Robine Janssen

Computed Tomography (CT) is an essential imaging technique in cancer care, providing detailed anatomical information for diagnosis, treatment planning, and follow-up. However, conventional CT systems require patients to lie down and rely on relatively complex and costly rotating gantries. Our research focuses on developing a new generation of CT imaging that can overcome some of these limitations while maintaining high image quality and reducing radiation dose. 
We are working on the development of a novel standing CT system using advanced image reconstruction techniques. By acquiring CT data from a standing patient using sparse and unconventional acquisition trajectories, the system aims to provide high-quality images while reducing the amount of radiation required. Because incomplete CT data cannot be reconstructed directly using conventional methods, we are investigating novel reconstruction approaches, including deep learning-based methods, to recover the missing information and obtain diagnostically useful images. 
The standing position also offers important opportunities for cancer imaging. Imaging the body under natural, weight-bearing conditions may provide complementary information compared with conventional supine CT, while simplified patient positioning could make imaging more accessible and patient-friendly. 
Beyond standalone CT, the technology could be integrated with emerging standing PET systems to create a fully standing PET-CT platform. This could provide improved anatomical localization and attenuation correction for molecular imaging while enabling new approaches to whole-body cancer imaging. 
Overall, our research aims to contribute to more flexible, dose-efficient, and high-quality medical imaging technologies, with the long-term goal of supporting improved cancer diagnosis, characterization, and multimodal imaging. 
 

Robine Janssen graduated as a Biomedical Engineer from Ghent University in 2025, where her master dissertation focused on inpainting sparse CT sinograms with AI to enable anatomical colocalization and attenuation correction for sparse PET scans. After graduation, she started her PhD at the MEDISIP research group of Ghent University, where she further explores AI applications in medical imaging. 
 

• Lab address: 
  IBiTech-MEDISIP, Campus UZ Gent, C. Heymanslaan 10, 
  The Core, entrance 37a, 9000 Gent, Belgium
• MEDISIP research group
• The Walk-Through PET  
• The Infinity Lab 
• LinkedIn
• ORCID
• Robine Janssen can provide developed AI algorithms for medical image segmentation and analysis can be tailored to meet specific research needs
• Robine Janssen is interested to receive invitations for presentations or talks