Temporal views of flattened mitral valve geometries

• Verfasst von: Eulzer, Pepe [VerfasserIn]
• Verfasst von: Engelhardt, Sandy [VerfasserIn]
• Verfasst von: De Simone, Raffaele [VerfasserIn]
• Titel: Temporal views of flattened mitral valve geometries
• Verf.angabe: Pepe Eulzer, Sandy Engelhardt, Nils Lichtenberg, Raffaele de Simone, Kai Lawonn
• Jahr: 2020
• Umfang: 10 S.
• Teil: volume:26
• Teil: year:2020
• Teil: number:1
• Teil: pages:971-980
• Teil: extent:10
• Fussnoten: Date of publication: 19 August 2019 ; Gesehen am 03.02.2020
• Titel Quelle: Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on visualization and computer graphics
• Ort Quelle: New York, NY : IEEE, 1995
• Jahr Quelle: 2020
• Band/Heft Quelle: 26(2020), 1, Seite 971-980
• ISSN Quelle: 1941-0506
• DOI: doi:10.1109/TVCG.2019.2934337
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Data visualization
• Sach-SW: Geometry
• Sach-SW: medical visualization
• Sach-SW: Mitral valve
• Sach-SW: parameterization
• Sach-SW: quantification
• Sach-SW: Solid modeling
• Sach-SW: spatio-temporal visualization
• Sach-SW: Surgery
• Sach-SW: Three-dimensional displays
• Sach-SW: Two dimensional displays
• Sach-SW: Valves
• K10plus-PPN: 1689081813

Abstract

The mitral valve, one of the four valves in the human heart, controls the bloodflow between the left atrium and ventricle and may suffer from various pathologies. Malfunctioning valves can be treated by reconstructive surgeries, which have to be carefully planned and evaluated. While current research focuses on the modeling and segmentation of the valve, we base our work on existing segmentations of patient-specific mitral valves, that are also time-resolved ($3\mathrmD+\mathrmt$) over the cardiac cycle. The interpretation of the data can be ambiguous, due to the complex surface of the valve and multiple time steps. We therefore propose a software prototype to analyze such $3\mathrmD+\mathrmt$ data, by extracting pathophysiological parameters and presenting them via dimensionally reduced visualizations. For this, we rely on an existing algorithm to unroll the convoluted valve surface towards a flattened 2D representation. In this paper, we show that the $3\mathrmD+\mathrmt$ data can be transferred to 3D or 2D representations in a way that allows the domain expert to faithfully grasp important aspects of the cardiac cycle. In this course, we not only consider common pathophysiological parameters, but also introduce new observations that are derived from landmarks within the segmentation model. Our analysis techniques were developed in collaboration with domain experts and a survey showed that the insights have the potential to support mitral valve diagnosis and the comparison of the pre- and post-operative condition of a patient.