A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise
In recent years, there has been attention on leveraging the statistical modeling capabilities
of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) data …
of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) data …
Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning
Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs
deep neural networks to extract knowledge from available datasets and then applies the …
deep neural networks to extract knowledge from available datasets and then applies the …
De-aliasing and accelerated sparse magnetic resonance image reconstruction using fully dense CNN with attention gates
When sparsely sampled data are used to accelerate magnetic resonance imaging (MRI),
conventional reconstruction approaches produce significant artifacts that obscure the …
conventional reconstruction approaches produce significant artifacts that obscure the …
AI in MRI: Computational frameworks for a faster, optimized, and automated imaging workflow
Over the last decade, artificial intelligence (AI) has made an enormous impact on a wide
range of fields, including science, engineering, informatics, finance, and transportation. In …
range of fields, including science, engineering, informatics, finance, and transportation. In …
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets
Although deep learning (DL) methods are powerful for solving inverse problems, their
reliance on high-quality training data is a major hurdle. This is significant in high …
reliance on high-quality training data is a major hurdle. This is significant in high …
Machine learning for automatic Alzheimer's disease detection: addressing domain shift issues for building robust models
Alzheimer's disease (AD) is a type of brain disease that affects a person's ability to perform
daily tasks. Modern neuroimaging techniques have made it possible to detect structural and …
daily tasks. Modern neuroimaging techniques have made it possible to detect structural and …
The challenge of fetal cardiac MRI reconstruction using deep learning
Dynamic free-breathing fetal cardiac MRI is one of the most challenging modalities, which
requires high temporal and spatial resolution to depict rapid changes in a small fetal heart …
requires high temporal and spatial resolution to depict rapid changes in a small fetal heart …
Coarse–Super-Resolution–Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for
tumor motion management in image-guided radiation therapy (IGRT). However, current 4D …
tumor motion management in image-guided radiation therapy (IGRT). However, current 4D …
Adaptive Knowledge Distillation for High-Quality Unsupervised MRI Reconstruction with Model-Driven Priors
Z Wu, X Li - IEEE Journal of Biomedical and Health Informatics, 2024 - ieeexplore.ieee.org
Magnetic Resonance Imaging (MRI) reconstruction has made significant progress with the
introduction of Deep Learning (DL) technology combined with Compressed Sensing (CS) …
introduction of Deep Learning (DL) technology combined with Compressed Sensing (CS) …
Iterative Data Refinement for Self-Supervised Learning MR Image Reconstruction
X Liu, J Zou, T Sun, R Wu, X Zheng… - 2023 IEEE 20th …, 2023 - ieeexplore.ieee.org
Magnetic Resonance Imaging (MRI) is an important technique in the clinic. Fast MRI based
on k-space undersampling and high-quality image reconstruction has been widely utilized …
on k-space undersampling and high-quality image reconstruction has been widely utilized …