Segmentierung von Hirngefäßen und Blutflussanalyse in der 4D-Magnetresonanzangiographie bei zerebralen arteriovenösen Malformationen - Untersuchungen zu Hämodynamik und Gewebemarkern

Zur Planung einer invasiven Therapie für Patienten mit Gefäßfehlbildungen des Gehirns, sog. arteriovenöse Malformationen (Abk.: AVM), ist die Abschätzung des individuellen natürlichen Blutungsrisikos von entscheidender Bedeutung. Im Rahmen des Projektes wurden neue Methoden zur Segmentierung von AVMs in 3D-TOF-MRA-Bilddaten sowie zur Analyse des Blutflsuses in 4D-TREAT-MR-Bilddaten entwickelt und zur Auswertung im Rahmen von Studien in ein Softwaresystem namens AnToNIa (Abk. f.: Analysis Tool for Neuro Imaging Data) integriert.  Mithilfe der hier verfügbaren Bildanalyse- und Visualisierungsmethoden ist eine Quantifizierung und dreidimensionale Darstellung des Blutflusses bei AVM-Patienten in hoher räumlicher und zeitlicher Auflösung möglich (Abb. 1).


Abb. 1: Dynamische Darstellung des Bluteinflusses (a-i) auf einem hochaufgelösten 3D-Oberflächenmodell des zerebralen Gefäßsystems

Zur genauen Darstellung und Analyse der räumlichen Struktur des Gefäßsystems im Gehirn konnte durch das neue vierstufige Segmentierungsverfahren unter Einbeziehung von Form- und Intensitätsinformationen eine deutliche Verbesserung gegenüber etablierten Verfahren erreicht werden (Abb. 2). Für die zeitaufgelöste Magnetresonanzangiographie (TWIST/TREAT) wurde ein neues Verfahren der referenzbasierten Kurvenanpassung zur robusten Quantifizierung der Hämodynamik auf Basis von 4D-MRA-Bildsequenzen mit hoher Genauigkeit entwickelt. Im Rahmen einer Monte Carlo Simulation konnte gezeigt werden, dass die Präzision des neuen Verfahrens gegenüber den etablierten Verfahren um 59% gesteigert und dabei die Laufzeit um 33% reduziert werden konnten. Ein weiterer wesentlicher Vorteil des neuen Verfahrens ist die implizite Berücksichtigung der individuellen physiologischen Charakteristika durch die Verwendung einer Referenzkurve.


Abb. 2: 3D-Oberflächenmodell eines zerebralen Gefäßsystems von einem Patienten mit diagnostizierter AVM.


Abb. 3: Farbcodierte Darstellung der extrahierten Werte der Bolus Arrivial Time (BAT) auf einem 3D-Oberflächenmodell (links) und in einer 3D-TOF-MRA-Schicht (rechts). Anhand der BAT-Werte wird erkennbar, welche Gefäße zuerst und welche später durchflossen werden.

Insgesamt wurden innerhalb des Projektes mehr als 50 Patienten mit der TWIST/TREAT untersucht und die Daten mittels der hier der entwickelten Software analysiert. Zunächst wurde der Zusammenhang zwischen den makrovaskulären Fluss und der mikrovaskulären Perfusion um den Nidus herum untersucht. Die Ergebnisse dieser Untersuchung sprechen für zwei Ebenen der Perfusionsbeeinträchtigung: eine makrovaskulär-territoriale und eine mikrovaskulär-lokale Ebene. Darüber hinaus wurde untersucht, ob sich AVMs mit hohem und niedrigem Blutungsrisiko hinsichtlich ihrer hämodynamischen Parameter unterscheiden. Hierbei zeigte sich statistisch robust, dass hohe arterielle Einflussgeschwindigkeiten einen Risikofaktor für eine AVM-Blutung darstellen. Das visuelle Rating und der Vergleich mit der konventionellen Angiographie sind abgeschlossen. Hierbei zeigte sich, dass die dreidimensionale flusskodierte Sichtweise auf die Daten erhebliche Vorteile bietet. Es wurden drei intranidale Flussmuster identifiziert: homogen, uni¬direktional und heterogen.

