Antoine Merlet is an assistant professor in artificial intelligence, medical imaging, and positron emission tomography (PET) systems. His work focuses on integrating deep learning, image processing, and Monte Carlo simulation to improve the performance and accuracy of PET imaging for both clinical and research applications.

He earned his Ph.D. in Computer Engineering, Automation, and Signal Processing from the University of Burgundy, France. His doctoral research focused on the simulation and validation of the GE Discovery MI PET/CT scanner using the GATE Monte Carlo platform. His research was conducted in collaboration with the Institute of Molecular Chemistry of the University of Burgundy (ICMUB), the Georges-François Leclerc Cancer Center (CGFL), and GE HealthCare.

• Ph.D., Computer Engineering, Automation and Signal Processing, SATT SAYENS-Université Bourgogne, France
• M.A., Medical Imaging, Erasmus Mundus Joint Master Degree in Medical Imaging and Applications (MAIA), University of Girona, Spain & University of Cassino and Southern Lazio, Italy

• Image processing, Classification, Segmentation & Registration
• Computer Aided Diagnosis
• Medical Imaging: PET / CT / SPECT / MRI
• Deep Learning and Machine Learning
• Languages and tools: Python / C++ / MATLAB / Bash; TensorFlow / PyTorch / Keras / Caffe / MatConvNet; GATE / GitHub / Linux / Elastix / LaTeX

1. A. Merlet et al., "Validation of a Discovery MI 4-ring model according to the NEMA NU 2-2018 standards: from Monte Carlo simulations to clinical-like reconstructions," EJNMMI Physics, vol. 11, 2024.
2. A. Merlet et al., "Development and validation of a Monte Carlo model for a SiPM-based PET scanner," Eur. J. Nuclear Med. Mol. Imaging, vol. 49, suppl. 1, p. S345, 2022.
3. A. Merlet et al., "Modeling the GE Discovery MI scanner in GATE: optimization of the digitizer," in Proc. IEEE NSS/MIC, 2021.
4. A. Merlet et al., "Monte Carlo simulation of a last-generation PET scanner: preliminary results according to the NEMA NU2-2018 standard," Eur. J. Nuclear Med. Mol. Imaging, vol. 48, suppl. 1, p. S181, 2021.
5. A. Merlet and R. Jolivot, "Filter-less low cost multispectral imaging system," in Proc. ESIT 2016, Thailand, 2016.