Search Results for author:
Found 25 papers, 7 papers with code
SRE-CNN: A Spatiotemporal Rotation-Equivariant CNN for Cardiac Cine MR Imaging
However, current equivariant CNN methods fail to fully exploit these symmetry priors in dynamic MR imaging.
LINEAR: Learning Implicit Neural Representation With Explicit Physical Priors for Accelerated Quantitative T1rho Mapping
In this study, a novel subject-specific unsupervised method based on the implicit neural representation is proposed to reconstruct T1rho-weighted images from highly undersampled k-space data, which only takes spatiotemporal coordinates as the input.
Learning Stable and Passive Neural Differential Equations
We take a recently proposed Polyak Lojasiewicz network (PLNet) as an Lyapunov function and then parameterize the vector field as the descent directions of the Lyapunov function.
Proximal Gradient Descent Unfolding Dense-spatial Spectral-attention Transformer for Compressive Spectral Imaging
PGDUDST requires only 58% of the training time of RDLUF-MixS^2-9stg to achieve comparable reconstruction results.
Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.
RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
Neural networks are typically sensitive to small input perturbations, leading to unexpected or brittle behaviour.
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI
We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable.
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method.
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction.
Deep unfolding as iterative regularization for imaging inverse problems
Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems.
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction
Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction.
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.
K-UNN: k-Space Interpolation With Untrained Neural Network
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data.
Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)
Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR T1\r{ho} imaging.
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI
Specifically, focusing on accelerated MRI, we unroll a zeroth-order algorithm, of which the network module represents the regularizer itself, so that the network output can be still covered by the regularization model.
SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem.
Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multiple Data Sources
Accordingly, a full-coverage high-resolution air pollutant dataset with timely updates and historical long-term records is essential to support both research and environmental management.
Deep Manifold Learning for Dynamic MR Imaging
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
Deep Low-rank plus Sparse Network for Dynamic MR Imaging
However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods.
Deep Low-rank Prior in Dynamic MR Imaging
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
An Unsupervised Deep Learning Method for Multi-coil Cine MRI
Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied.
Model Learning: Primal Dual Networks for Fast MR imaging
Experi-ments on in vivo MR data demonstrate that the proposed method achieves supe-rior MR reconstructions from highly undersampled k-space data over other state-of-the-art image reconstruction methods.
Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI.
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Usually, acquiring less data is a direct but important strategy to address these issues.
