Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

L Feng, D Ma, F Liu - NMR in Biomedicine, 2022 - Wiley Online Library
Quantitative mapping of MR tissue parameters such as the spin‐lattice relaxation time (T1),
the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ) …

Single‐shot T2 mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network

C Cai, C Wang, Y Zeng, S Cai, D Liang… - Magnetic resonance …, 2018 - Wiley Online Library
Purpose An end‐to‐end deep convolutional neural network (CNN) based on deep residual
network (ResNet) was proposed to efficiently reconstruct reliable T2 mapping from single …

MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T2 Mapping With Severe Head Motion Using Overlapping-Echo Acquisition

Q Yang, Y Lin, J Wang, J Bao, X Wang… - … on Medical Imaging, 2022 - ieeexplore.ieee.org
Use of synthetic data has provided a potential solution for addressing unavailable or
insufficient training samples in deep learning-based magnetic resonance imaging (MRI) …

A simultaneous multi‐slice T2 mapping framework based on overlapping‐echo detachment planar imaging and deep learning reconstruction

S Li, J Wu, L Ma, S Cai, C Cai - Magnetic Resonance in …, 2022 - Wiley Online Library
Purpose Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific
research. However, most qMRI techniques are time‐consuming and sensitive to motion …

[HTML][HTML] Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging

L Ma, J Wu, Q Yang, Z Zhou, H He, J Bao, L Bao… - Neuroimage, 2022 - Elsevier
Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the
characterization of multiple tissue properties non-invasively and has shown great potential …

Physics-driven deep learning methods for fast quantitative magnetic resonance imaging: Performance improvements through integration with deep neural networks

Y Zhu, J Cheng, ZX Cui, Q Zhu, L Ying… - IEEE Signal …, 2023 - ieeexplore.ieee.org
Quantitative magnetic resonance imaging (qMRI) aims to obtain quantitative biophysical
parameters based on physical models derived from MR spin magnetization evolution. This …

Robust Single-Shot T2 Mapping via Multiple Overlapping-Echo Acquisition and Deep Neural Network

J Zhang, J Wu, S Chen, Z Zhang, S Cai… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis
and scientific research. However, most MRI experiments remain qualitative, especially …

Single‐shot T2 mapping via multi‐echo‐train multiple overlapping‐echo detachment planar imaging and multitask deep learning

B Ouyang, Q Yang, X Wang, H He, L Ma… - Medical …, 2022 - Wiley Online Library
Background Quantitative magnetic resonance imaging provides robust biomarkers in clinics.
Nevertheless, the lengthy scan time reduces imaging throughput and increases the …

Towards Better Generalization Using Synthetic Data: A Domain Adaptation Framework for T2 Mapping via Multiple Overlapping-Echo Acquisition

C Zhang, Q Yang, L Fan, S Yu, L Sun… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The generation of synthetic data using physics-based modeling provides a solution to
limited or lacking real-world training samples in deep learning methods for rapid quantitative …

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

H Huang, Q Yang, J Wang, P Zhang… - Physics in Medicine & …, 2023 - iopscience.iop.org
Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging
(MRI) development. However, even with the graphics processing unit (GPU) acceleration …