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 …
Machine learning in magnetic resonance imaging: image reconstruction
Abstract Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management
and monitoring of many diseases. However, it is an inherently slow imaging technique. Over …
and monitoring of many diseases. However, it is an inherently slow imaging technique. Over …
SDC-UDA: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation
Recent advances in deep learning-based medical image segmentation studies achieve
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
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 …
One-dimensional deep low-rank and sparse network for accelerated MRI
Deep learning has shown astonishing performance in accelerated magnetic resonance
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …
Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer
Objectives To analyze whether CT image normalization can improve 3-year recurrence-free
survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) …
survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) …
Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method
Quantitative tissue characteristics, which provide valuable diagnostic information, can be
represented by magnetic resonance (MR) parameter maps using magnetic resonance …
represented by magnetic resonance (MR) parameter maps using magnetic resonance …
Accelerated MRI reconstruction with separable and enhanced low-rank Hankel regularization
Magnetic resonance imaging serves as an essential tool for clinical diagnosis, however,
suffers from a long acquisition time. Sparse sampling effectively saves this time but images …
suffers from a long acquisition time. Sparse sampling effectively saves this time but images …
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 …
Radial magnetic resonance image reconstruction with a deep unrolled projected fast iterative soft-thresholding network
B Qu, J Zhang, T Kang, J Lin, M Lin, H She… - Computers in Biology …, 2024 - Elsevier
Radially sampling of magnetic resonance imaging (MRI) is an effective way to accelerate the
imaging. How to preserve the image details in reconstruction is always challenging. In this …
imaging. How to preserve the image details in reconstruction is always challenging. In this …