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: Qian Tao is an Assistant Professor at the Department of Imaging Physics, Delft University of Technology. Her research interest includes cardiac magnetic resonance imaging (CMR), medical image analysis, and artificial intelligence (AI) for radiological applications. Dr. Tao has extensive experience collaborating with radiologists and cardiologists on clinical applications of CMR for diagnosis and intervention of cardiovascular disease patients. She serves on the Editorial Board of Radiology: Artificial Intelligence, and is a committee member of the Society of Cardiovascular Magnetic Resonance Registry.
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: Xiaobo Qu received his Ph.D. degree in communication engineering from Xiamen University, Xiamen, China, where he is a professor and the vice director of the Department of Electronic Science, the director of the Biomedical Intelligent Cloud Research and Development Center, and the vice director of the Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance. He received the Distinguished Young Scholar Award from the Natural Science Foundation of Fujian Province of China (2018), He Yici Chair Professor Award (2018) from Xiamen University, Excellent Young Scientists Fund Award from the National Natural Science Foundation of China (2021), First Prize in Natural Science Award, and Yunsheng Youth Science and Technology Award of Fujian Province of China (2022), and Liu Xiao Mathematical and Electronic Science Award from Xiamen University (2023). He is an associate editor of IEEE Transactions on Computational Imaging, a senior editor of BMC Medical Imaging, an associate editor of Magnetic Resonance Letters, etc. His research interests include magnetic resonance imaging and spectroscopy, computational imaging, machine learning, artificial intelligence, and cloud computing. Qu is a senior member of IEEE and a member of ISMRM.
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