Quantitative magnetic resonance imaging of brain anatomy and in vivo histology
Quantitative magnetic resonance imaging (qMRI) goes beyond conventional MRI, which
aims primarily at local image contrast. It provides specific physical parameters related to the …
aims primarily at local image contrast. It provides specific physical parameters related to the …
Deep learning techniques for inverse problems in imaging
Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …
wide variety of inverse problems arising in computational imaging. We explore the central …
Systematic review of reconstruction techniques for accelerated quantitative MRI
Purpose To systematically review the techniques that address undersampling artifacts in
accelerated quantitative magnetic resonance imaging (qMRI). Methods A literature search …
accelerated quantitative magnetic resonance imaging (qMRI). Methods A literature search …
Learning regularization parameter-maps for variational image reconstruction using deep neural networks and algorithm unrolling
We introduce a method for the fast estimation of data-adapted, spatially and temporally
dependent regularization parameter-maps for variational image reconstruction, focusing on …
dependent regularization parameter-maps for variational image reconstruction, focusing on …
A fetal brain magnetic resonance acquisition numerical phantom (FaBiAN)
Accurate characterization of in utero human brain maturation is critical as it involves complex
and interconnected structural and functional processes that may influence health later in life …
and interconnected structural and functional processes that may influence health later in life …
CPP-Net: Embracing Multi-Scale Feature Fusion into Deep Unfolding CP-PPA Network for Compressive Sensing
Z Guo, H Gan - Proceedings of the IEEE/CVF Conference …, 2024 - openaccess.thecvf.com
In the domain of compressive sensing (CS) deep unfolding networks (DUNs) have garnered
attention for their good performance and certain degree of interpretability rooted in CS …
attention for their good performance and certain degree of interpretability rooted in CS …
Quantitative MR image reconstruction using parameter-specific dictionary learning with adaptive dictionary-size and sparsity-level choice
Objective: We propose a method for the reconstruction of parameter-maps in Quantitative
Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter …
Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter …
MRI reconstruction with side information using diffusion models
Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted
images of the same anatomy to emphasize different tissue properties. Due to the long …
images of the same anatomy to emphasize different tissue properties. Due to the long …
Dual‐excitation flip‐angle simultaneous cine and T1 mapping using spiral acquisition with respiratory and cardiac self‐gating
Purpose To develop a free‐breathing cardiac self‐gated technique that provides cine
images and slice profile–corrected T1 maps from a single acquisition. Methods Without …
images and slice profile–corrected T1 maps from a single acquisition. Methods Without …
Magnetic resonance imaging reconstruction via non‐convex total variation regularization
M Shen, J Li, T Zhang, J Zou - International Journal of Imaging …, 2021 - Wiley Online Library
Magnetic resonance imaging (MRI) reconstruction model based on total variation (TV)
regularization can deal with problems such as incomplete reconstruction, blurred boundary …
regularization can deal with problems such as incomplete reconstruction, blurred boundary …