Die im Rahmen des Forschungsprojektes entwickelten Verfahren und deren Implementierung in ein benutzerfreundliches Auswertetool bilden zudem die Grundlage für diverse weitere Forschungsarbeiten, insbesondere auf dem Gebiet der Hirngefäßaneurysmen.

Das Projekt wird von der Deutschen Forschungsgemeinschaft gefördert (Ha2355/10-1).

Ausgewählte Publikationen

  1. Forkert N.D.,  Illies T., Goebell E., Fiehler J., Säring D., Handels H.,
    Computer-aided Nidus Segmentation and Angiographic Characterization of Arteriovenous Malformations,
    International Journal of Computer Assisted Radiology and Surgery, 8, 775-786, 2013
  2. Forkert N., Schmidt-Richberg A., Fiehler J., Illies T., Möller D., Säring D., Handels H., Ehrhardt J.,
    3D Cerebrovascular Segmentation combining Fuzzy Vessel Enhancement and Level-sets with Anisotropic Energy Weights,
    Magnetic Resonance Imaging, 31, 2, 262-271, 2013
  3. Forkert N., Fiehler J., Illies T., Möller D., Handels H., Säring D.,
    4D Blood Flow Visualization Fusing 3D and 4D MRA Image Sequences,
    Journal of Magnetic Resonance Imaging, 36, 2, 443-53, 2012
  4. Forkert N., Illies T., Möller D., Handels H., Säring D., Fiehler J.,
    Analysis of the Influence of 4D MRA Temporal Resolution on Time-to-Peak Estimation Accuracy for Different Cerebral Vessel Structures,
    American Journal of Neuroradiology, 33(11), 2103-2109, 2012
  5. Forkert N., Fiehler J., Schönfeld M., Sedlacik J., Regelsberger J., Handels H., Illies T.,
    Intranidal Signal Distribution in Post-contrast Time-of-Flight MRA is Associated with Rupture Risk Factors in Arteriovenous Malformations,
    Clinical Neuroradiology, Epub ahead of print, Aug. 2012, Doi 10.1007/s00062-012-0168-8
  6. Forkert N., Kaesemann P., Treszl A., Siemonson S., Cheng B., Handels H., Fiehler J., Thomalla G.,
    Comparison of 10 TTP and Tmax Estimation Techniques for MR Perfusion-Diffusion Mismatch Quantification,
    American Journal of Neuroradiology, 34, 1697-1703, 2012
  7. Forkert N., Schmidt-Richberg A., Fiehler J., Illies T., Möller D., Handels H., Säring D.,
    Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets,
    Methods of Information in Medicine, 5, 415-422, 2012
  8. Forkert N. Schmidt-Richberg A., Fiehler J., Illies T., Möller D., Handels H., Säring D.,
    Fuzzy-based Vascular Structure Enhancement in Time-of-Flight MRA Images for Improved Cerebrovascular Segmentation,
    Methods of Information in Medicine, 50, 1, 74-83, 2011
  9. Forkert N., Säring D., Handels H.,
    Automatic Analysis of the Anatomy of Arteriovenous Malformations  using 3D and 4D MRA Image Sequences,
    MedInfo 2010, Kapstadt, South Africa, Studies in Health Technology and Informatics, 160, 1268-72, 2010
  10. Forkert N., Säring D., Fiehler J., Illies T., Möller D., Handels H.,
    Automatic Brain Segmentation in Time-of-Flight MRA Images,
    Methods of Information in Medicine, 48, 5, 399-407, 2009
  11. Dennis Säring, Jens Fiehler, Nils Forkert, Merle Piening, Heinz Handels
    Visualization and Analysis of Cerebral Arteriovenous Malformation Combining 3D and 4D MR Image Sequences,
    International Journal of Computer Assisted Radiology and Surgery, 2, 75-79, 2007

Projektteam

Dipl.-Inf. Nils Folkert (Institut für Medizinische Informatik, UKE Hamburg)
Dr. Dennis Säring (Institut für Medizinische Informatik, UKE Hamburg)
Prof. Dr. Heinz Handels

Kooperationspartner

Prof. Dr. med. Jens Fiehler
Dr. med. Till Illies
Klinik für Neuroradiologische Diagnostik und Intervention, UKE

 

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4D Medical Image Computing for Image-based Risk Assessment in Radiation Therapy of Moving Tumors

Second phase of the DFG-funded project „4D Medical Image Computing for Model-based Analysis of Respiratory Tumor and Organ Motion“

Respiratory organ and tumor motion is a significant source of error in radiation therapy of the thorax and upper abdomen. In recent years, a variety of technical solutions has been developed to explicitly account for breathing motion during radiation treatment. Methods in clinical use are e. g. so-called gating techniques or respiratory-triggered dose delivery. This means that radiation is only delivered during specified phases of the patients’ breathing cycle. The phases are usually determined using external breathing signals and breathing motion indicators like abdominal bellows or camera-based tracking of surface/skin motion.

