“R4” Reconstruction, Resolution, Regularization and Representation – Overcoming Medical Imaging Challenges
Abstract:
This session will encompass latest scientific advances in medical image reconstruction dealing with motion and other artifacts; super-resolution imaging and regularization for improved image quality; and representation learning (inherent to large vision/imaging models, but also neural implicit representations).
Organiser & chair: Julia Schnabel
Session Schedule
- 8:00 - 8:20
- Invited talk - Deep Learning for Motion Correction in MRI
- Julia Schnabel
- 8:20 - 8:40
- Invited talk - Self-supervised MR reconstruction
- Efrat Shimron
- 8:40 - 9:00
- Invited talk - Model-based Quantitative MRI Meets Machine Learning
- Gary Zhang
- 9:00 - 9:20
- Invited talk - Machine Learning in PET Image Reconstruction – Opportunities, Challenges, and Common Pitfalls
- Georg Schramm
- 9:20 - 9:40
- Invited talk - Towards Accurate Quantitative Photoacoustic Tomography: Combining Model and Learning Based Algorithms
- Andreas Hauptmann
- 9:40 - 11:00
- Invited poster - Neural Implicit k-space Representations
- Daniel Rueckert
- 9:40 - 11:00
- Invited poster - Physics-informed Machine Learning for Maternal and Fetal MRI
- Jana Hutter
- 9:40 - 11:00
- Contributed poster - TBC
- TBC
- 9:40 - 11:00
- Contributed poster - TBC
- TBC
- 9:40 - 11:00
- Contributed poster - TBC
- TBC