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 …
Data-and physics-driven deep learning based reconstruction for fast mri: Fundamentals and methodologies
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended
scanning times often compromise patient comfort and image quality, especially in …
scanning times often compromise patient comfort and image quality, especially in …
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 …
Experimental design for MRI by greedy policy search
In today's clinical practice, magnetic resonance imaging (MRI) is routinely accelerated
through subsampling of the associated Fourier domain. Currently, the construction of these …
through subsampling of the associated Fourier domain. Currently, the construction of these …
Learning-Augmented -means Clustering
$ k $-means clustering is a well-studied problem due to its wide applicability. Unfortunately,
there exist strong theoretical limits on the performance of any algorithm for the $ k $-means …
there exist strong theoretical limits on the performance of any algorithm for the $ k $-means …
Fast data-driven learning of parallel MRI sampling patterns for large scale problems
In this study, a fast data-driven optimization approach, named bias-accelerated subset
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …
AutoSamp: autoencoding k-space sampling via variational information maximization for 3D MRI
Accelerated MRI protocols routinely involve a predefined sampling pattern that
undersamples the k-space. Finding an optimal pattern can enhance the reconstruction …
undersamples the k-space. Finding an optimal pattern can enhance the reconstruction …
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 …
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model
We introduce a generative smoothness regularization on manifolds (SToRM) model for the
recovery of dynamic image data from highly undersampled measurements. The model …
recovery of dynamic image data from highly undersampled measurements. The model …