Research Group

Computational Patient Models

Dr. Kristina Giske

Dr. Kristina Giske

Research Group Leader

Our team is shaping digital patient twins tailored for radiotherapy. At the interface of advanced imaging, biophysical organ and tissue modeling, and artificial intelligence we explore subtle treatment responses using spatio-temporal-resolved in-silico patient replicas.

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

Our research is dedicated to in-silico radiotherapy simulation of cancer patients undergoing curative irradiation treatment. The first challenge for technically precise radiation therapy lies in adapting dose delivery to deformations in the patient’s anatomy induced by motion and physiological changes. The second challenge is to detect subtle changes in longitudinal imaging, which allow to test treatment outcome prediction models.

Our vision is to create a virtual patient model, serving as a proxy for treatment simulation by accumulating the physical dose and predicting the therapeutic effect of the planned treatment. This together empowers treating physicians to tailor the treatment to the specific needs of the patient before irradiation and during the treatemtn course. In-silico model development, advancements in imaging and computer technology, and our passion for unusual solutions are our tools in the fight against cancer.

Ongoing Projects

MIRROR - Mobile Illustrator of Radiotherapy course for Review, data Orchestration, and Reflection browser-based interactive radiothery patient models. In color and in 4D.
CLARITY - CineMR-guided ML-driven breaAthing models for adaptive RadIoTherapY Image processing for radiotherapy under respiratory motion
CBCTart - Data-Driven CBCT Image Quality Improvements for Online Adaptive Radiotherapy DL-based synthetic CT generation competing traditional approahces for dose calculation
LUDWIG - Lung imaging biomarker capability of Diffusion Weighted Imaging utilizing xGboost DWI as imaging biomarker for lungCa radiotherapy seen by XGBoost
ANAGRAPH - Anatomy Validation utilizing Knowledge Graphs knowledge graphs for validation of autark segmentaiton agents

Team

  • Employee image

    Dr. Kristina Giske

    Research Group Leader

  • Employee image

    Joao Pedro Dias Rodrigues

    Research Engineer, MSc Physics, MIRROR project lead

  • Employee image

    Goran Stanic

    Doctoral Student, MSc Physics, CBCTart project lead

  • Employee image

    Richard Häcker

    Doctoral Student, MSc Physics, CLARITY project lead

  • Employee image

    Nora Wolf

    Master's Thesis Project, Physics Uni HD, LUDWIG project lead

  • Employee image

    Alvaro Garmendia Navarro

    Bachelor's Thesis Project, Computer Science UniHD, ANAGRAPH project lead

Alumni

Reza Seyed Seif | Sina Thürwächter | Florian Ebert | Jakob Kreft | Dr. Pasit Jarutatsanangkoon | Mark Arndt ❘ Vahdaneh Kiani ❘ Mikulas Bankovic ❘ Nora Wolf ❘ Dr. Cornelius Bauer ❘ Tom Vichtl ❘ Katrin Weigand ❘ Eric Volkmann ❘ Stephen Schaumann ❘ Mohammad Amin Rashid ❘ Peter Lysakovski ❘ Dr. Kathrin Bartelheimer ❘ Rachid Zeghlache ❘ Charlotte Bordt ❘ Dr. Hendrik Teske ❘ Dr. Markus Stoll ❘ Dr. 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 ❘ Dr. Eva Stoiber 

Selected Publications

Walter A, Hoegen P, Stanic G, Rodrigues JP, Adeberg S, Jäkel  O, Frank M, Giske K. 2024 Segmentation of seventy-one anatomical structures necessary for the evaluation of guideline-conform clinical target volumes in head and neck cancers. Cancers 2024, 16(2), 415. doi.org/10.3390/cancers16020415 Preprints 2023, 2023121562. doi.org/10.20944/preprints202312.1562.v1

Yawson AK, Walter A, Wolf N, Klüter S, Hoegen P, ADebus J, Frank M, Jaekel O, Giske K. 2024 Essential parameters needed for a U-Net-based segmentation of individual bones on planning CT images in the head & neck region using limited datasets for radiotherapy application. Phys Med Biol. in press https://doi.org/10.1088/1361-6560/ad1996/

Bauer CJ,  Teske H, Walter A, Hoegen P, Adeberg S, Debus J, Jaekel O, Giske K. 2023 Biofidelic image registration for head and neck region utilizing an in-silico articulated skeleton as a transformation model. Phys Med Biol. 68(9):095006

Niebuhr NI, Johnen W, Echner G, Runz A, Bach M, Stoll M, Giske K, Greilich S, Pfaffenberger A. 2019 The ADAM-pelvis phantom - an anthropomorphic, deformable, and multimodal phantom for MRgRT. Phys Med Biol. 64(4):04NT05

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

For a full list of articles, theses etc. from the division, see here.

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Employee image

Dr. Kristina Giske

Research Group Leader
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