Research in Medical Imaging

ReCoVa – Reconstruction of cerebrovascular networks

Objective:

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.

Participants: Matthias Schneider Sven Hirsch Gábor Székely

Partners:

NCCR Co-Me
Functional Imaging and Neurovascular Coupling, University of Zurich (Prof. Weber)
Institute for Fluid Dynamics, ETH Zurich (Prof. Jenny)

qMFM

Objective:

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.

Participants: Zoé Goey Sven Hirsch Gábor Székely

Partners:

EMPA,  Nanoscan

Biomechanical Simulation of Transcatheter Aortic Valve Implantation

Objective:

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.

Participants: Christoph Russ Sven Hirsch Michael Gessat Gábor Székely

Partners:

University Hospital Zurich - Division of Cardiovascular Surgery (Prof. Falk)

Institute of Mechanical Systems, ETH (Profs. Kuhl & Mazza)

FUSIMO - Patient specific modelling and simulation of focused ultrasound in moving organs

Objective:

The aim of FUSIMO is to develop, implement and validate a multi-level model for moving abdominal organs for use in magnetic resonance-guided focused ultrasound surgery.

In recent years, High-Intensity Focused Ultrasound and Focused Ultrasound (FUS) have become frequent tools for non-invasive benign tumour therapy. However, treating tumors with focused ultrasound is challenging in terms of reliable therapy planning, monitoring and outcome prediction especially in moving organs with a complex blood supply. It is important to understand that the processes involved in FUS therapy are multi-level ranging from organ morphology, perfusion and motion, down to microscopic and cellular level. The relation within and between these levels is not well understood.

In this project, a multi-level model for moving abdominal organs for use in magnetic resonance-guided focused ultrasound surgery will be developed. The overall model will consist of several sub-models, which interact and describe aspects in a hierarchical manner. The integrated model will consist of:
- Abdominal organ model to simulate motion and the influence on ultrasound application
- Target organ/tumour model to capture organ/tumour physiology, and organ/tumour reaction to therapy
- Microscopic tissue model to simulate direct heat ablation, model energy distribution, tissue heating and cooling
- Model to evaluate first steps to simulate drug delivery, microbubble distribution and dynamics

Participants: Golnoosh Samei Christine Tanner Gábor Székely

Partners:

Frauenhofer MEVIS, Germany,
University of Dundee, United Kingdom,
Technische Universiteit Delft, Netherlands
Stiftelsen SINTEF, Norway
Medical Imaging Research Institute, Germany
IBSmm Engineering spol. s r. o., Czech Republic
INSIGHTEC LTD, Israel
GEMS PET SYSTEMS AB, Sweden
UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA, Italy
Eidgenössische Technische Hochschule Zürich, Switzerland
Fundatis Medis, Romania

DDMOAR

Objective:

Data-Driven Multimodal Object Acquisition and Rendering

The target of this project is to build a general framework for multimodal data-driven acquisition and rendering. The ultimate goal is to visually as well as haptically capture an object during unconstrained interaction and manipulation for subsequent display. 

Participants: Anatolii Sianov Matthias Harders

Partners:

Eidgenössische Technische Hochschule Zürich, Switzerland

Rapid PRO

Objective:

An emerging trend in surgical simulation is the demand for custom systems by medical device manufacturers. This requires surgical simulator companies to provide highly specialized, tailored simulations, at low production numbers, in limited time.

Therefore, this project aims at creating a framework for rapid prototyping of customized systems. The starting point will be a generic simulator core. Various options to quickly extend the latter to specific needs will be addressed, including data-driven simulation methods.

Participants: Michael Emmersberger Basil Fierz Martin Seiler Jonas Spillmann Matthias Harders

Partners:

VirtaMed AG, Switzerland
Eidgenössische Technische Hochschule Zürich, Switzerland

Weakly Supervised Segmentation for Building Statistical Models

Objective:

The goal of this project is to investigate and develop unsupervised or minimally supervised techniques for the segmentation of the anatomy (in particular, bone structures).

Statistical models of variability in anatomical structures are essential for a wide range of medical applications, e.g., to reconstruct missing information in implant design or help localize bones for radiotherapy planning. Segmentation of the anatomy is a necessary step towards such robust statistical shape and intensity modeling. A major obstacle in generating such statistical models is the need for a sufficiently large amount of correctly segmented medical data, from which the statistical model can be derived. Besides the purposes of active shape modelling based segmentation, it is beneficial to develop prior-less or weakly-supervised techniques to prepare the initial data used for modelling, and to deal with cases not well described by the statistical shape model (e.g. trauma, pathology).

