Nicolai Schoch

Surgical data science for next-generation interventions

Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.Lena Maier-Hein, Swaroop Vedula, Stefanie Speidel, Nassir Navab, Ron Kikinis, Adrian Park, Matthias Eisenmann, Hubertus Feussner, Germain Forestier, Stamatia Giannarou, Makoto Hashizume, Darko Katic, Hannes Kenngott, Michael Kranzfelder, Anand Malpani, Keno März, Thomas Neumuth, Nicolas Padoy, Carla Pugh, Nicolai Schoch, Danail Stoyanov, Russell Taylor, Martin Wagner, Gregory D. Hager, Pierre Jannin

Comprehensive patient-specific information preprocessing for cardiac surgery simulations.

PurposePatient-specific biomechanical simulations of the behavior of soft tissue gain importance in current surgery assistance systems as they can provide surgeons with valuable ancillary information for diagnosis and therapy. In this work, we aim at supporting minimally invasive mitral valve reconstruction (MVR) surgery by providing scenario setups for FEM-based soft tissue simulations, which simulate the behavior of the patient-individual mitral valve subject to natural forces during the cardiac cycle after an MVR. However, due to the complexity of these simulations and of their underlying mathematical models, it is difficult for non-engineers to sufficiently understand and adequately interpret all relevant modeling and simulation aspects. In particular, it is challenging to set up such simulations in automated preprocessing workflows such that they are both patient-specific and still maximally comprehensive with respect to the model.MethodsIn this paper, we address this issue and present a fully automated chain of preprocessing operators for setting up comprehensive, patient-specific biomechanical models on the basis of patient-individual medical data. These models are suitable for FEM-based MVR surgery simulation. The preprocessing methods are integrated into the framework of the Medical Simulation Markup Language and allow for automated information processing in a data-driven pipeline.ResultsWe constructed a workflow for holistic, patient-individual information preprocessing for MVR surgery simulations. In particular, we show how simulation preprocessing can be both fully automated and still patient-specific, when using a series of dedicated MVR data analytics operators. The outcome of our operator chain is visualized in order to help the surgeon understand the model setup.ConclusionWith this work, we expect to improve the usability of simulation-based MVR surgery assistance, through allowing for fully automated, patient-specific simulation setups. Combined visualization of the biomechanical model setup and of the corresponding surgery simulation results fosters the understandability and transparency of our assistance environment.

High Performance Computing for Cognition-Guided Cardiac Surgery: Soft Tissue Simulation for Mitral Valve Reconstruction in Knowledge-Based Surgery Assistance

Medical simulations play an increasingly important role in today’s clinical and surgical treatment processes. The scope of this work is the support of the surgical operation of a mitral valve reconstruction (MVR) by means of biomechanical simulations. Based on numerical simulation, the natural anatomical setting, the ring implantation and the valve closure are modelled and efficiently computed in order to provide surgeons during the operation with additional morphological and functional information. Our simulation is based on the Finite Element Method (FEM) and implemented using the open-source C++ FEM software HiFlow3. Integrating patient data and surgical expert knowledge, and making efficient use of High-Performance Computing (HPC) methods allows for obtaining valuable simulation results for surgery assistance in adequate times. In this work, we focus on the intelligent setup of the biomechanical model and the flexible interfaces of the HPC-based implementation of the resulting MVR simulation, thereby aiming at a cognition-guided, patient-specific integration into systems for surgery assistance.

Towards an open-source semantic data infrastructure for integrating clinical and scientific data in cognition-guided surgery

In the surgical domain, individual clinical experience, which is derived in large part from past clinical cases, plays an important role in the treatment decision process. Simultaneously the surgeon has to keep track of a large amount of clinical data, emerging from a number of heterogeneous systems during all phases of surgical treatment. This is complemented with the constantly growing knowledge derived from clinical studies and literature. To recall this vast amount of information at the right moment poses a growing challenge that should be supported by adequate technology. While many tools and projects aim at sharing or integrating data from various sources or even provide knowledge-based decision support - to our knowledge - no concept has been proposed that addresses the entire surgical pathway by accessing the entire information in order to provide context-aware cognitive assistance. Therefore a semantic representation and central storage of data and knowledge is a fundamental requirement. We present a semantic data infrastructure for integrating heterogeneous surgical data sources based on a common knowledge representation. A combination of the Extensible Neuroimaging Archive Toolkit (XNAT) with semantic web technologies, standardized interfaces and a common application platform enables applications to access and semantically annotate data, perform semantic reasoning and eventually create individual context-aware surgical assistance. The infrastructure meets the requirements of a cognitive surgical assistant system and has been successfully applied in various use cases. The system is based completely on free technologies and is available to the community as an open-source package.

Towards Cognition-Guided Patient-Specific FEM-Based Cardiac Surgery Simulation

Biomechanical surgery simulation can provide surgeons with useful ancillary information for intervention planning, diagnosis and therapy. The simulation therefore most importantly needs to be patient-specific, surgical knowledge-based and comprehensive in terms of the underlying simulation model and the patient’s data. Moreover, the simulation setup and execution should be largely automated and integrated into the surgical treatment workflow. However, this still rarely holds and simulation-based surgery support is not yet commonly established in the clinic. In this work, we address this problem in the context of cardiac surgery, and present the setup and results of a prototypic cognition-guided, patient-specific FEM-based cardiac surgery simulation system. We have designed a semantic data infrastructure and implemented cognitive software components that autonomously interact with the medical data via a common ontology. Using this setup, we anable the creation of knowledge-based, patient-specific surgery simulation scenarios for mitral valve reconstruction surgery, that are executed by means of the FEM simulation software HiFlow3. The obtained simulation results are provided to the surgeon in order to support surgical decision making.

Enhancing 4D PC-MRI in an aortic phantom considering numerical simulations

To date, cardiovascular surgery enables the treatment of a wide range of aortic pathologies. One of the current challenges in this field is given by the detection of high-risk patients for adverse aortic events, who should be treated electively. Reliable diagnostic parameters, which indicate the urge of treatment, have to be determined. Functional imaging by means of 4D phase contrast-magnetic resonance imaging (PC-MRI) enables the time-resolved measurement of blood flow velocity in 3D. Applied to aortic phantoms, three dimensional blood flow properties and their relation to adverse dynamics can be investigated in vitro. Emerging ”in silico” methods of numerical simulation can supplement these measurements in computing additional information on crucial parameters. We propose a framework that complements 4D PC-MRI imaging by means of numerical simulation based on the Finite Element Method (FEM). The framework is developed on the basis of a prototypic aortic phantom and validated by 4D PC-MRI measurements of the phantom. Based on physical principles of biomechanics, the derived simulation depicts aortic blood flow properties and characteristics. The framework might help identifying factors that induce aortic pathologies such as aortic dilatation or aortic dissection. Alarming thresholds of parameters such as wall shear stress distribution can be evaluated. The combined techniques of 4D PC-MRI and numerical simulation can be used as complementary tools for risk-stratification of aortic pathology.