Program
Regular papers (morning session)
09:00-09:10 | Welcome |
09:10-10:10 | Oral Session |
09:10-09:20 | Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy Laura Alvarez Florez, Jörg Sander, Mimount Bourfiss, Fleur V. Y. Tjong, Birgitta K. Velthuis, Ivana Išgum |
09:20-09:30 | Deep Conditional Shape Models for 3D cardiac image segmentation Athira J Jacob, Puneet Sharma, Daniel Ruckert |
09:30-09:40 | Sparse annotation strategies for segmentation of short axis cardiac MRI Josh Stein, Maxime Di Folco, Julia A. Schnabel |
09:40-09:50 | Impact of catheter orientation on cardiac radiofrequency ablation Massimiliano Leoni, Argyrios Petras, Zoraida Moreno Weidmann, Jose M Guerra, Luca Gerardo Giorda |
09:50-10:00 | Effects of Fibrotic Border Zone on Drivers for Atrial Fibrillation: An In-Silico Mechanistic Investigation Shaheim Ogbomo-Harmitt, George Obada, Nele Vandersickel, Andy King, Oleg Aslanidi |
10:00-10:10 | Automated Coronary Vessels Segmentation in X-ray Angiography Using Graph Attention Network Haorui He, Abhirup Banerjee, Robin Choudhury, Vicente Grau |
10:10-10:15 | Coffee break |
10:15-11:00 | Keynote: Dr. Qian Tao - Delft University of Technology, the Netherlands Title: Artificial Intelligence for Cardiovascular Magnetic Resonance Imaging Cardiovascular magnetic resonance imaging (CMR) is the current gold standard imaging modality for heart, providing in-depth information on cardiac structure, motion, tissue properties etc. However, CMR is traditionally an expensive examination due to its slow imaging and complicated analysis. In recent years, artificial intelligence (AI) has made significant advancements to improve the accessibility of CMR through accelerated acquisition and automated reporting. We will discuss the advancements of AI in the field of CMR, current challenges, and opportunities for future. Short bio: ![]() |
11:00-11:15 | Poster teaser |
11:15-12:15 | Poster session (see list of accepted papers) |
12:15-12:30 | Prizes and closing remarks + intro to CMRxRecon in the afternoon |
CMRxRecon challenge (afternoon session)
14:00-14:25 | Welcome and opening remarks |
14:25-14:50 | Keynote: Prof. Xiaobo Qu – Xiamen University, China Title: Physics-Driven Synthetic Data Artificial Intelligence for Biomedical Magnetic Resonance Deep learning has driven innovation in the field of biomedical magnetic resonance, but encounters the bottleneck of insufficient or even unavailable training data. In this talk, we will discuss an emerging paradigm, imaging physics-based data synthesis for network training. Signal generation models, enhanced data generation and advanced neural network structures with representative applications, particularly on fast, parametric and cardiac magnetic resonance imaging, are discussed. Short bio: ![]() |
14:50-15:10 | Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction Bingyu Xin, Meng Ye, Leon Axel, Dimitris N. Metaxas |
15:10-15:30 | Deep Cardiac MRI Reconstruction with ADMM George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen |
15:30-15:50 | Coffee break + poster session (see list of accepted papers) |
15:50-16:10 | k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction Liping Zhang, Weitian Chen |
16:10-16:30 | Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction Yidong Zhao; Yi Zhang; Qian Tao |
16:30-16:50 | Prizes and closing remarks |