Background
Thierry Meerbothe obtained his bachelor’s (2019) and master’s (2021) degree in Applied Physics at Delft University of Technology in the physics for health track. He did a thesis project in a collaboration between TU Delft and the Netherlands Cancer Institute (NKI) to develop a deep learning framework for dose prediction in patients with prostate cancer planned with autoplanning. Since September 2021 Thierry works as a PhD student in the radiotherapy department of the UMC Utrecht and the Computational imaging group under the supervision of Stefano Mandija and Nico van den Berg. His focus is on the development of clinical applications of Electrical Properties Tomography through electromagnetic simulations, MRI measurements and deep learning.

Three most recent publications

• Ilias I. Giannakopoulos, Alessandro Arduino, Cornelis A. T. van den Berg, Zhongzheng He, Kyu‐Jin Jung, Dong‐Hyun Kim, Riccardo Lattanzi, Jessica A. Martinez, Thierry Meerbothe, Freddy Odille, Adriano Troia, Luca Zilberti, and Stefano Mandija. Construction of Phantoms for MR Electrical Properties Tomography (From Structure to Composition): A Guideline From the ISMRM Electro‐Magnetic Tissue Properties Study Group. Journal of Magnetic Resonance Imaging, 2025.
• Thierry G. Meerbothe, Kyu‐Jin Jung, Chuanjiang Cui, Dong‐Hyun Kim, Cornelis A. T. van den Berg, and Stefano Mandija. Electrical properties based B1+ prediction for electrical properties tomography reconstruction evaluation. Magnetic Resonance in Medicine, 2025.
• Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, and Dong-Hyun Kim. A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography. NeuroImage, 2025.

Projects

• Electrical properties

Social media and other resources
Email: t.g.meerbothe@umcutrecht.nl | LinkedIn |