Research in Medical Imaging
Vision is the most important sense humans have. Computer vision tries to endow machines with similar capabilities to interpret visual input, and to act upon it. With the Medical Imaging team, we work on mainly four aspects:
- Virtual Reality in Medicine
- Medical Image Analysis
- Biomedical Simulations
- Biomedical Microsystems
To learn more about the individual topics and to show a list of related projects, please select a topic below:
Benefits from better understanding of physiological mechanisms involved in development of social diseases like vascular malfunctions or cancer can hardly be exaggerated. The goal of the group is to apply the computational finite element techniques developed and validated in material science over the last decades to model selected aspects in such systems. The underlying atomic phenomena in focus include solid mechanics, fluid flow, chemical signaling, heat transfer - and their interplay. The Biomedical Simulations group is led by Dr. Sven Hirsch.
ReCoVa – Reconstruction of cerebrovascular networks
The general aim of the project is to reconstruct complete cortical cerebrovascular networks on the basis of partially incomplete, high-resolution tomographic data.
We are developing intelligent algorithms based on known morphological features of the cerebral vasculature. Ultimately we will generate a full vessel network based on insufficient data. Even if we focus on optimizing the results of an incomplete and error-prone measurement, the developed technology will in the future also support enhancing low resolution-images that cannot resolve smaller vessels, by filling up the missing parts of the vascular network based on a tissue footprint, either explicitly or in a statistical fashion. This would facilitate a reduction of imaging efforts substantially by moving to lower resolution imaging with the gain of a larger field of view e.g. of µCT instead of synchrotron radiation based tomography, or even extending the application domain to in vivo investigations relying on macroscopic imaging modalities like CT or MRI. Secondly, the methodology may later be used to combine individual patches of a sample to a larger volume by filling up the voids in a consistent fashion, even allowing the integration of image information extracted from different modalities.
Quantitative magnetic force microscopy (qMFM) is a technique to back out the magnetic structure of a sample from a measurement with MFM. The project objective is implement a reconstruction technique to evaluate the data and to package these tools in software that effectively hides complexity from the user.
In the very few cases where qMFM could be implemented so far, it provided otherwise inaccessible information on the magnetization mechanisms in thin films due to its high resolution, insensitivity to applied external fields, and calibrations validated independently of probe models. Here we seek to develop measurement techniques for reproducibly acquiring MFM data that can be evaluated quantitatively, to design numerical tools for this evaluation, and to package these tools in software that effectively hides complexity from the user.
We contribute algorithms for finding a correct and optimal calibration function for the implementation of qMFM, and performing the necessary image data deconvolutions. This involves taking into account priors such as e.g. knowledge of the negligible effect, far from the tip center, of high spatial frequency components. As a result of this work algorithms will be available to obtain optimal calibration functions from data obtained using highly accurate, automated measurement routines. Finally, our strategy for disseminating the knowledge obtained in this project is the establishment of web-based a framework for the effective utilization of these tools, including visualization capability and comprehensive tutorials.
Biomechanical Simulation of Transcatheter Aortic Valve Implantation
We investigate the biomechanical mechanisms behind Transcatheter Aortic Valve Implantation (TAVI) to reduce the risk assiciated with the intervention.
Transcatheter Aortic Valve Implantation (TAVI) has been established as the new standard treatment for high-risk patients with severe aortic valve stenosis. Thereby, a replacement valve consisting of three soft tissue leaflets fixed inside a metallic, foldable support frame (stent), is positioned and unfolded inside the diseased aortic valve under beating heart conditions. The native valve leaflets are dislocated by the unfolding stent and pressed against the vascular wall. The method is being applied worldwide, efficacy and safety have been proven superior to standard treatment of inoperable patients and at least non-inferior in patients with increased operative risk. Nevertheless, the technique is linked to a number of complications. Some are rather rare, but lethal, such as ruptures of vascular of ventricular tissue or the partial or complete obstruction of the coronary arteries by the native leaflets or by a misplaced stent. More frequent, postoperative arrhythmia or bradycardia as well as paravalvular leaks along the perimeter of the stented valve are witnessed. While not directly life-threatening, these complications will require additional correction (e.g. pacemaker) and are known to result in a worse long-term perspective. Our interdisciplinary research group at University Hospital Zürich and ETH Zürich is set out to investigate the biomechanical mechanisms behind these complications with the long-term aim to derive strategies for a patient-specific optimization of the planning as well as the conduction of TAVI.
