I am a PhD student working with the Computer Vision Laboratory at ETH Zurich (Swiss Federal Institute of Technology). My supervisor is Prof. Gabor Szekely. Previously, I studied Computational Visualistics at the Otto-von-Guericke University in Magdeburg, where I received a Bakkalaureat (B.Sc.) in 2009 followed by a Diplom-Ingeneur (M.Eng.) degree in 2010. During my studies I collaborated intensively with the CSIRO funded Australian E-Health Research Centre between 2007 and 2009 to improve rendering and interaction with real-time endoscopic training simulations. I further assisted the Fraunhofer IFF Institute in medical rendering and visualization questions in 2008. I formerly worked as a software engineer for Tarakos GmbH between 2006 and 2009, as well as Siemens Corporate Research in 2010 and 2011 to develop medical visualization products in various imaging modalities.
My research interests span across Computer Graphics, Computer Vision and Biomedical Engineering. In particular, I focus on medical simulation and visualization related to minimally invasive surgery.
Transcatheter aortic valve implantation (TAVI) is a minimally invasive off-pump procedure to replace diseased aortic heart valves. Known complications include paravalvular leaks, atrioventricular blocks, coronary obstruction and annular rupture. Careful procedure planning including appropriate stent selection and sizing are crucial. Few patient-speciﬁc geometric parameters, like annular diameters, annular perimeter and measurement of the distance to the coronary ostia, are currently used within this process. Biomechanical simulation allows the consideration of extracted anatomy and material parameters for the intervention, which may improve planning and execution phases. We present a simulation workﬂow using a fully segmented aortic root anatomy, which was extracted from pre-operative CT-scan data and apply individual material models and parameters to predict the procedure outcome. Our results indicate the high relevance of calciﬁcation location and size for intervention planning, which are not sufﬁciently considered at this time. Our analysis can further provide guidance for accurate, patient-speciﬁc device positioning and future adaptations to stent design.
Anatomy related heterogeneous discontinuities in material properties strongly inﬂuence human soft tissue behavior under mechanical load. We present a parametric model of the aortic root, which deﬁnes multi-material surface regions for advanced tissue property control and supports patient-speciﬁc model reﬁnement. Our results strongly indicate improvements for in-silico clinical application experiments using multi-material simulation.
Robust and reliable validation of stent designs and materials is an essential aspect in the development and improvement of these medical devices. It is essential to perform a variety of experiments at an early stage of this process to deﬁne suitable material requirements and stent geometry. We present a rapid prototyping framework for silicone-based vessel phantoms that allows the use of a variety of synthetic material properties for ﬂexible validation of simulated stent deformation. Finite element analysis results are compared to mechanical response tests performed under deterministic conditions. A validation work-ﬂow using optical tracking of stent deformation allows the comparison of simulated and mechanically obtained data. The results indicate that mechanical response to compression in artiﬁcial vessels can be analysed and validated across deﬁned tissue properties within the presented framework.
There is a need for identifying quantitative imaging (e.g. MRI) signatures for prostate cancer (CaP), so that computer-aided diagnostic methods can be trained to detect disease extent in vivo. Determining CaP extent on in vivo MRI is difficult to do; however, with the availability of ex vivo surgical whole mount histological sections (WMHS) for CaP patients undergoing radical prostatectomy, co-registration methods can be applied to align and map disease extent onto pre-operative MR imaging from the post-operative histology. Yet obtaining digitized images of WHMS for co-registration with the pre-operative MRI is cumbersome since (a) most digital slide scanners are unable to accommodate the entire section, and (b) significant technical expertise is required for whole mount slide preparation. Consequently, most centers opt to construct quartered sections of each histology slice. Prior to co-registration with MRI, however, these quartered sections need to be digitally stitched together to reconstitute a digital, pseudo WMHS. Histostitcher© is an interactive software program that uses semi-automatic registration tools to digitally stitch quartered sections into pseudo WMHS. Histostitcher© was originally developed using the GUI tools provided by the Matlab programming interface, but the clinical use was limited due to the inefficiency of the interface. The limitations of the Matlab based GUI include (a) an inability to edit the fiducials, (b) the rendering being extremely slow, and (c) lack of interactive and rapid visualization tools. In this work, Histostitcher© has been integrated into the eXtensible Imaging Platform (XIP™) framework (a set of libraries containing functionalities for analyzing and visualizing medical image data). XIP™ lends the stitching tool much greater flexibility and functionality by (a) allowing interactive and seamless navigation through the full resolution histology images, (b) the ability to easily add, edit, or remove fiducials and annotations in order to register the quadrants and map the disease extent. In this work, we showcase examples of digital stitching of quartered histological sections into pseudo-WHMS using Histostitcher© via the new XIP™ interface. This tool will be particularly useful in clinical trials and large cohort studies where a quick, interactive way of digitally reconstructing pseudo WMHS is required.
