Stent Maps — Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations

Abstract

Transcatheter aortic valve implantation (TAVI) is a minimally-invasive method for the treatment of aortic valve stenosis in patients with high surgical risk. Despite the success of TAVI, side effects such as paravalvular leakages can occur postoperatively. The goal of this project is to quantitatively analyze the co-occurrence of this complication and several potential risk factors such as stent shape after implantation, implantation height, amount and distribution of calcifications, and contact forces between stent and surrounding structure. In this paper, we present a two-dimensional visualization (stent maps), which allows (1) to comprehensively display all these aspects from CT data and mechanical simulation results and (2) to compare different datasets to identify patterns that are typical for adverse effects. The area of a stent map represents the surface area of the implanted stent - virtually straightened and uncoiled. Several properties of interest, like radial forces or stent compression, are displayed in this stent map in a heatmap-like fashion. Important anatomical landmarks and calcifications are plotted to show their spatial relation to the stent and possible correlations with the color-coded parameters. To provide comparability, the maps of different patient datasets are spatially adjusted according to a corresponding anatomical landmark. Also, stent maps summarizing the characteristics of different populations (e.g. with or without side effects) can be generated. Up to this point several interesting patterns have been observed with our technique, which remained hidden when examining the raw CT data or 3D visualizations of the same data. One example are obvious radial force maxima between the right and non-coronary valve leaflet occurring mainly in cases without leakages. These observations confirm the usefulness of our approach and give starting points for new hypotheses and further analyses. Because of its reduced dimensionality, the stent map data is an appropriate input for statistical group evaluation and machine learning methods.

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

“…Stent projections The aortic valve separates the left ventricle from the trunk of the aorta, i.e., it lies between the heart and the main arterial system. Born et al [BSR * 14] introduced a map‐based depiction to investigate side effects occurring after treatment of aortic valve stenosis using transcatheter aortic valve implantation (TAVI). TAVI is a minimally invasive treatment method in which the abnormal valve is displaced toward the aortic wall by an expanded stent containing a prosthetic aortic valve.…”

“…Based on numerical simulations, different risk factors were identified for paravalvular leakage such as stent deformation, radial attachment forces, or calcifications. To analyze the influence of these risk factors Born et al [BSR * 14] presented a 2D visualization (stent maps) of the expanded stent. As input the stent mesh including its centerline as well as mesh‐based representations of calcifications are needed.…”

“…Depending on which properties the final visualization should have, different 2D layouts are suitable. In simple cases, projection has been used to Create a 2D from a 3D tree structure [KFW * 01, TBB * 07, NGB * 09, BSR * 14]. The obvious flaws of this approach are possible self‐intersections that may lead to visual ambiguity and/or occlusion of data.…”

Mitral valve flattening and parameter mapping for patient-specific valve diagnosis

“…Flattened depictions have been proposed for the circulatory system, the colon, the brain and the bones. A 2D representation of aortic valve prostheses has been intro-duced as well [2]. Properties like stent compression were assessed after implantation in order to analyze complication co-occurrences.…”

Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data

“…Born et al. [BSRea14] present the 2D stent map for aortic valve stenosis analysis, where generalized cylindric coordinates are extracted from the vessel centerline to create a mapping of different stent parameters, e.g., radial force or compression level. In contrast to these approaches, we do not sample around the vessel's centerline but rather along the inner vessel wall.…”