Deep learning-based lung image registration: A review
H ** to solve series problems in clinical applications. Since the lungs are soft and fairly …
Cardiac MR: from theory to practice
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality,
causing over 17. 9 million deaths worldwide per year with associated costs of over $800 …
causing over 17. 9 million deaths worldwide per year with associated costs of over $800 …
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging
Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
Deep learning‐based motion quantification from k‐space for fast model‐based magnetic resonance imaging motion correction
Background Intra‐scan rigid‐body motion is a costly and ubiquitous problem in clinical
magnetic resonance imaging (MRI) of the head. Purpose State‐of‐the‐art methods for …
magnetic resonance imaging (MRI) of the head. Purpose State‐of‐the‐art methods for …
Multimodal MRI reconstruction assisted with spatial alignment network
K Xuan, L ** and cine MRI using low‐rank reconstruction with non‐rigid cardiac motion correction
Purpose To introduce non‐rigid cardiac motion correction into a novel free‐running
framework for the simultaneous acquisition of 3D whole‐heart myocardial T 1 T _1 and T 2 T …
framework for the simultaneous acquisition of 3D whole‐heart myocardial T 1 T _1 and T 2 T …
Unrolled and rapid motion-compensated reconstruction for cardiac CINE MRI
Abstract In recent years Motion-Compensated MR reconstruction (MCMR) has emerged as a
promising approach for cardiac MR (CMR) imaging reconstruction. MCMR estimates cardiac …
promising approach for cardiac MR (CMR) imaging reconstruction. MCMR estimates cardiac …
[PDF][PDF] Physics-driven deep learning for computational magnetic resonance imaging
Physics-driven deep learning methods have emerged as a powerful tool for computational
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new …
Reconstruction-driven motion estimation for motion-compensated MR CINE imaging
In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach
to address highly undersampled acquisitions by incorporating motion information between …
to address highly undersampled acquisitions by incorporating motion information between …