Virtual Colonoscopy is an important procedure for screening and detecting colorectal cancer. It increases patient comfort and reduces risks compared to optical colonoscopy. Oral contrast is used to emphasize the soft tissue border and avoid the need for physical colon cleansing. In order to ensure a reliable diagnosis, it is currently necessary to remove the fecal tagging in a time consuming pre-processing step. As the result can include artifacts and may effect polyp size, this paper proposes a novel technique that allows realistic visualization of the surface boundary based on unmodiﬁed CT images. A combined iso-surface reconstruction and direct volume rendering approach is developed to handle partial volume artifacts efﬁciently and allow on-the-ﬂy surface reconstruction. The algorithm supports real-time analysis of detected surfaces and can differentiate material transitions between air, soft tissue and ﬂuid. The surface-based rendering furthermore allows photo-realistic visualization through screen space shading to support procedure planning and interactive training.
Colonoscopy is considered the gold standard for detection and removal of precancerous polyps in the colon. Being a difficult procedure to master, exposure to a large variety of patient and pathology scenarios is crucial for gastroenterologists' training. Currently, most training is done on patients under supervision of experienced gastroenterologists. Being able to undertake a majority of training on simulators would greatly reduce patient risk and discomfort. A next generation colonoscopy simulator is currently under development, which aims to address the shortfalls of existing simulators. The simulator consists of a computer simulation of the colonoscope camera view and a haptic device that allows insertion of an instrumented colonoscope to drive the simulation and provide force feedback to the user. The simulation combines physically accurate models of the colonoscope, colon and surrounding tissues and organs with photorealistic visualization. It also includes the capability to generate randomized case scenarios where complexity of the colon physiology, pathology and environmental factors, such as colon preparation, can be tailored to suit training requirements. The long term goal is to provide a metrics based training and skill evaluation system that is not only useful for trainee instruction but can be leveraged for skills maintenance and eventual certification.
Australia has the second highest incidence of colorectal cancer in the world, with less than 40% early detection. Screening programs are being phased in slowly as positives usually require a colonoscopy with limited resources available. Colonoscopy is a difficult procedure to master with hundreds of cases required to reach an expert skill level. Training currently happens on real patients with increased risk and cost over patient-free simulation based training. Existing simulators rate poorly for realism and complexity and are under-utilised. Development of a high fidelity, portable simulator has the potential to significantly reduce cost and risk of training and provide a platform for future certification.
Simulation of surgical procedures requires visualisation of realistic geometric models of organs generated from 3D patient images. Signiﬁcant improvements in visual realism can be obtained through appropriate surface texturing and shading. In this paper we present a novel technique for continuous surface parameterisation of colonic geometric models utilising surface voxel to centreline correspondences derived from Laplace’s equation. Our technique creates continuous texture coordinates which exhibit minimal distortion and which enable us to use advanced texturing and shading techniques. Experimental results from phantom volumes and real cases are detailed.
In the field of biomedical simulation, material parameter estimation is an important aspect in building accurate patient specific models. These parameters are derived from non-invasive, commonly imaging-based techniques, which allow the specification of mechanical elasticity models.
Aortic valve replacement is commonly performed on patients showing severe calcifications around the leaflets preventing backflow into the left ventricle. TAVI allows the delivery of a porcine aortic valve using a stent and therefore avoiding the need of open heart surgery. Segmentation (and deformable registration) of calcifications found in CT scans acquired pre- and post-interventional are of great interest to procedure planning and guidance.
Automated generation of object molds essentially only requires inversion of geometry. For solid objects it is therefore a trivial problem. However, when trying to generate object molds for hollow, thin-walled, non-manifold objects, the complexity increases rapidly, as the mold-parts have to allow continuous distribution of infused liquid as well as its removal after curing.
Surgical Simulator for Medical Training, CSIRO, Brisbane (Australia)
Colonoscopy Simulation, AEHRC, Brisbane (Australia)
Siemens Corporate Research - Imaging & Visualization, Princeton (USA)
Visualization, Otto-von-Guericke University, Magdeburg (Germany)
Computer Vision Laboratory Sternwartstrasse 7 CH-8092 Zürich Switzerland
Tel: +41 44 63 27829 Fax: +41 44 63 21199 E-Mail: russc [at] ethz . ch Web: http://www.vision.ee.ethz.ch