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Miscellaneous Information

Bio: I received my M.Sc. degrees from the Budapest University of Technology and Economics (BME) in Electrical Engineering (2005) and Biomedical Engineering (2007). In 2011 I completed my Ph.D. in Medical Image Processing at University College Dublin, with thesis entitled “Quantification and visualization of placental abnormalities” [1] [2] [3]. Currently my research interests are in delivering Surgical Planning / Image Guided Surgery applications. Since I joined CMIC I’ve been involved in the development of the NifTK platform, with special focus on image guided intervention (Nifty-IGI [6]) modules, where I led the development of NiftyLink [7] and NiftyGuide [8]. I am also contributing to the NiftyReg project and to the MITK platform. My current research interests lay in Surgical Planning, Image guided interventions, Real time image processing and visualisation, and GPU programming using OpenCL.

Research: My current work is focused on the Epilepsy-Navigator (EpiNav) project, which is collaboration between CMIC and our clinical partners at NHNN. The EpiNav project aims to deliver a real-time interactive planning solution for the implantation of sEEG electrodes into the brain and also to provide tools for resection planning and patient follow-up after the treatment. The main challenges are to fuse images from different imaging modalities to identify critical areas of brain function (connections and blood vessels), to automatically determine the optimal entry point on the skull surface from where the target areas can be reached without complications and furthermore to analyse the risk that is associated with each electrode trajectory. Performing these tasks in a real-time interactive manner allows the neurosurgeon to plan the best operative approach for inserting recording electrodes and for planning surgical resections in less time. The benefits to the patient are higher success rate and fewer complications.


Fig.1: Multi-modality brain map and risk visualisation for assisted SEEG electrode placement planning

On Figure 1 The semi-transparent part of the skull represent non-suitable points while the coloured patch shows the potential entry points with associated risk. Green – low risk, red – high risk. In this case the target point was picked at random. Two critical structures are displayed on this image: cyan - veins, red - arteries.


tarm traj7assisted change

Fig.2: Change in the planned trajectory due to he use of assisted planning

Figure 2 shows a change in the planned trajectory due to the use of multi-modality images and the assisted planning system. The arrow in green is the old path while the purple arrow is new path resulting from assisted planning. The increased distance to blood vessels can be observed.

1. Zombori, G., J. Ryan, and F. McAuliffe, Volume-based segmentation of the placenta in ultrasound imaging. Radiological Society of North America Chicago, 2009.
2. Zombori, G., J. Ryan, et al. Advanced noise reduction in placental ultrasound imaging using CPU and GPU: a comparative study. International Society for Optics and Photonics, 2010.
3. Moran, M., Zombori, G., et al., Computerised assessment of placental calcification post ultrasound–a novel new software tool. Ultrasound in Obstetrics & Gynecology, 2012.
4. Rodionov, R., Zombori, G., et al. "Feasibility of multimodal 3D neuroimaging to guide implantation of intracranial EEG electrodes." Epilepsy research 107.1 (2013): 91-100. 2013.
5. Zombori, G., et al. "A computer assisted planning system for the placement of sEEG electrodes in the treatment of epilepsy." In: (Proceedings) Information Processing in Computer-Assisted Interventions. Fukuoka, Japan, 2014 .