Conventional segmentation methods for such large sets on segmentation data require either a significant amount of manual, interactive effort of the medical personnel or a statistical shape model of the bone in question, and sometimes a combination of both. The objective of this project is to develop weakly supervised methods for segmentation for subsequent automatic generation of statistical models. To this end, we are utilizing single atlas methods to provide semantic context in a general segmentation framework. We are particularly interested in bone segmentation to be utilized in the generation of a statistical skeleton atlas of the body.

Participants: Tobias Gass Orçun Göksel Gábor Székely

Flow Energy and Vessel Biology of the Cerebral Aneurysm

Objective:

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.

Participants: Julien Egger Sven Hirsch Gábor Székely

Partners:

Neuroradiology, Hirslanden Clinic (Prof. Rüfenacht)
CABMM, University of Zurich
IT’IS foundation
Gyrotools

Aaron Fogelson, University Utah

Image-based validation of computational models for vascularized solid tumour growth

Objective:

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.

Participants: Björn Menze Sven Hirsch Gábor Székely

Partners:

NCCR Co-Me Animal Imaging Center, ETH Zurich (Prof. Rudin )

BEAMING

Objective:

Today, in spite of advanced video conferencing, shared virtual environments, and gaming environments such as Second Life, it is still simply much more efficient to physically travel to remote location for business, scientific or family meetings—even if at a huge environmental, energetic and opportunity cost.

The science and technology developed in BEAMING will for the first time give people a real sense of physically being in a remote location with other people, and vice versa—without actually traveling.

BEAMING is a four year FP7 EU collaborative project which started on Jan 1st 2010.

The main task of the Virtual Reality in Medicine group is the development of methods for visual and haptic capture of objects, as well as the remote display of these acquired digital copies.

Project-Website: http://.www.beaming-eu.org

Participants: Seokhee Jeon Matthias Harders

Partners:

Starlab Barcelona, Spain
Universitat de Barcelona, Spain
University College London, United Kingdom
Eidgenössische Technische Hochschule Zürich, Switzerland
Scuola Superiore di Studi Universitari e di Perfezionamento Sant’ Anna, Italy
Technion - Israel Institute of Technology, Israel
Interdisciplinary Center Herzliya, Israel
IBM Haifa Research Lab, Israel
Consorci Institut d’Investigacions Biomediques August Pi i Sunyer, Spain
Aalborg Universitet, Denmark
Technische Universitaet Muenchen, Germany

Virtual Patient Models

Objective:

A key element of training with virtual reality surgical simulators is the definition of the
simulated patients. This step typically includes the generation of geometric models of
healthy and pathological anatomy, organ textures, vessel structures, and the determination
of tissue deformation parameters. The target of this project is to extend and modify
previously developed generic methods to patient-specific scenarios. Moreover, the
various modules will be combined into a user-friendly, complete training scene generation
tool. In this context, aspects of optimal human-computer interaction, workflow, and
usability will be addressed.

A key element of training with virtual reality surgical simulators is the definition of the
simulated patients. This step typically includes the generation of geometric models of
healthy and pathological anatomy, organ textures, vessel structures, and the determination
of tissue deformation parameters. The target of this project is to extend and modify
previously developed generic methods to patient-specific scenarios. Moreover, the
various modules will be combined into a user-friendly, complete training scene generation
tool. In this context, aspects of optimal human-computer interaction, workflow, and
usability will be addressed.

Participants: Thomas Wolf Michael Emmersberger Matthias Harders

Partners:

VirtaMed AG, Switzerland
Eidgenössische Technische Hochschule Zürich, Switzerland

Patient-Specific Model Generation for Surgical Training Simulation

Objective:

The target of this project is to extend and modify previously developed generic methods to patient-specific scenarios. Moreover, the various modules will be combined into a user-friendly, complete training scene generation tool. In this context, aspects of optimal human-computer interaction, workflow, and usability will be addressed.

A key element of training with virtual reality surgical simulators is the definition of the simulated patients. This step typically includes the generation of geometric models of healthy and pathological anatomy, organ textures, vessel structures, and the determination of tissue deformation parameters.

Participants: Thomas Wolf Michael Emmersberger Matthias Harders

Partners:

VirtaMed AG, Switzerland
Eidgenössische Technische Hochschule Zürich, Switzerland

Evaluation and Validation of Ultrasound for the Management of Organ Motion in Tumour Therapy

