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Found 9 papers, 1 papers with code
Accelerated Parallel Magnetic Resonance Imaging with Compressed Sensing using Structured Sparsity
Compressed sensing allows one to reconstruct a high-quality image with fewer samples which can be collected with a faster scan.
The Dependence of Parallel Imaging with Linear Predictability on the Undersampling Direction
Parallel imaging with linear predictability takes advantage of information present in multiple receive coils to accurately reconstruct the image with fewer samples.
Physics-driven Deep Learning for PET/MRI
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease.
Utilizing the Structure of the Curvelet Transform with Compressed Sensing
The curvelet representation is approximately sparse; thus, it is a useful sparsifying transformation to be used with compressed sensing.
Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform
The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities.
Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization
In this manuscript, we present a generalization of several existing iterative model based algorithms.
Fast Variable Density Poisson-Disc Sample Generation with Directional Variation
We present a fast method for generating random samples according to a variable density Poisson-disc distribution.
Di-chromatic Interpolation of Magnetic Resonance Metabolic Imagery
Magnetic resonance imaging with hyperpolarized contrast agents can provide unprecedented \textit{in-vivo} measurements of metabolism, but yields images that are lower resolution than that achieved with proton anatomical imaging.
Utilizing the Wavelet Transform's Structure in Compressed Sensing
In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result.
