Deep Learning for MRI Reconstruction

Development and evaluation of deep learning methods for MRI reconstruction as part of the machine learning team behind a clinically used software product.

The work covers reconstruction pipelines based on Recurrent VarNet, DIRCN, and image-to-image models. My role spans training, refining, and evaluating models across varied experimental conditions, running systematic optimisation experiments, and contributing to reproducible workflows for the machine learning model lifecycle.

The central challenge is recovering clinically useful images from incomplete or degraded measurements, which places a strong emphasis on quantitative evaluation and failure analysis rather than qualitative inspection of outputs alone. This connects directly to broader research interests in inverse problems, learned representations, consistency constraints, and robustness across acquisition conditions.