Radiation therapy is the established method for non-resectable tumours. The art of radiation therapy is to deliver a lethal dose to all cancerous tissue whilst sparing as much healthy tissue as possible. Recent advances in three-dimensional (3D) planning and treatment technologies have enabled the delivery of highly conformal dose distributions. However exploiting the full potential of these highly localised treatment methods requires compensation for organ motion, which is substantial for tumours in the thorax and the abdomen. Beside quasiperiodic respiratory motion, the organ undergoes secondary modes of deformations caused for example by the cardiac cycle motion, digestive activity, gravity, muscle relaxation, or filling of the bladder. While methods have been proposed to compensate to some extent for perpetual breathing motion, they neglect the drift caused by the secondary modes and hence become inaccurate after a short period of time. In previous work, we have shown that the liver motion can be more accurately predicted by employing a statistical drift model updated by the current position of internal surrogate markers. Expanding on this theoretical analysis, we aim in this project at developing the techniques for real-time tumour location predictions based on the 3D position of one or more US tracked surrogate landmarks. Key milestones include efficient adaptive acquisition of 4D (3D+time) MRI data, syncronous acquisition of ultrasound and 4D MRI data, tracking of surrogate landmarks in ultrasound, prediction of organ motion based on surrogate marker position and statistical motion model, and evaluation of prediction accuracy. The 3-year project starts in January 2010 and is supported by the Swiss National Science Foundation.

Participants: Valeria De Luca Christine Tanner Gábor Székely

Partners: Radiology Department, University Hosptial Geneva
Division of Radiological Physics, University Hospital Basel
Medical Image Analysis Centre, University of Basel
Centre for Proton Therapy, PSI

Exploring the Potential of Visuo-Haptic Augmented Reality for Medical Education

Objective:

In this collaborative project, we explore the application of visuo-haptic augmented reality in medical education. The goal is to augment both the real visual and haptic environment seamlessly with virtual information, while maintaining full functionality. One targeted future application area is training of breast cancer screening.

The goals of this collaboration are two-fold. First, we focus on haptic AR systems that enable us to augment the attributes of real objects with virtual force feedback. Second, we apply the integrated visuo-haptic AR system to medical training and evaluate the performance in empirical usability experiments.

Participants: Seokhee Jeon Matthias Harders

Partners:

Eidgenössische Technische Hochschule Zürich, Switzerland
POSTECH, South Korea

ArthroS

Objective:

The target of this project is the development of a training simulator for knee arthroscopy.

Participants: Martin Seiler Jonas Spillmann Matthias Harders

Partners:

VirtaMed AG, Switzerland
Eidgenössische Technische Hochschule Zürich, Switzerland
University Hospital Balgrist, Switzerland
Zurich University of Applied Sciences, Switzerland

PASSPORT

Objective:

The PASSPORT for Liver Surgery project aims to develop patient-specific models of the liver which integrates anatomical, functional, mechanical, appearance, and biological modeling. To these static models, PASSPORT will add dynamics liver deformation modeling and deformation due to breathing, and regeneration modeling.

These models, integrated in the Open Source framework SOFA, will culminate in generating the first multi-level and dynamic “Virtual patient-specific liver” allowing not only to accurately predict feasibility, results and the success rate of a surgical intervention, but also to improve surgeons’ training via a fully realistic simulator, thus directly impacting upon definitive patient recovery suffering from liver diseases.

The main task of the Virtual Reality in Medicine group is the development of methods for stable mesh modifications as well as patient-specific texturing.

Project-Website: http://www.passport-liver.eu/

Participants: Basil Fierz Olexiy Lazarevych Matthias Harders

Partners:

Institut de Recherche contre les Cancers de l’Appareil Digestif, France
Eidgenössische Technische Hochschule Zürich, Switzerland
Technische Universität München, Germany
Université Catholique de Louvain, Belgium
Imperial College of London, United Kingdom
Institut National de Recherche en Informatique et Automatique, France
Universität Leipzig, Germany University College of London, United Kingdom
Université Louis Pasteur, France
Karl Storz GmbH & Co. KG, Germany

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.

Participants: Dominik Szczerba Bryn Lloyd Sven Hirsch Gábor Székely

Partners: ETH Zürich, Animal Imaging Center

ISJRP

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.

Participants: Dominik Szczerba Robert McGregor Sven Hirsch Gábor Székely

Partners: Indian Institute of Technology, Kanpur, India

Acquisition and Modelling of Respiratory Organ Motion

Objective: The main goal of this project is to develop methods for the acquisition and modelling of respiratory organ motion and its intra-subject and inter-subject variability. Such realistic motion data are required to improve proton therapy planning in the presence of organ motion. For example, different dose delivery methodologies can be compared with respect to sensitivity to organ motion.

The developed 4DMRI method can capture breathing and its variability without using ionising radiation. Together with motion estimation by non-rigid registration, this permits for the first time to study the variability of respiratory motion over tens of minutes and provides valuable knowledge for numerous other treatments such as motion correction in radiofrequency (RF) tumor ablation or gating in MR guided Focused Ultrasound (MRgFUS).

