Statistical analysis of noise in MRI
S Aja-Fernández, G Vegas-Sánchez-Ferrero - Switzerland: Springer …, 2016 - Springer
This work is the result of more than 10 years of research in the area of MRI from a signal and
noise perspective. Our interest has always been to properly model the noise that affects our …
noise perspective. Our interest has always been to properly model the noise that affects our …
Nonlinear GRAPPA: A kernel approach to parallel MRI reconstruction
GRAPPA linearly combines the undersampled k‐space signals to estimate the missing k‐
space signals where the coefficients are obtained by fitting to some auto‐calibration signals …
space signals where the coefficients are obtained by fitting to some auto‐calibration signals …
K-space and image domain collaborative energy-based model for parallel MRI reconstruction
Z Tu, C Jiang, Y Guan, J Liu, Q Liu - Magnetic Resonance Imaging, 2023 - Elsevier
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR
examinations more accessible. Prior arts including the deep learning models have been …
examinations more accessible. Prior arts including the deep learning models have been …
WKGM: weighted k‐space generative model for parallel imaging reconstruction
Z Tu, D Liu, X Wang, C Jiang, P Zhu… - NMR in …, 2023 - Wiley Online Library
Deep learning based parallel imaging (PI) has made great progress in recent years to
accelerate MRI. Nevertheless, it still has some limitations: for example, the robustness and …
accelerate MRI. Nevertheless, it still has some limitations: for example, the robustness and …
Improving accelerated MRI by deep learning with sparsified complex data
Z Jin, QS Xiang - Magnetic Resonance in Medicine, 2023 - Wiley Online Library
Purpose To obtain high‐quality accelerated MR images with complex‐valued reconstruction
from undersampled k‐space data. Methods The MRI scans from human subjects were …
from undersampled k‐space data. Methods The MRI scans from human subjects were …
MultiNet PyGRAPPA: Multiple neural networks for reconstructing variable density GRAPPA (a 1H FID MRSI study)
S Nassirpour, P Chang, A Henning - NeuroImage, 2018 - Elsevier
Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool for mapping metabolite
levels across the brain, however, it generally suffers from long scan times. This severely …
levels across the brain, however, it generally suffers from long scan times. This severely …
Partial fourier reconstruction through data fitting and convolution in k‐space
A partial Fourier acquisition scheme has been widely adopted for fast imaging. There are
two problems associated with the existing techniques. First, the majority of the existing …
two problems associated with the existing techniques. First, the majority of the existing …
Group feature selection for enhancing information gain in MRI reconstruction
Y Chang, M Saritac - Physics in Medicine & Biology, 2022 - iopscience.iop.org
Magnetic resonance imaging (MRI) has revolutionized radiology. As a leading medical
imaging modality, MRI not only visualizes the structures inside the body but also produces …
imaging modality, MRI not only visualizes the structures inside the body but also produces …
An improved non‐Cartesian partially parallel imaging by exploiting artificial sparsity
Purpose To improve the performance of non‐Cartesian partially parallel imaging (PPI) by
exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions …
exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions …
Improving GRAPPA using cross‐sampled autocalibration data
In conventional generalized autocalibrating partially parallel acquisitions, the autocalibration
signal (ACS) lines are acquired with a frequency‐encoding direction in parallel to other …
signal (ACS) lines are acquired with a frequency‐encoding direction in parallel to other …