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Computational Patient Models

In-silico patient models for radiation oncology

headed by Dr. Kristina Giske


We are physicists, medical informaticians, computer scientists, mathematicians, and radiooncologists working together for the benefit of the patient.

You also want to join our team within your thesis project? Get in touch

Our research is dedicated to the simulation of the cancer patient receiving curative treatment by irradiation. The challenge for technically precise radiation therapy is the adaptation of dose delivery to deformations in the patient’s anatomy, which are induced by motion and physiological changes. Our vision is to design a virtual patient model or a digital twin, which is serving as a proxy for treatment simulation predicting the therapeutic effect of the planned treatment. Thus, the treating physicians are empowered to tailor the treatment to the specific needs of the patient prior to irradiation. Algorithm development, emerging computer technology, and our passion for unusual solutions are our tools in the fight against cancer.

Static in-silico patient models become alive through bio-mechanical motion laws. Motion vector fields help to monitor and optimize the accumulating dose distribution within each individual patient.

Research focus

The strength of radiation therapy - as a curative cancer treatment - is to precisely shape the dose deposition during delivery to a locally restricted target volume sparing healthy organs. As a consequence, radiotherapy is capable to reduce toxicity and adverse effects while simultaneously maintaining the efficiency of tumour control. 

Based on tomographic imaging of the patient’s anatomy a virtual patient model needs to be constructed inside a computer program to guide the targeted planning process aligning the coordinate systems of the radiation beam and the target volume inside the patient. Without adapting the beam position and shape inevitable patient deformation due to respiratory motion, pose change, and weight loss would result in deviations of the optimized treatment. Here is where our expertise comes into play!

Our projects aim at teaching the computer model

  • …to breath like the patient by detecting the respiratory motion patterns applying efficient image processing methods
  • …to change pose like the patient by bio-mechanical motion modelling using the degrees of freedom of body structures
  • …to imitate the variability of patient’s organs caused by e.g. the bladder filling applying bio-mechanically controlled constraints to the organ’s neighbourhood
  • …to lose weight or swallow like the patient by controlling the heterogeneity parameters of the involved soft tissue types

Controlling the virtual model aligned to the individual patient scans using our methods will allow us

  • …to identify the possible risks to fall behind the therapeutic goals  
  • …to develop strategies to compensate for the identified risks
  • …to guide the offline and online adaptation process of the dose delivery

Current projects

  • Biomechanical patient modelling
  • GPGPU parallelisation for image processing algorithms
  • OpenGL/CUDA visualization techniques
  • Virtual Reality head-mounted device applications
  • Deep learning for tissue segmentation using neuronal networks
  • Voxelized, tessellated, and analytical patient representation
  • Handling different MRI scans for MR-guided treatment


  • Dr. Kristina Giske (Group leader)
  • Kathrin Bartelheimer (PhD student)
  • Peter Lysakovski (master's student)
  • Mohammad Amin Rashid (master's student)
  • Stephen Schaumann (master's student)
  • Danah Pross (student assistant)

Alumni: Rachid Zeghlache Charlotte Bordt Hendrik Teske Markus Stoll Paul Mercea Angelika Czekalla Julian Suleder Simon Kirchhof Luis Fernando Paredes Ocampo Johannes Merz Daniel Schaubach Thomas Wollmann  Jan Meis Anna Storz Nico Schweiger Sarah Grimm Henry Müssemann Angelika Laha Anna Storz Dr. Eva Stoiber

Funded by

we gratefully acknowledge our supporting organisations

Selected publications

Stoiber EM, Bougatf N, Teske H, Bierstedt C, Oetzel D, Debus J, Bendl R, Giske K. 2017 Analyzing human decisions in IGRT of head-and-neck cancer patients to teach image registration algorithms what experts know. Radiat Oncol. 12:104

Teske H, Bartelheimer K, Meis J, Bendl R, Stoiber EM, Giske K. 2017 Construction of a biomechanical head and neck motion model as a guide to evaluation of deformable image registration. Phys Med Biol. 62(12):N271-N284

Teske H, Bartelheimer K, Bendl R, Stoiber EM, Giske K. 2017 Handling images of patient postures in arms up and arms down position using a biomechanical skeleton model. Current Directions in Biomedical Engineering 3(2):469-472

Bartelheimer K, Teske H, Bendl R, Giske K. 2017 Tissue-specific transformation model for CT-images. Current Directions in Biomedical Engineering 3(2):525-528

Stoll M, Stoiber EM, Grimm S, Debus J, Bendl R, Giske K. 2016 Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations. PLoS One 11(12):e0168916

Teske H, Mercea P, Schwarz M, Nicolay NH, Sterzing F, Bendl R. 2015 Real-time markerless lung tumor tracking in fluoroscopic video: Handling overlapping projected structures. Med Phys. 42(5):2540-9

Stoll M, Giske K, Debus J, Bendl R, Stoiber EM. 2014 The frequency of re-planning and its variability dependent on the modification of the re-planning criteria and IGRT correction strategy in head and neck IMRT. Radiat Oncol. 9:175

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