Project-Website: http://www.vision.ethz.ch/4dmri/

Participants: Philippe Cattin Martin von Siebenthal Gábor Székely

Partners: Division of Radiation Medicine, PSI
Institute of Biomedical Engineering, ETHZ/USZ

Mosaicking of Endoscopic Placenta Images to Assist Treatment of the Twin to Twin Transfusion Syndrom

Objective: The Twin to Twin Transfusion Syndrome (TTTS) is a disease of the placenta. It affects identical monochorionic (shared placenta) twins during pregnancy where blood passes from one baby to the other through abnormal vascular connections within their shared placenta. One baby, the recipient twin, gets too much blood that might overload his cardiovascular system and might die from heart failure. The other baby, the donor twin, does not get enough blood and may die from severe anemia. The tragedy is that these babies are healthy. The problem is in the placenta. The death rate for twins who develop TTTS at mid-pregnancy may be as high as 80 to 100 percent. Babies may die in utero, at birth from prematurity or years later from the effects of TTTS. Those who survive suffer from many serious problems, including cerebral palsy.

This disease can be treated by endoscopic laser surgery. The procedure uses an endoscope to identify, and a laser to coagulate, the connecting vessels in the placenta and block the passage of blood from one twin to the other. The procedure for identifying the abnormal connections is not so easy. The endoscope has a small field of view and the obtained images show severe lens distortions and suffer from weak visibility in the amniotic fluid. This makes it difficult for the surgeon to ensure that all the abnormal vascular connections have been found and treated accordingly. This project consists of two phases: the first phase is devoted to the calibration of the endoscope, in order to eliminate the lens distortion in the images. The second phase comprises of the construction of a mosaic of the entire placenta with these small images. This will give the surgeon a map of the entire placenta, on which he can easily detect all the abnormal connecting vessels.

Participants: Philippe Cattin Mireille Reeff Gábor Székely

Partners: University Hospital Zürich

Quantitative Endoscopy

Objective: Real-time quantitative measurements and 3D visualization during endoscopic surgery can provide the clinician with valuable additional information. The goal is to use the endoscope rather as imaging device than just as a keyhole!

Providing this additional information without adding another tool to the operating scene and without demanding more training for the surgeon is another important aspect (less cost, less risk, shorter surgery).

In a first step we want to work on scenes where something can be tracked rigidly such as in orthopedic or even neurosurgical surgery. In a second phase we might consider to bring this technology to more complex scenes where deformations can occur. But we will restrict ourselves to the case where this motion and deformation can be parameterized.

Once the relationship between the target anatomy, the tools and the preoperative model is establish it is also possible to create a see-through patient with Augmented Reality techniques so the surgeon has all information in the same display.

Project-Website: http://www.vision.ethz.ch/cwengert/research.php

Participants: Philippe Cattin Christian Wengert Gábor Székely

Partners: EPFL, Virtual Reality and Active Interfaces Group: Charles Baur
CHUV, Neurosurgery: John M. Duff

Application of the Morel Atlas for target volume planning in functional neurosurgery

Objective:

Performing functional neurosurgery without the possibility of direct neural stimulation requires the precise determination of the target position solely based on pre-operative MR data. This will be achieved by navigating the pilot MR images using the Morel histological atlas of the basal ganglia.

Currently only fixed geometrical relations between specific anatomical landmarks in appropriately selected datasets are used for the prediction of the stereotactical locations during interventional planning. The Morel atlas, however, implicitly contains much more information about the anatomical relations between the relevant structures of the basal ganglia.

In order to improve the precision of the interventional planning we will convert the currently existing atlas in a digital representation and develop statistical shape models to fully exploit its inherent potential.

We will then develop software tools to be integrated into the overall TcMRgFUS system for high precision interventional planning based on structural data gained from MR acquisitions. These tools will enable atlas-based target volume planning allowing to overlay the full set of stereotactic targets onto the MR image used for the planning process.

Participants: Andras Jakab Axel Krauth Gábor Székely

HAMAM

HAMAM - European Highly Accurate Breast Cancer Diagnosis through Integration of Biological Knowledge, Novel Imaging Modalities, and Modelling - consists of 9 project partners from 7 countries with leading expertise in the field of breast imaging diagnosis, with EIBIR as the coordinating partner. The 3-year project started in September 2008 and is supported by the European Commission.

Project-Website: http://www.hamam-project.org/cms/website.php

Participants: Jan Lesniak Christine Tanner Rémi Blanc Gábor Székely

Partners: EIBIR (AT)
University College London (UK)
MEVIS Research Gmbh (DE)
MEVIS Medical Solutions AG (DE)
ETH Zurich - Computer Vision Laboratory (CH)
Radboud Universiteit Nijmegen (NL)
The University of Dundee (UK)
CHARITE Berlin (DE)
Boca Raton Community Hospital, Inc (US)