[HTML][HTML] MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping
Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying
tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse …
tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse …
[HTML][HTML] Quantitative susceptibility mapping through model-based deep image prior (MoDIP)
The data-driven approach of supervised learning methods has limited applicability in solving
dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters …
dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters …
[HTML][HTML] Affine transformation edited and refined deep neural network for quantitative susceptibility mapping
Deep neural networks have demonstrated great potential in solving dipole inversion for
Quantitative Susceptibility Mapping (QSM). However, the performances of most existing …
Quantitative Susceptibility Mapping (QSM). However, the performances of most existing …
[HTML][HTML] Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks
Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue
magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) …
magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) …
[HTML][HTML] Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks
Quantitative susceptibility mapping (QSM) is an MRI post-processing technique that
produces spatially resolved magnetic susceptibility maps from phase data. However, the …
produces spatially resolved magnetic susceptibility maps from phase data. However, the …
[HTML][HTML] Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset
Recently, deep neural networks have shown great potential for solving dipole inversion of
quantitative susceptibility mapping (QSM) with improved results. However, these studies …
quantitative susceptibility mapping (QSM) with improved results. However, these studies …
[HTML][HTML] Deepsti: towards tensor reconstruction using fewer orientations in susceptibility tensor imaging
Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging technique
that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor …
that characterizes the anisotropic tissue magnetic susceptibility with a second-order tensor …
What's in a Prior? Learned Proximal Networks for Inverse Problems
Proximal operators are ubiquitous in inverse problems, commonly appearing as part of
algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep …
algorithmic strategies to regularize problems that are otherwise ill-posed. Modern deep …
A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation
Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the
underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based …
underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based …
A spatially adaptive regularization based three-dimensional reconstruction network for quantitative susceptibility mapping
L Bao, H Zhang, Z Liao - Physics in Medicine & Biology, 2024 - iopscience.iop.org
Objective. Quantitative susceptibility mapping (QSM) is a new imaging technique for non-
invasive characterization of the composition and microstructure of in vivo tissues, and it can …
invasive characterization of the composition and microstructure of in vivo tissues, and it can …