Preliminary program (the timings are still subject to change)

Session 1

08:00 Keynote: Deep Fast MR Imaging: when Compressed Sensing meets Deep Neural Network. Dong Liang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

08:45 Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction. Balamurali Murugesan (Indian Institute of Technology, Madras)

08:55 Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging. Tong Zhang (King's College London)

09:05 APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network. Chaoping Zhang (Erasmus MC - University Medical Center Rotterdam)

09:15 Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network. Guanhua Wang (Tsinghua University)

09:25 Deep learning for Low-Field to High-Field MR Image Quality Transfer with Probabilistic Decimation Simulator. Hongxiang Lin (University College London)

09:35 Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. Patricia M Johnson (New York University)

09:45 Discussions and Q&A

Coffee break

Session 2

10:30 Keynote: Regularizing Inverse Problems with Deep Neural Networks. Markus Haltmaier, University of Innsbruck

11:15 Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval. Akira Kudo (Fujifilm corporation)

11:25 Flexible Conditional Image Generation of Missing Data with Learned Mental Maps. Benjamin Hou (Imperial College London)

11:35 Stain Style Transfer using Transitive Adversarial Networks. Shaojin Cai (Fuzhou University)

11:45 Task-GAN: Improving Generative Adversarial Network for Image Reconstruction. Jiahong Ouyang (Stanford University)

11:55 TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis. Chengjia Wang (University of Edinburgh)

12:05 Modeling and Analysis Brain Development with Discriminative Dictionary Learning. Mingli Zhang (Montreal Neurological Institute, Mcgill University)

12:15 Discussion and Q&A

Lunch break

Session 3

13:30 Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation. Camille Pouchol (KTH)

13:40 Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Networks. Sahar Yousefi (Leiden University Medical Center)

13:50 Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior. Yixing Huang (Friedrich-Alexander-Universität Erlangen-Nürnberg)

14:00 Deep Learning based approach to quantification of PET tracer uptake in small tumors. Laura Dal Toso (King's College London)

14:10 Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks. Mayank Patwari (Siemens Healthineers)

14:20 Deep Learning based Metal Inpainting in the Projection Domain: Initial Results. Tristan Gottschalk (Friedrich-Alexander-Universität Erlangen-Nürnberg)

14:30 Discussion and Q&A

Coffee break

Session 4

15:00 Keynote: Learning Model-Based Image Reconstruction for X-Ray CT. Yong Long (Shanghai Jiao Tong University)

15:45 Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer. Boa Kim (KAIST)

15:55 Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data. Peter A. von Niederhäusern (DBE, University of Basel)

16:05 Neural Denoising of Ultra-Low Dose Mammography. Michael Green (Tel-Aviv University)

16:15 Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging. Alberto Gomez (King's College London)

16:25 Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy. Saeed Izadi (Simon Fraser University)

16:35 PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction. Farah Deeba (UBC)

16:45 Discussion and Q&A