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 …

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 …

Parallel MRI using phased array coils can be viewed as an application of the multichannel
sampling theory. Specifically, in the case of uniform 1-D undersampling, Papoulis' classical …

A deep learning (DL) method for accelerated magnetic resonance (MR) imaging is
presented that incorporates domain knowledge of parallel MR imaging to augment the DL …

A deep learning framework is presented that transforms the task of MR image reconstruction
from randomly undersampled k-space data into pixel classification. A DL network was …

Autocalibration signal is acquired in the k-space-based parallel MRI reconstruction for
estimating interpolation coefficients and reconstructing missing unacquired data. Many ACS …

Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated
virtual coils - IOPscience This site uses cookies. By continuing to use this site you agree to our …

In this paper, a novel sensitivity mapping method is proposed for the image domain parallel
MRI (pMRI) technique. Instead of refining raw sensitivity maps by means of conventional …

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 …

In conventional generalized autocalibrating partially parallel acquisitions, the autocalibration
signal (ACS) lines are acquired with a frequency‐encoding direction in parallel to other …