External signals, however, are only indicators or surrogates of the inner body tumor motion. Considering especially the risk of intra- and interfractional variations of respiratory motion patterns (and the relationship between tumor motion and breathing signals, respectively) this project aims at investigating the suitability of different motion indicators for predicting tumor motion. Based on 4D CT and 4D MRT image sequences of lung tumor patients acquired over the course of treatment we further intend to quantify dosimetric influences of intra- and interfractional motion variability in standard and gated radiation therapy. Dosimetric consequences in gated radiation therapy are studied simulating the use of different motion indicators – with the final goal of establishing an indicator-specific risk assessment and the development of strategies for combination and optimization of typical breathing motion surrogates (fig. 1).
 
The project is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG), HA 2355/9-2).

 

 

Fig. 1: Results of a correlation analysis between skin and tumor motion for a lung tumor patient (red: high correlation, green: low correlation). The correlation analysis was based on a 4D CT image se-quence of the patient and the results are used to determine optimal scanline positions of a line laser when applied as a breathing motion indicator in e. g. respiratory-triggered radiation therapy.

Project Team:

M.Sc. Matthias Wilms
Dipl-Inf. Dipl.-Phys. René Werner
Dr. Jan Ehrhardt
Prof. Dr. Heinz Handels

Cooperation Partners:

Prof. Dr. H.-P. Schlemmer, Dr. M. Eichinger / Dr. R. Floca
Abteilung Radiologie / AG Software Development for Integrated Diagnostic and Therapy
Deutsches Krebsforschungszentrum (DKFZ) Heidelberg, Germany

Prof. Dr. Dr. J. Debus, Dr. Dr. C. Thieke
Klinik für Radio-Onkologie und Strahlentherapie
Universitätsklinikum Heidelberg, Germany

Prof. Dr. C. Petersen, Dr. F. Cremers
Klinik und Poliklinik für Strahlentherapie und Radioonkologie
Universitätsklinikum Hamburg-Eppendorf (UKE), Germany

 

 

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Simulation and Evaluation of the Impact of Breathing Motion on Thoracic Dose Distributions for Different Radiation Delivery Techniques using 4D CT Data

Modern radiation therapy techniques are capable to deliver dose within millimeter or even submillimeter precision. Highly accurate knowledge about position and shape of anatomical and pathological structures is a prerequisite to exploit the potential of these techniques. However, in current clinical practice treatment planning is still based on 3D CT imaging; movements of anatomical and pathological structures are not represented. Therefore uncertainties arising are accounted for by introducing safety margins (fig. 1). Margins are usually defined with respect to observations based on appropriate patient populations. This causes problems for the individual patient: On the one hand safety margins could be too large; this would increase the volume of healthy tissue to be irradiated which in turn would increase the likelihood of treatment related complications. On the other hand margins could be too small; this would limit the chances of successful treatment. Furthermore, for certain radiation delivery techniques such as intensity modulated radiotherapy the interplay of segmentation (i.e. application of small radiation fields of only a few monitor units) and target motion raises the risk of cold and hot spots (areas of high or low dose when compared to the planned dose).

The problems gain in importance with increasing motion amplitudes. Consequently, breathing motion - which causes motion amplitudes of up to several centimetres (fig. 2) - states a profound problem within the field of radiation therapy of thoracic tumors. By means of 4D CT data sets of lung tumor patients our project aims to answer fundamental questions within the field of radiation therapy of thoracic tumors. As a starting point we analyze breathing dynamics; related questions are here: What kind of motion patterns of anatomical and pathological structures exist? Do current safety margin concepts used in clinical practice cover the motion patterns? Is there a chance to improve the safety margin concepts by our observations?

