9:00 AM Coffee & Registration

9:30 AM Session I

Keynote I: Michael Unser, EPFL

Approximate k-space models and Deep Learning for fast photoacoustic reconstruction - Andreas Hauptmann

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks - Fabian Balsiger

Bayesian Deep Learning for Accelerated MR Image Reconstuction - Jo Schlemper

Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions - Felix Horger

11:00 AM Coffee break

11:30 AM Session II

Keynote II: Jong Chul Ye, KAIST

Left Atria Reconstruction from a Series of Sparse Catheter Paths using Neural Networks - Alon Baram

Detecting Anatomical Landmarks for Motion Estimation in Weight-bearing Imaging of Knees - Bastian Bier

Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging - Akshay S Chaudhari

Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging - Orcun Goksel

High quality ultrasonic multi-line transmission through deep learning - Sanketh Vedula

Poster spotlight talks (all poster presenters)

1:30 PM Lunch break and poster sessions

ETER-net: End To End MR image reconstruction using Recurrent neural network - Changheun Oh

Cardiac MR Motion Artefact Correction from K-space using Deep Learning-based Reconstruction - Ilkay Oksuz

Complex Fully Convolutional Neural Networks for MR Image Reconstruction - Muneer Ahmad Dedmari

Deep Learning based Image Reconstruction for Diffuse Optical Tomography - Hanene Ben Yedder

Sparse-View CT Reconstruction Using Wasserstein GANs - Franz Thaler

Improved Time-Resolved MRA using k-space Deep Learning - Eunju Cha

Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image - Chen Qin

A U-nets Cascade for Sparse View Computed Tomography - Andreas Kofler

All contributed talks have been allocated 15 minutes (12 minutes for the talk plus 3 minutes for questions). Please prepare your talk accordingly.

All poster spotlight presentations are 2 mins.