CIBM SP CHUV-UNIL Computational Neuroanatomy & Fetal Imaging Section
Thomas obtained from EPFL his master degree in Computational Science and Engineering in 2018 as well as his PhD in Computer Science in 2022. In his thesis, he focused on optimizing Cartesian sampling masks for accelerated MRI, with the goal of achieving the best image quality with the shortest acquisition time. In this context, he worked with both classical and deep learning methods for reconstruction, as well as reinforcement learning approaches for optimizing sampling masks.
He joined the CIBM SP CHUV-UNIL Section in 2022 as a postdoctoral researcher. He works on deep learning-based quality control of fetal brain MRI as well as 3D super-resolution reconstruction at the Medical Image Analysis Laboratory (MIAL) headed by Dr. Meritxell Bach Cuadra.
Keywords: magnetic resonance imaging (MRI), fetal imaging, deep learning