Improving parallel imaging by jointly reconstructing multi‐contrast data
Purpose To develop parallel imaging techniques that simultaneously exploit coil sensitivity
encoding, image phase prior information, similarities across multiple images, and …
encoding, image phase prior information, similarities across multiple images, and …
Coupled dictionary learning for multi-contrast MRI reconstruction
Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-
weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These …
weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These …
Joint multi‐contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging
Purpose To improve the image quality of highly accelerated multi‐channel MRI data by
learning a joint variational network that reconstructs multiple clinical contrasts jointly …
learning a joint variational network that reconstructs multiple clinical contrasts jointly …
[HTML][HTML] A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine
Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in
visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple …
visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple …
Total generalized variation regularization for multi-modal electron tomography
In multi-modal electron tomography, tilt series of several signals such as X-ray spectra,
electron energy-loss spectra, annular dark-field, or bright-field data are acquired at the same …
electron energy-loss spectra, annular dark-field, or bright-field data are acquired at the same …
Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi‐channel multi‐contrast MRI acquisitions
Multi‐contrast images are commonly acquired together to maximize complementary
diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to …
diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to …
Alternating minimization algorithm for hybrid regularized variational image dehazing
Imaging quality is often significantly degraded under hazy weather condition. The purpose of
this paper is to recover the latent sharp image from its hazy version. It is well known that the …
this paper is to recover the latent sharp image from its hazy version. It is well known that the …
Joint calibrationless reconstruction of highly undersampled multicontrast MR datasets using a low‐rank Hankel tensor completion framework
Purpose To jointly reconstruct highly undersampled multicontrast two‐dimensional (2D)
datasets through a low‐rank Hankel tensor completion framework. Methods A multicontrast …
datasets through a low‐rank Hankel tensor completion framework. Methods A multicontrast …
PEARL: Cascaded Self-supervised Cross-fusion Learning For Parallel MRI Acceleration
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic
resonance imaging (AMRI) but is hampered by the reliance on extensive training data …
resonance imaging (AMRI) but is hampered by the reliance on extensive training data …
XSIM: A structural similarity index measure optimized for MRI QSM
Purpose The structural similarity index measure (SSIM) has become a popular quality metric
to evaluate QSM in a way that is closer to human perception than RMS error (RMSE) …
to evaluate QSM in a way that is closer to human perception than RMS error (RMSE) …