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 …
Region-focused multi-view transformer-based generative adversarial network for cardiac cine MRI reconstruction
Cardiac cine magnetic resonance imaging (MRI) reconstruction is challenging due to spatial
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …
CMRxRecon: an open cardiac MRI dataset for the competition of accelerated image reconstruction
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for
cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes …
cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes …
Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI
Purpose To introduce a dual‐domain reconstruction network with V‐Net and K‐Net for
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …
Implicit neural networks with fourier-feature inputs for free-breathing cardiac MRI reconstruction
JF Kunz, S Ruschke, R Heckel - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Cardiacmagnetic resonance imaging (MRI) requires reconstructing a real-time video of a
beating heart from continuous highly under-sampled measurements. This task is …
beating heart from continuous highly under-sampled measurements. This task is …
The state-of-the-art in cardiac MRI reconstruction: results of the CMRxRecon challenge in MICCAI 2023
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow
imaging and motion artifacts. Undersampling reconstruction, especially data-driven …
imaging and motion artifacts. Undersampling reconstruction, especially data-driven …
Movienet: Deep space–time‐coil reconstruction network without k‐space data consistency for fast motion‐resolved 4D MRI
Purpose To develop a novel deep learning approach for 4D‐MRI reconstruction, named
Movienet, which exploits space–time‐coil correlations and motion preservation instead of k …
Movienet, which exploits space–time‐coil correlations and motion preservation instead of k …
Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging
Motion-compensated MR reconstruction (MCMR) is a powerful concept with considerable
potential, consisting of two coupled sub-problems: Motion estimation, assuming a known …
potential, consisting of two coupled sub-problems: Motion estimation, assuming a known …
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 …
CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for
cardiac diseases. However, a significant drawback of CMR is its slow imaging speed …
cardiac diseases. However, a significant drawback of CMR is its slow imaging speed …