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[HTML][HTML] Deep learning-based rigid motion correction for magnetic resonance imaging: a survey
Physiological and physical motions of the subjects, eg, patients, are the primary sources of
image artifacts in magnetic resonance imaging (MRI), causing geometric distortion, blurring …
image artifacts in magnetic resonance imaging (MRI), causing geometric distortion, blurring …
Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence
Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
Deep learning for accelerated and robust MRI reconstruction
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
Accelerated pure shift NMR spectroscopy with deep learning
H Zhan, J Liu, Q Fang, X Chen, L Hu - Analytical Chemistry, 2024 - ACS Publications
Pure shift nuclear magnetic resonance (NMR) spectroscopy presents a promising solution to
provide sufficient spectral resolution and has been increasingly applied in various branches …
provide sufficient spectral resolution and has been increasingly applied in various branches …
A user independent denoising method for x‐nuclei MRI and MRS
Purpose X‐nuclei (also called non‐proton MRI) MRI and spectroscopy are limited by the
intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x …
intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x …
Deep learning for accelerated and robust MRI reconstruction: a review
R Heckel, M Jacob, A Chaudhari, O Perlman… - ar** based on multiple overlap**-echo detachment (MOLED) imaging
Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the
characterization of multiple tissue properties non-invasively and has shown great potential …
characterization of multiple tissue properties non-invasively and has shown great potential …
Towards Better Generalization Using Synthetic Data: A Domain Adaptation Framework for T2 Map** via Multiple Overlap**-Echo Acquisition
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 …
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 …
(MRI) development. However, even with the graphics processing unit (GPU) acceleration …