Model-based image reconstruction for MRI
JA Fessler - IEEE signal processing magazine, 2010 - ieeexplore.ieee.org
Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging
modality. The inverse FFT has served the MR community very well as the conventional …
modality. The inverse FFT has served the MR community very well as the conventional …
ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA
Purpose Parallel imaging allows the reconstruction of images from undersampled multicoil
data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and …
data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and …
Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm
Water/fat separation is a classical problem for in vivo proton MRI. Although many methods
have been proposed to address this problem, robust water/fat separation remains a …
have been proposed to address this problem, robust water/fat separation remains a …
Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic -Minimization
In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of
potential benefits ranging from high-temporal-rate observation of physiological processes to …
potential benefits ranging from high-temporal-rate observation of physiological processes to …
P‐LORAKS: low‐rank modeling of local k‐space neighborhoods with parallel imaging data
Purpose To propose and evaluate P‐LORAKS a new calibrationless parallel imaging
reconstruction framework. Theory and Methods LORAKS is a flexible and powerful …
reconstruction framework. Theory and Methods LORAKS is a flexible and powerful …
Parallel MR image reconstruction using augmented Lagrangian methods
S Ramani, JA Fessler - IEEE transactions on medical imaging, 2010 - ieeexplore.ieee.org
Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE)
requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity …
requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity …
A Majorize-Minimize Subspace Approach for Image Regularization
In this work, we consider a class of differentiable criteria for sparse image computing
problems, where a nonconvex regularization is applied to an arbitrary linear transform of the …
problems, where a nonconvex regularization is applied to an arbitrary linear transform of the …
Scan-specific generative neural network for MRI super-resolution reconstruction
The interpretation and analysis of Magnetic resonance imaging (MRI) benefit from high
spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time …
spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time …
A comparative simulation study of bayesian fitting approaches to intravoxel incoherent motion modeling in diffusion‐weighted MRI
PT While - Magnetic resonance in medicine, 2017 - Wiley Online Library
Purpose To assess the performance of various least squares and Bayesian modeling
approaches to parameter estimation in intravoxel incoherent motion (IVIM) modeling of …
approaches to parameter estimation in intravoxel incoherent motion (IVIM) modeling of …
Anatomically constrained reconstruction from noisy data
Noise is a major concern in many important imaging applications. To improve data signal‐to‐
noise ratio (SNR), experiments often focus on collecting low‐frequency k‐space data. This …
noise ratio (SNR), experiments often focus on collecting low‐frequency k‐space data. This …