In the next step we study the impact of breathing motion on the dose distribution applied to the patient; these analyses are done within a computer based simulation study. Different radiation delivery techniques are simulated such as 3D conformal radiation therapy and intensity modulated radiotherapy. For the different techniques the advantage of gated radiation delivery over ungated radiation delivery is examined.

This project is funded by the Deutsche Krebshilfe (DKH-Nr. 107899).

Fig. 1: Planning CT, planned dose distribution, and corresponding target volumes (planning target volume PTV and gross tumor volume GTV) of a lung tumor patient; the GTV-PTV safety margin is defined motion oriented.

Fig. 2: Respiratory motion as acquired by 4D CT. The lung tumor to be irradiated is highlighted in red.

Selected Publications:

  1. René Werner, Jan Ehrhardt, Alexander Schmidt-Richberg, Bernd Bodmann, Florian Cremers, Heinz Handels
    Dose Accumulation based on Optimized Motion Field Estimation using Non-Linear Registration in Thoracic 4D CT Image Data
    In: Dössel O., Schlegel W.C. World Congress on Medical Physics and Biomedical Engineering, WC 2009, Springer Verlag, Berlin, IFBME Proceedings 25/IV, 950-953, 2009.
  2. R. Werner, F. Fehlauer, J. Ehrhardt, D. Albers, F. Cremers, R. Schmidt, H. Handels: Impact of Breathing Phase on Thoracic Dose Distribution - an IMRT Treatment Planning Study using 4D CT Image Data. 9th Biennial ESTRO Meeting on Physics and Radiation Technology For Clinical Radiotherapy of the European Society of Therapeutic Radiology, Barcelona, Radiotherapy & Oncology 84(Supplement 1): 153, 2007.

Project Team:

Dipl.-Inf. Dipl.-Phys. René Werner
Dr. Jan Ehrhardt
Prof. Dr. Heinz Handels

Cooperation Partners:

Dr. rer. nat. Florian Cremers
Department of Radiotherapy and Radio-Oncology
University Medical Center Hamburg-Eppendorf

 

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Integrated 4D Segmentation and Registration of Spatio-temporal Image Data

The introduction of spatio-temporal tomographic image data enabled the analysis of dynamic physiological processes like heart beat or respiratory lung motion. However, its potential is not yet fully capitalized on. A comprehensive diagnostic and therapeutic usage of 4D data requires on the one hand a delineation of clinically relevant structures (segmentation), on the other hand an explicit description of motion characteristics (registration). Classical approaches regard both problems independently, however, a mutual dependency between them exists.

Aim of this project is the development of simultaneous segmentation and registration approaches that allow for a modeling of the mutual dependency. A-priory knowledge about physiology and motion dynamic is introduced by formulating appropriate side conditions. Methods for an automatic parameter detection and refinement allow for an adaption of the proposed segmentation and registration algorithm to specific medical applications. Moreover, the introduction of interaction tools enables the user-driven correction and improvement of results.

The base idea of the approach is illustrated in the figure below. Looking at two 3D images of a spatio-temporal data set (called reference and target image), a given segmentation of the reference image is assumed. Aim is on the one hand the calculation of a segmentation of the target image, on the other hand the motion estimation between target and reference image. An additional term ensures the consistency of segmentation and registration by comparing target and transformed reference segmentation.

The approaches developed in this project are evaluated for the simultaneous segmentation and motion estimation of lung and liver on the base of clinical CT data.

The project is funded by Deutsche Forschungsgemeinschaft (DFG: EH 224/3-1).

Selected Publications:

