7 papers
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…
Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRI
George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
Dynamic MRI enables a range of clinical applications, including cardiac function assessment, organ motion tracking, and radiotherapy guidance. However, fully sampling the dynamic k…
End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
Accelerating dynamic MRI is vital for advancing clinical applications and improving patient comfort. Commonly, deep learning (DL) methods for accelerated dyn…
Joint Supervised and Self-supervised Learning for MRI Reconstruction
George Yiasemis, Nikita Moriakov, Clara I. Sánchez +2
Magnetic Resonance Imaging (MRI) represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully-sampled -space…
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Cardiac MRI (CMRI) is a cornerstone imaging modality that provides in-depth insights into cardiac structure and function. Multi-contrast CMRI (MCCMRI), which acquires sequences wit…
vSHARP: variable Splitting Half-quadratic Admm algorithm for Reconstruction of inverse-Problems
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke +1
Medical Imaging (MI) tasks, such as accelerated parallel Magnetic Resonance Imaging (MRI), often involve reconstructing an image from noisy or incomplete measurements. This amounts…