5 citations · 10 across the 16 of their papers we have counts for
13 papers · 1 filter
Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI
Yi Zhang, Yidong Zhao, Tijmen Toxopeus +3
Quantitative cardiac magnetic resonance imaging (MRI) enables non-invasive myocardial tissue characterization but relies on robust motion correction within these variable-length, v…
Adapting Frozen Mono-modal Backbones for Multi-modal Registration via Contrast-Agnostic Instance Optimization
Yi Zhang, Yidong Zhao, Qian Tao
Deformable image registration remains a central challenge in medical image analysis, particularly under multi-modal scenarios where intensity distributions vary significantly acros…
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Lasse Hansen, Wiebke Heyer, Christoph Großbröhmer +51
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the…
Bridging Classical and Learning-based Iterative Registration through Deep Equilibrium Models
Yi Zhang, Yidong Zhao, Qian Tao
Deformable medical image registration is traditionally formulated as an optimization problem. While classical methods solve this problem iteratively, recent learning-based approach…
Physics-Informed Neural ODEs for Temporal Dynamics Modeling in Cardiac T1 Mapping
Nuno Capitão, Yi Zhang, Yidong Zhao +1
Spin-lattice relaxation time () is an important biomarker in cardiac parametric mapping for characterizing myocardial tissue and diagnosing cardiomyopathies. Conventional Modi…
Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa +33
Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundati…
Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge
Fanwen Wang, Zi Wang, Yan Li +60
Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the {clinical reference standard} for diagnosing cardiovascular disea…
Lost in Tracking: Uncertainty-guided Cardiac Cine MRI Segmentation at Right Ventricle Base
Yidong Zhao, Yi Zhang, Orlando Simonetti +2
Accurate biventricular segmentation of cardiac magnetic resonance (CMR) cine images is essential for the clinical evaluation of heart function. However, compared to left ventricle…
Deep-learning-based groupwise registration for motion correction of cardiac mapping
Yi Zhang, Yidong Zhao, Lu Huang +2
Quantitative mapping by MRI is an increasingly important tool for clinical assessment of cardiovascular diseases. The cardiac map is derived by fitting a known signal m…
Recurrent Inference Machine for Medical Image Registration
Yi Zhang, Yidong Zhao, Hui Xue +3
Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent adva…
Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
Yidong Zhao, Yi Zhang, Qian Tao
Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previou…
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
Yidong Zhao, Joao Tourais, Iain Pierce +5
Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural networks (…
Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI
Xinqi Li, Yi Zhang, Yidong Zhao +2
Quantitative cardiac magnetic resonance imaging (MRI) is an increasingly important diagnostic tool for cardiovascular diseases. Yet, co-registration of all baseline images within t…