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 …
[HTML][HTML] A review of optimization-based deep learning models for mri reconstruction
W Bian, YK Tamilselvam - AppliedMath, 2024 - mdpi.com
Magnetic resonance imaging (MRI) is crucial for its superior soft tissue contrast and high
spatial resolution. Integrating deep learning algorithms into MRI reconstruction has …
spatial resolution. Integrating deep learning algorithms into MRI reconstruction has …
Joint deep model-based MR image and coil sensitivity reconstruction network (joint-ICNet) for fast MRI
Magnetic resonance imaging (MRI) can provide diagnostic information with high-resolution
and high-contrast images. However, MRI requires a relatively long scan time compared to …
and high-contrast images. However, MRI requires a relatively long scan time compared to …
Diffusion modeling with domain-conditioned prior guidance for accelerated mri and qmri reconstruction
This study introduces a novel image reconstruction technique based on a diffusion model
that is conditioned on the native data domain. Our method is applied to multi-coil MRI and …
that is conditioned on the native data domain. Our method is applied to multi-coil MRI and …
[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 …
Physics-driven deep learning methods for fast quantitative magnetic resonance imaging: Performance improvements through integration with deep neural networks
Quantitative magnetic resonance imaging (qMRI) aims to obtain quantitative biophysical
parameters based on physical models derived from MR spin magnetization evolution. This …
parameters based on physical models derived from MR spin magnetization evolution. This …
Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI
Objectives An appropriate and fast clinical referral suggestion is important for intra-axial
mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable …
mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable …
Improving quantitative MRI using self‐supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping
Purpose This paper proposes a novel self‐supervised learning framework that uses model
reinforcement, REference‐free LAtent map eXtraction with MOdel REinforcement (RELAX …
reinforcement, REference‐free LAtent map eXtraction with MOdel REinforcement (RELAX …
Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning
Objectives To establish a robust interpretable multiparametric deep learning (DL) model for
automatic noninvasive grading of meningiomas along with segmentation. Methods In total …
automatic noninvasive grading of meningiomas along with segmentation. Methods In total …
Artificial intelligence in cardiac magnetic resonance fingerprinting
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for
multiparametric quantitative characterization of the tissues of interest in a single acquisition …
multiparametric quantitative characterization of the tissues of interest in a single acquisition …