Publications

Authors: Sebastian Schmelzle, Thomas van de Kamp, Michael Heethoff, Vincent Heuveline, Philipp Lösel, Jürgen Becker, Felix Beckmann, Frank Schluenzen, Jörg U. Hammel, Andreas Kopmann, Wolfgang Mexner, Matthias Vogelgesang, Nicholas T. Jerome, Oliver Betz, Rolf Beutel, Benjamin Wipfler, Alexander Blanke, Steffen Harzsch, Marie Hörnig, Tilo Baumbach

Date Published: 7th Sep 2017

Publication Type: InProceedings

DOI: 10.1117/12.2275959

Citation: Developments in X-Ray Tomography XI,p.24,SPIE

Created: 7th Sep 2019 at 10:54, Last updated: 5th Mar 2024 at 21:23

Towards Cognition-Guided Patient-Specific Numerical Simulation for Cardiac Surgery Assistance

Data Mining and Uncertainty Quantification

Abstract (Expand)

Motivation. Patient-specific, knowledge-based, holistic surgical treatment planning is of utmost importance when dealing with complex surgery. Surgeons need to account for all available medical patient … data, keep track of technical developments, and stay on top of current surgical expert knowledge to define a suitable surgical treatment strategy. There is a large potential for computer assistance, also, and in particular, regarding surgery simulation which gives surgeons the opportunity not only to plan but to simulate, too, some steps of an intervention and to forecast relevant surgical situations. Purpose. In this work, we particularly look at mitral valve reconstruction (MVR) surgery, which is to re-establish the functionality of an incompetent mitral valve (MV) through implantation of an artificial ring that reshapes the valvular morphology. We aim at supporting MVR by providing surgeons with biomechanical FEM-based MVR surgery simulations that enable them to assess the simulated behavior of the MV after an MVR. However, according to the above requirements, such surgery simulation is really beneficial to surgeons only if it is patient-specific, surgical expert knowledge-based, comprehensive in terms of the underlying model and the patient’s data, and if its setup and execution is fully automated and integrated into the surgical treatment workflow. Methods. This PhD work conducts research on simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. First, we derive a biomechanical MV/MVR model and develop an FEM-based MVR surgery simulation using the FEM software toolkit HiFlow3. Following, we outline the functionality and features of the Medical Simulation Markup Language (MSML) and how it simplifies the biomechanical modeling workflow. It is then detailed, how, by means of the MSML and a set of dedicated MVR simulation reprocessing operators, patient-individual medical data can comprehensively be analyzed and processed in order for the fully automated setup of MVR simulation scenarios. Finally, the presented work is integrated into the cognitive system architecture of the joint research project Cognition-Guided Surgery. We particularly look at its semantic knowledge and data infrastructure as well as at the setup of its cognitive software components, which eventually facilitate cognition-guidance and patient-specifity for the overall simulation-enhanced MVR assistance pipeline. Results and Discussion. We have proposed and implemented, for the first time, a prototypic system for simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. The overall system was evaluated in terms of functionality and performance. Through its cognitive, data-driven pipeline setup, medical patient data and surgical information is analyzed and processed comprehensively, efficiently and fully automatically, and the hence set-up simulation scenarios yield reliable, patient-specific MVR surgery simulation results. This indicates the system’s usability and applicability. The proposed work thus presents an important step towards a simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance, and can – once operative – be expected to significantly enhance MVR surgery. Concluding, we discuss possible further research contents and promising applications to build upon the presented work.

Editor:

Date Published: 8th Feb 2017

Publication Type: Doctoral Thesis

DOI: 10.11588/heidok.00022579

Citation:

Created: 10th May 2023 at 15:24, Last updated: 5th Mar 2024 at 21:25

heiMAP: Virtueller Kartentisch

Data Mining and Uncertainty Quantification

Abstract

Not specified

Authors: Martin Baumann, Maria Effinger, Dirk Eller, Vincent Heuveline, Christian Kempf, Lukas Loos, Leonhard Maylein, Jörg Peltzer, Michelle Pfeiffer, Veit Probst, others