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 …

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR
examinations more accessible. Prior arts including the deep learning models have been …

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 …

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 …

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 …

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 …

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 …

Purpose To improve the performance of non‐Cartesian partially parallel imaging (PPI) by
exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions …

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