University Hospital Zurich - Division of Cardiovascular Surgery (Prof. Falk)
Flow Energy and Vessel Biology of the Cerebral Aneurysm
The goal of the proposed project is to continue developing and validating a numerical model that simulates the principal mechanisms involved in the formation and the endovascular treatment of aneurysms. Validation requires use of adequate methodologies to compare and match virtual with real world observations. Ex-vivo and in-vitro models, and where unavoidable, succinct use of animal models are needed to obtain highly valued biological validation information.
Energy of cerebral blood flow may lead in approx. 2-4% of people to vessel wall fatigue with creation of an intracranial aneurysm that may carry a risk of rupture. Vessel and blood biology are both critically influenced by shear, a measure of friction between moving elements of blood and the endothelial lining of the vessel wall. Clot formation is also closely involved in endovascular treatment with good and bad effects. Computational study of wall remodeling effects could include areas and quality of clot formation, that are understood to critically drive the biology of the vessel wall by inflammation and degradation mechanisms.As in any other field of biomechanical engineering, numerical simulations offer the possibilities to test a variety of scenarios and mechanisms allowing for forecasting disease evolution and for treatment planning. Since current medical imaging can provide exact 3D replicas of concerned patients, we wish to understand critical mechanisms that would help to predict rupture risk in unruptured aneurysms and in case of treatment need, to choose the optimal implant with the best chance to produce healing, i.e. reverse remodeling of the vessel wall. Once the interplay of clot formation and suitable endovascular implants is better understood, predictive techniques will support patientspecific, personalized assessment to increase the saftey of the intervention.
Image-based validation of computational models for vascularized solid tumour growth
In tumor modeling we focus on developing new approaches for a comprehensive validation of predictive models of tumor growth using image information.
While both, model development and tumor imaging, have seen rapid progress in recent years, rather little progress has been made so far towards assimilating experimental data into tumor models. We address the full integration of measurement and simulation by focusing on the initialization problem and the development of new methods for a fast image-based inference by combining generative models of disease progression with efficient techniques from machine learning. We rely primarily on functional imaging data from of small animal models for acquiring multimodal time series of tumor progression in vivo.
Modeling Tumor Growth and Angiogenesis
Objective: We are investigating carefully selected aspects of tumor growth and neovascularization, which is of major interest both for clinical care and basic biological research. Modeling the process of tumor development can not only improve our insight into the underlying mechanisms but will also contribute to the development of novel treatment strategies like optimizing dose delivery during radiation therapy or systematic selection of anti-angiogenic drugs depending on tumor phenotype.
Our goal is to develop a comprehensive simulation package, allowing to simulate the underlying physical and biochemical processes, covering mutual relationships between mechanical and biochemical stimuli, transport phenomena like flow, diffusive transport or active cell migration or possibly even gene expression, resulting in tissue formation, deformation or removal. The major aspects, which need to be accounted for, are listed below:
- Production, transport and absorption of
- molecules (nutrients, growth factors and inhibitors):(chemotaxis,chemokinesis)
- cells (haptotaxis,haptokinesis)
- Vessel growth (endothelial cell transport, growth, death)
- Tissue growth (tumor cell transport, growth, death)
- Blood flow in vessels
- Accurate biomechanical tissue model
Major emphasis will be given to the interaction and interrelation of these processes, in contrast to the currently existing approaches which usually only deal with one of these issues in an isolated fashion.
Partners: ETH Zürich, Animal Imaging Center
Cardiovascular diseases are one of the major causes of human morbidity and mortality, calling for novel and more efficient methods of treatment. Non-invasive diagnostic procedures are therefore of particular importance in managing the diseases. Among the different diagnostic modalities available today, magnetic resonance imaging (MRI) stands out as a potential single non-invasive tool for a comprehensive cardiac examination, allowing the assessment of vessel morphology and the quantification of blood flow through larger vessels. Despite its great potential, MRI exhibits limitations in particular when information with very high spatial and/or temporal resolution is needed. It has also been shown that inter-individual differences in vessel geometry have considerable impact on the flow regime. Accordingly, the applicability of generalized models and experimental setups for studying human blood flow is limited. Due to these restrictions it cannot be expected, that MR imaging can directly provide all data necessary for diagnosis and interventional planning. It has to be combined with advanced image analysis and simulation tools, which allow to extract and to reliably extrapolate data from the MR measurements, leading to a dense and detailed spatio- temporal description of the vascular flow. The objective of this project is therefore to create the fundaments of a comprehensive computational framework unifying appropriate MR imaging methods, image analysis, model building and visualization algorithms, and simulation techniques based on computational fluid dynamics (CFD), providing a highly efficient tool for the clinicians to select the best therapy for cardiovascular malfunctions.
Partners: Indian Institute of Technology, Kanpur, India