Scalable Non-Cartesian MRI
Deep reconstruction methods for efficient magnetic resonance imaging.
This project direction focuses on scalable reconstruction algorithms for non-Cartesian magnetic resonance imaging. It connects model-based imaging, deep neural networks, and efficient reconstruction pipelines for practical MRI acquisition settings.
Representative work includes the R2D2 deep neural network series for scalable non-Cartesian MRI and related conference work on efficient MRI reconstruction.