Search Results for author:
Found 17 papers, 6 papers with code
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
We propose using a coordinate network decoder for the task of super-resolution in MRI.
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
However, they require several iterations of a large neural network to handle high-dimensional imaging tasks such as 3D MRI.
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction.
Multi-Task Accelerated MR Reconstruction Schemes for Jointly Training Multiple Contrasts
To address this issue, we propose multi-task learning (MTL) schemes that can jointly reconstruct multiple datasets.
Deep subspace learning for efficient reconstruction of spatiotemporal imaging data
Model-based deep learning approaches, such as unrolled neural networks, have been shown to be effective tools for efficiently solving inverse problems.
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
Self-supervised learning (SSL) has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data for downstream tasks.
Greedy Learning for Large-Scale Neural MRI Reconstruction
Model-based deep learning approaches have recently shown state-of-the-art performance for accelerated MRI reconstruction.
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction.
Learning to Sample MRI via Variational Information Maximization
Accelerating MRI scans requires optimal sampling of k-space data.
Risk Quantification in Deep MRI Reconstruction
Reliable medical image recovery is crucial for accurate patient diagnoses, but little prior work has centered on quantifying uncertainty when using non-transparent deep learning approaches to reconstruct high-quality images from limited measured data.
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice.
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts.
Degrees of Freedom Analysis of Unrolled Neural Networks
Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks.
Uncertainty Quantification in Deep MRI Reconstruction
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks.
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination.
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.