  1. A. Schmidt-Richberg, H. Handels, J. Ehrhardt:
    Integrated Segmentation and Non-linear Registration for Organ Segmentation and Motion Field Estimation in 4D CT Data.
    Methods Inf Med, 48(4): 334–339, Jan 2009.
  2. A. Schmidt-Richberg, J. Ehrhardt, R. Werner, H. Handels
    Direction-Dependent Regularization for Improved Estimation of Liver and Lung Motion in 4D Image Data.
    In: SPIE Medical Imaging 2010, San Diego, USA, Vol. 7623, 76232Y, 2010.
  3. A. Schmidt-Richberg, J. Ehrhardt, R. Werner, H. Handels
    Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-dependent Regularization.
    In: G.-Z. Yang et al. (eds.): Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009, London, LNCS Vol. 5761, 755–762, 2009.
  4. J. Ehrhardt, A. Schmidt-Richberg, H. Handels
    Simultaneous Segmentation and Motion Estimation in 4D-CT Data Using a Variational Approach.
    In: J.M. Reinhardt et al. (eds.): Image Processing, SPIE Medical Imaging 2008, San Diego, Vol. 6914, 37-1–37-10, 2008.
  5. J. Ehrhardt, A. Schmidt-Richberg, H. Handels
    A Variational Approach for Combined Segmentation and Estimation of Respiratory Motion in Temporal Image Sequences.
    IEEE International Conference on Computer Vision 2007, ICCV 2007, Rio de Janeiro, Brazil, CD-ROM-Proceedings, IEEE Catalog Number CFP07198-CDR (ISBN 978-1-4244-1631-8), 2007. 

Project Team:

Dipl.-Inf. Alexander Schmidt-Richberg
Dr. Jan Ehrhardt
Prof. Dr. Heinz Handels

 

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Artefact reduced Reconstruction of 4D-CT Data Sets from 3D-CT Data Segments

Problem:

Modern CT scanners are able to acquire multiple slices at the same time. But the volume scanned within one rotation of the gantry is very often too small to image organ motion. But applications e.g. in radiation oncology may need information about organ motion. It is for example important to image and measure organ motion caused by breathing. For this reason tumour patients have been scanned several times with a CT scanner delivering single 3D-CT data segments. With the use of these segments 4D-CT data sets can be reconstructed. They can be used for further analysis of organ motions due to breathing.

Methods:

A modern multi-slice CT scanner has been used to scan a part (= segment) of the patients during free breathing [1]. The position within the breathing cycle was measured with a spirometer so that multiple CT data segments were acquired at different positions within the breathing cycle. By moving the CT couch data segments for the whole thoracic region could be scanned.

The 3D-CT data segments have to be processed to reconstruct a 4D-CT data set from the 3D-CT data segments. Therefore data segments are needed for each couch position that have been acquired in the same position within the breathing cycle. Using standard techniques, segments are selected that are closest to the lung volume chosen (fig. 1). This method can be called "nearest neighbour interpolation". Segments that are most similar to the volume desired are chosen. By the use of this method artefacts occur that are visible especially at the diaphragm.

Fig. 1: Selected 2D view with artefacts form a 4D data set reconstructed with nearest neighbour interpolation

To reduce these artefacts an interpolation method has been developed using non-linear image registration methods. The non-linear registration method is based on the "optical flow". The application of this method on two data segments approximates a vector transformation field describing the shift of the single voxels between neighbouring lung volumes. With this 3D transformation field data segments can be calculated for the lung volume selected. The results (fig. 2) show less artefacts than the images reconstructed with the nearest neighbour strategy (fig. 1).

Fig. 2: Selected 2D view with reduced artefacts form a 4D data set reconstructed with non-linear optical flow based image registration methods

Selected Publications:

  1. Jan Ehrhardt, René Werner, Thorsten Frenzel, Dennis Säring, Wei Lu, Daniel Low, Heinz Handels: Optical Flow based Method for Improved Reconstruction of 4D CT Data Sets Acquired During Free Breathing.
    Medical Physics, 34, 2, 711-721, 2007.
  2. René Werner, Jan Ehrhardt, Thorsten Frenzel, Dennis Säring, Wei Lu, Daniel Low, Heinz Handels: Motion Artifact Reducing Reconstruction of 4D CT Image Data for the Analysis of Respiratory
    Dynamics.
    Methods of Information in Medicine, 46, 254-260, 2007.
  3. Heinz Handels, René Werner, Thorsten Frenzel, Dennis Säring, Wei Lu, Daniel Low, Jan Ehrhardt:
    Generation of 4D CT Image Data and Analysis of Lung Tumour Mobility During the Breathing Cycle.
    Stud Health Technol Inform, 124, 977-982, 2006.

