AI-based reconstruction for fast MRI—A systematic review and meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
Adaptive diffusion priors for accelerated MRI reconstruction
Deep MRI reconstruction is commonly performed with conditional models that de-alias
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
Learning to optimize: A primer and a benchmark
Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …
develop optimization methods, aiming at reducing the laborious iterations of hand …
Unsupervised MRI reconstruction via zero-shot learned adversarial transformers
Supervised reconstruction models are characteristically trained on matched pairs of
undersampled and fully-sampled data to capture an MRI prior, along with supervision …
undersampled and fully-sampled data to capture an MRI prior, along with supervision …
Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning
Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled
data is important in many clinical applications. In recent years, deep learning-based …
data is important in many clinical applications. In recent years, deep learning-based …
End-to-end variational networks for accelerated MRI reconstruction
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the
development of two complementary methods: acquiring multiple views of the anatomy …
development of two complementary methods: acquiring multiple views of the anatomy …
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data
B Yaman, SAH Hosseini, S Moeller… - Magnetic resonance …, 2020 - Wiley Online Library
Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
Federated learning of generative image priors for MRI reconstruction
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit
privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …
privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …
Deep learning for seismic inverse problems: Toward the acceleration of geophysical analysis workflows
Seismic inversion is a fundamental tool in geophysical analysis, providing a window into
Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for …
Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for …