Deep-learning-based optimization of the under-sampling pattern in MRI
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …
B-spline parameterized joint optimization of reconstruction and k-space trajectories (bjork) for accelerated 2d mri
Optimizing k-space sampling trajectories is a promising yet challenging topic for fast
magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method …
magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method …
PILOT: Physics-informed learned optimized trajectories for accelerated MRI
Magnetic Resonance Imaging (MRI) has long been considered to be among" the gold
standards" of diagnostic medical imaging. The long acquisition times, however, render MRI …
standards" of diagnostic medical imaging. The long acquisition times, however, render MRI …
J-MoDL: Joint model-based deep learning for optimized sampling and reconstruction
HK Aggarwal, M Jacob - IEEE journal of selected topics in …, 2020 - ieeexplore.ieee.org
Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to
recover MRI data from undersampled multichannel Fourier measurements, are widely used …
recover MRI data from undersampled multichannel Fourier measurements, are widely used …
Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
While enabling accelerated acquisition and improved reconstruction accuracy, current deep
MRI reconstruction networks are typically supervised, require fully sampled data, and are …
MRI reconstruction networks are typically supervised, require fully sampled data, and are …
[HTML][HTML] The road to breast cancer screening with diffusion MRI
M Iima, D Le Bihan - Frontiers in oncology, 2023 - frontiersin.org
Breast cancer is the leading cause of cancer in women with a huge medical, social and
economic impact. Mammography (MMG) has been the gold standard method until now …
economic impact. Mammography (MMG) has been the gold standard method until now …
Deep learning for accelerated and robust MRI reconstruction: a review
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
[HTML][HTML] Benchmarking MRI reconstruction neural networks on large public datasets
Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance
Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard …
Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard …
[HTML][HTML] Jointly Learning Non-Cartesian k-Space Trajectories and Reconstruction Networks for 2D and 3D MR Imaging through Projection
CG Radhakrishna, P Ciuciu - Bioengineering, 2023 - mdpi.com
Compressed sensing in magnetic resonance imaging essentially involves the optimization
of (1) the sampling pattern in k-space under MR hardware constraints and (2) image …
of (1) the sampling pattern in k-space under MR hardware constraints and (2) image …
Optimizing full 3d sparkling trajectories for high-resolution magnetic resonance imaging
GR Chaithya, P Weiss, G Daval-Frérot… - … on Medical Imaging, 2022 - ieeexplore.ieee.org
The Spreading Projection Algorithm for Rapid K-space sampLING, or SPARKLING, is an
optimization-driven method that has been recently introduced for accelerated 2D MRI using …
optimization-driven method that has been recently introduced for accelerated 2D MRI using …