensitometric images was conducted, using 2 different implementations of the region growing algorithm. Here we report all segmentation steps (selection of the region of interest, filtering, segmentation). Studied degenerated aortic bioprostheses were represented by two Carpentier-Edwards Supra Annular Valve (porcine leaflets), one Edwards Perimount (pericardial leaflets) and one Medtronic Mosaic (porcine leaflets). Both segmentation methods (Isotropic Region Growing and Stick Region Growing) allowed a semi-automatic segmentation with 3D reconstruction of all bioprosthetic components (stent, leaflets, degeneration/calcifications). Explanted bioprosthesis CT images were also processed and used as reference. Segmentation results were compared by means of quantitative criteria. Semi-automatic segmentation using region growing algorithm seems to provide an interesting approach for the morphological characterization of degenerated aortic bioprostheses. We believe that in the next future CT scan images segmentation may play an important role to better understand the mechanism of dysfunction in failing aortic bioprostheses. Moreover, bioprostheses 3D reconstructions could be integrated into preoperative planning tools to optimize valve-in-valve procedure.

Computer-Assisted Transcatheter Heart Valve Implantation in Valve-in-Valve Procedures

“…24 As we have underlined, ViV procedures present the optimal conditions for the development of an image-based assistance, given the propensity of surgical heart valve to preprocedural elaboration of CT data and segmentation. 25 Stented surgical heart valves present radio-opaque markers, 12 follow the constant motion of the heart, and present little or no deformation during the heart cycle.…”

“…The availability and reliability of ViV are currently considered in the debate about the paradigm shift of prosthesis preference 25 (mechanical valves vs bioprostheses). Within this framework, we believe that the choice of the bioprosthesis at surgical implantation should take into consideration the potential future need for VIV procedure 26,27 and the patients' clinical characteristics.…”

Initial experience with cinematic rendering for chest cardiovascular imaging

“…Both surgical and endovascular approaches are utilized for cardiac valve replacement, and complications of artificial valves may best be evaluated using 3D post-processing techniques. 11 Although the data are not yet available comparing prosthetic valve evaluation with CR to traditional VR, the highly detailed and life-like images produced through CR methodology may improve the diagnosis of subtle complications such as early bioprosthetic valve degeneration or the formation of small thrombi (Figure 4).…”