AI-driven anatomical and response adapted proton therapy
- Planned secondments: Norwegian University of Science and Technology - NTNU (Norway), Cosylab (Slovenia) and Politecnico di Milano (Italy)
- PhD program: Aarhus University, Faculty of Health
Project description
Adaptive radiotherapy currently focuses on anatomical variation, using daily imaging to restore planned dose distributions. However, many image changes during treatment reflect biological response – tumour regression or progression, and early normal-tissue effects – which may require genuine dose level adaptation rather than dose restoration.
This project will develop AI-based methods to distinguish between anatomical and biological components of daily image changes, as well as methods for subsequent corresponding dose optimization. The doctoral candidate will follow a structured progression of methodological tasks:
- Synthetic image generation: develop and validate methods to produce anatomically and biologically plausible training images.
- AI-based response characterization: build models that distinguish anatomy-driven from biology-driven image changes using multimodal features (population anatomy models, quantitative image biomarkers, radiomics, segmentation, accumulated dose, and uncertainty measures).
- Dose-optimization strategies: design algorithms that execute dose restoration and/or dose adaptation according to the identified type of change, including combined scenarios.
- In-silico integration: implement a proof-of-concept pipeline for response categorization and adaptive dose planning and evaluate it within a clinical treatment-planning system.
Three clinical datasets will be available for this (supporting different stages of the project: (1) daily CBCT from proton-treated head and neck cancer patients – central for modelling anatomical variation and testing daily-update strategies. (2) PET/MRI at baseline, mid-treatment, and follow-up from photon-treated patients – provides multimodal information on biological response patterns. (3) MRI and PET from proton-treated head and neck patients – supports cross-modality modelling and validation of combined anatomical and biological effects.
The project will take place in the research group AI and Big Data in Radiation Oncology of Professor Stine Korreman at Aarhus University. The medical physics research group is embedded in the joint oncology research environment at Aarhus University Hospital and housed at the Danish Center for Particle Therapy. The research environment is well-established and of the highest international standard, with research activities in radiation oncology bridging translational and clinical research. Aarhus University has a PhD program in clinical medicine at the Faculty of Health.
For more information concerning the research project please contact:
Stine Korreman
Candidate profile
Doctoral Candidate at Aarhus University
The candidate should have a MSc in computer science, data science, biomedical engineering, or mathematics/physics with a strong programming profile. Prior experience with deep learning for image analysis/generation will be considered a strength. Python programming experience likewise.
Furthermore the following skills will be considered essential:
- Fluency in English (oral and written).
- Analytical skills and ability to work independently on a project basis.
- Research interests and ambitions for excellence in data driven science.
- Good communication skills relevant for working in an international research group.
Aarhus University - DCPT
PROJECT BENEFICIARY
The Danish Centre for Particle Therapy (DCPT) provides proton radiotherapy for patients with cancer and benign tumours. As the only proton facility in Denmark, we treat patients from all across the country in close collaboration with Danish oncology departments. We also treat patients from abroad if we have available capacity. The centre houses a comprehensive international research group. The group carries out pre-clinical-, experimental-, translational- and clinical research within radiation oncology, medical physics and radiobiology. DCPT also has an extensive clinical research programme with a focus on gathering clinical evidence to develop treatment. We work closely together across disciplines with a collective goal to deliver treatment of the highest quality, improve outcome and patient experience.