MSc Thesis: Machine Learning in Fetal MRI Reconstruction

Fetal Magnetic Resonance Imaging (MRI) has become increasingly important to assess the development of the fetal brain. However, the acquisition is challenging due to the uncontrollable fetal motion. This requires both improved MR acquisition and reconstruction procedures. The objective of this thesis is to develop a learning-based reconstruction pipeline to reconstruct and monitor the fetal heartbeat and to investigate how we can transfer knowledge from adult cardiac MRI reconstruction to fetal cardiac MRI.


  1. Computer Science, Biomedical Engineering or similar background
  2. Strong background in machine learning
  3. Interest in medical imaging
  4. Proficient in Python
  5. Experience with ML frameworks, e.g., PyTorch / Tensorflow / Keras (optional)

We offer

  1. a close, personal supervision
  2. to work in an interdisciplinary team
  3. to collaborate with international experts in machine learning and MR image reconstruction.
Kerstin Hammernik
Kerstin Hammernik
Research Scientist

My research interests include inverse problems, MRI and machine learning