Medical Image Reconstruction and Analysis


The aim of this session is to guide the audience through the medical imaging pipeline – from acquisition and reconstruction to analysis – with a little help from AI. The talks will cover different image modalities, and focus on inverse problems in medical image reconstruction, motion estimation and motion correction. As image quality and accurate diagnostic assessment are key for the applicability of AI-based solutions in the medical context, the talks cover these aspects and provide insights into the uncertainty of AI-based algorithms in image reconstruction and analysis.

Organisers: Kerstin Hammernik (Chair) & Julia Schnabel (Committee member)

Session Schedule

8:00 - 8:20 Invited talk -
Medical Image Reconstruction and Analysis - with a little help from AI
Kerstin Hammernik
8:20 - 8:40 Invited talk -
Data-driven model corrections and learned iterative reconstruction
Andreas Hauptmann
8:40 - 9:00 Invited talk -
Freeze it: Estimating and compensating motion in MRI
Thomas Küstner
9:00 - 9:20 Invited talk -
Image quality transfer and democratization of MRI
Daniel Alexander
9:20 - 9:40 Invited talk -
Uncertainty Estimation in Medical Image Analysis and Reconstruction
Ender Konukoglu
9:40 - 11:00 Invited poster -
The role of data and models for deep-learning based image reconstruction
Reinhard Heckel
9:40 - 11:00 Contributed poster -
Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks
Chaithya Giliyar Radhakrishna*
9:40 - 11:00 Contributed poster -
Joint Cryo-ET Alignment and Reconstruction with Neural Deformation Fields
Valentin Debarnot
9:40 - 11:00 Contributed poster -
A framework for self-supervised MR image reconstruction using sub-sampling via Noisier2Noise
Charles Millard*
9:40 - 11:00 Committee member poster -
iFind, you find… On fetal ultrasound compounding
Julia Schnabel