Project Team:

Dr. Jan Ehrhardt
Dipl.-Inf. Dipl.-Phys. René Werner
Prof. Dr. Heinz Handels

Cooperation Partners:

Prof. Daniel Low and Dr. Wei Lu
Mallinckrodt Institute of Radiology
Washington University
St. Louis, USA

Dr. med. Dr. rer. nat. Thorsten Frenzel
Department of Radiotherapy and Radio-Oncology
University Medical Center Hamburg-Eppendorf
Germany

 

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4D Medical Image Computing for Model-based Analysis of Respiratory Tumor and Organ Motion

Breathing motion is a significant source of error in radiation therapy planning of the thorax and upper abdomen. The development of 4D (= 3D+t) imaging methods opened up the possibility to capture the spatio-temporal behaviour of tumors and inner organs. This project aims at developing methods for modelling, analysis, and visualization of respiratory motion of tumors and inner organs. The project is based on artefact reduced 4D CT patient data with high spatial and temporal resolution. The methods will complement possibilities offered by 4D imaging techniques to improve radiation therapy of thoracic and abdominal tumors.

The main focus of the project is to develop and evaluate improved non-linear registration methods in order to enable a precise estimation of 3D motion fields in the 4D CT image data. These dense vector fields are used for subsequent analysis and modelling of respiratory motion of structures of interest in radiation therapy such as tumors and organs at risk (fig. 1 and 2). Based on the patient collective we study the interpatient variability of tumor and lung motion whereas different lung regions are considered to analyze regional lung motion. Results are used to compare internal target volumes (ITV, i.e. the volume covered by the moving target) for different patients and, e.g., to examine whether it is possible to identify different but typical patterns of regional lung motion.

The project is funded by Deutsche Forschungsgemeinschaft (DFG) (HA 2355/9-1).


Fig. 1: Visualization of the 3D motion field between the phase of end-expiration and end-inspiration. The motion field estimation is based on optical flow based registration. Absolute values of the displacement fields are visualized color-coded. Red arrows indicate displacements of more than 20 mm. Figure taken from Handels et al., IJMI 76S, 433-9, 2007.


Fig. 2: Color-coded visualization of estimated appearance probabilities of lung tumors of two patients, displayed in a 2D slice.

Selected Publications:

  1. Alexander Schmidt-Richberg, Jan Ehrhardt, René Werner, Heinz Handels
    Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-dependent Regularization
    In: G.-Z. Yang Hawkes D., Reuckert D., Noble A., Taylor C. (eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009, Part I, LNCS 5761, Springer Verlag, Berlin, 755-762, 2009.
  2. H. Handels, R. Werner, T. Frenzel, D. Säring, W. Lu, D. Low, and J. Ehrhardt:
    4D Medical Image Computing and Visualization of Lung Tumor Mobility in Spatio-temporal CT Image Data, International Journal of Medical Informatics, 76S, S433-S439, 2007.
  3. J. Ehrhardt, R. Werner, T. Frenzel, W. Lu, D. Low,  H. Handels:
    Analysis of Free Breathing Motion Using Artifact Reduced 4D CT Image Data, In: P.W. Pluim, J.M. Reinhardt (eds.), SPIE Medical Imaging 2007: Image Processing, San Diego, Proc. SPIE, Vol. 6512, 1N1-1N11, 2007.
  4. R. Werner, J. Ehrhardt, T. Frenzel, W. Lu, D. Low, H. Handels:
    Analysis of Tumor-influenced Respiratory Dynamics using Motion Artifact Reduced Thoracic 4D CT Images. In: T. Buzug et al. (eds.), Advances in Medical Engineering, Springer Verlag, Berlin, 181-186, 2007.

Project Team:

Dipl.-Inf. Dipl.-Phys. René Werner
Dr. Jan Ehrhardt
Dipl.-Inf. Alexander Schmidt-Richberg
Prof. Dr. Heinz Handels

Cooperation Partners:

Dr. rer. nat. Florian Cremers
Department of Radiotherapy and Radio-Oncology
University Medical Center Hamburg-Eppendorf (UKE)

Dr. med. Dr. rer. nat. Thorsten Frenzel
Ambulanzzentrum des UKE GmbH
Bereich für Strahlentherapie

Prof. Daniel Low and Dr. Wei Lu
Washington University in St. Louis, School of Medicine
St. Louis, MO, USA

 

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Created at July 12, 2010 - 11:32am. Last modified at June 30, 2014 - 11:45am.

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