prof. Brent van der Heyden (PhD)

Prof. van der Heyden’s research centers around leveraging Artificial Intelligence (AI) methodologies to (semi-)automate (pre)clinical workflows, to monitor the effectiveness and toxicity of radiotherapy treatments through biomarkers, and to enhance the quality of medical image diagnostics. Additionally, his research encompasses the advancement of medical imaging protocols, specifically focusing on multi-energy (µ)(CB)CT, and image reconstruction techniques.
These endeavors aim to effectively tackle the current challenges in radiotherapy while simultaneously minimizing potential hazards of ionizing radiation for the subjects undergoing medical imaging or radiotherapy treatment.

Through these research endeavors, his overall scientific research objects are twofold:

• to achieve a more precise translation from preclinical research findings into clinical practice, and
• to propel the field of radiotherapy by reducing the occurrence of radiation-induced side-effects and enhancing the effectiveness of curative radiotherapy through the implementation of adaptive fractionated treatment strategies. Every positive research finding is likely to have a direct positive impact on the well-being of cancer patients and their quality-of-life after post-treatment. 
   

Prof. dr. Brent van der Heyden completed his master’s degree (MSc) in medical-nuclear engineering technology in June 2016 through a joint scholar program offered by Hasselt University and the Catholic University of Leuven. During a MSc internship at Maastro clinic (Maastricht, NL), Brent focused on modeling the adverse effects of respiratory motion in preclinical precision irradiations of lung cancer. He commenced his doctoral studies within the physics research group at Maastro clinic, and successfully defended his PhD thesis on ‘Advanced Computed Tomography Imaging in Radiotherapy’ in June 2020. His thesis was awarded with the highest distinction (cum laude) in the Netherlands. In July 2020, he joined the lab of experimental radiotherapy at KULeuven to engage in research involving applied Artificial Intelligence methodologies to improve proton therapy range verification, proton radiography, radiotherapy dose calculations, and micro-CT imaging. After, he held a research appointment at the Belgian Nuclear Research Center (SCK•CEN) to develop a robotic radiotherapy dose verification system utilizing radiophotoluminiscent imaging. Since September 2022, Brent became Assistant Professor in Medical Device Technology at UGent.
 

• ‘Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging’, Physics in Medicine and Biology, 2022. (PMID: 35508145)
• ‘Artificial intelligence supported single detector multi-energy proton radiography system’, Physics in Medicine and Biology, 2021. (PMID: 33621962)
• ‘Deep learning based automated orthotopic lung tumor segmentation in whole-body mouse CT-scans’, Cancers, 2021. (PMID: 34572813)
• ‘A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder’, Physics in Medicine and Biology, 2020. (PMID: 32294626)
• ‘A comparison study between single- and dual-energy CT density extraction methods for neurological proton Monte Carlo treatment planning’, Acta Oncologica, 2019. (PMID: 31646923)
• ‘Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach’, Scientific Reports, 2019. (PMID: 30858409)
• ‘Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy’, Physics and Imaging in Radiation Oncology, 2019. (PMID: 33458300)
• ‘The influence of respiratory motion on dose delivery in a mouse lung tumour irradiation using the 4D MOBY phantom’, British Journal of Radiology, 2017. (PMID: 27626324)
   

• Lab address: 
  IBiTech-MEDISIP, Campus UZ Gent, C. Heymanslaan 10, 
  The Core, ingang 37a, 9000 Gent, Belgium 
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• Brent van der Heyden is interested to receive invitations for presentations or talks