Compressed sensing MRI: a review from signal processing perspective
JC Ye - BMC Biomedical Engineering, 2019 - Springer
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
[HTML][HTML] From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction
Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components
Purpose To apply the low‐rank plus sparse (L+ S) matrix decomposition model to
reconstruct undersampled dynamic MRI as a superposition of background and dynamic …
reconstruct undersampled dynamic MRI as a superposition of background and dynamic …
Low-Rank Modeling of Local -Space Neighborhoods (LORAKS) for Constrained MRI
JP Haldar - IEEE transactions on medical imaging, 2013 - ieeexplore.ieee.org
Recent theoretical results on low-rank matrix reconstruction have inspired significant interest
in low-rank modeling of MRI images. Existing approaches have focused on higher …
in low-rank modeling of MRI images. Existing approaches have focused on higher …
T2 shuffling: Sharp, multicontrast, volumetric fast spin‐echo imaging
Purpose A new acquisition and reconstruction method called T2 Shuffling is presented for
volumetric fast spin‐echo (three‐dimensional [3D] FSE) imaging. T2 Shuffling reduces …
volumetric fast spin‐echo (three‐dimensional [3D] FSE) imaging. T2 Shuffling reduces …
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
Abstract Compressed sensing MRI (CS-MRI) has shown great potential in reducing data
acquisition time in MRI. Sparsity or compressibility plays an important role to reduce the …
acquisition time in MRI. Sparsity or compressibility plays an important role to reduce the …
Improved magnetic resonance fingerprinting reconstruction with low‐rank and subspace modeling
B Zhao, K Setsompop, E Adalsteinsson… - Magnetic resonance …, 2018 - Wiley Online Library
Purpose This article introduces a constrained imaging method based on low‐rank and
subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). Theory …
subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). Theory …
Accelerated MR parameter mapping with low‐rank and sparsity constraints
Purpose To enable accurate magnetic resonance (MR) parameter mapping with accelerated
data acquisition, utilizing recent advances in constrained imaging with sparse sampling …
data acquisition, utilizing recent advances in constrained imaging with sparse sampling …
Blind compressive sensing dynamic MRI
SG Lingala, M Jacob - IEEE transactions on medical imaging, 2013 - ieeexplore.ieee.org
We propose a novel blind compressive sensing (BCS) frame work to recover dynamic
magnetic resonance images from undersampled measurements. This scheme models the …
magnetic resonance images from undersampled measurements. This scheme models the …