Search Results for author:
Found 17 papers, 7 papers with code
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.
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks
Part-prototype networks (e. g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts.
Deep Manifold Learning for Dynamic MR Imaging
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers.
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space
Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI) remains a challenge due to the trade-off between residual motion blurring caused by discretized motion states and undersampling artefacts.
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning
This is specifically achieved by devising an elaborated ``prophetic'' teacher, termed as ``Propheter'', that aims to learn the potential class distributions.
Fusion Based Holistic Road Scene Understanding
This paper addresses the problem of holistic road scene understanding based on the integration of visual and range data.
Iterated Tabu Search Algorithm for Packing Unequal Circles in a Circle
Meanwhile, it builds a neighborhood structure on the set of local minimum via two appropriate perturbation moves and integrates two combinatorial optimization methods, Tabu Search and Iterated Local Search, to systematically search for good local minima.
Deep Low-rank Prior in Dynamic MR Imaging
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
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.
A Multivariate Density Forecast Approach for Online Power System Security Assessment
A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems.
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.
Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control
Then we design the one-step actor-critic DRL scheme which is a simplified version of recent DRL algorithms, and it avoids the issue of Q value overestimation successfully.
Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imaging
While existing methods bin acquired data from neighboring time points to reconstruct one phase of the cardiac motion, our framework allows for a continuous, binning-free, and subject-specific k-space representation. We assign a unique coordinate that consists of time, coil index, and frequency domain location to each sampled k-space point.
Reconstruction-driven motion estimation for motion-compensated MR CINE imaging
Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i. e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization.
Propagation and Attribution of Uncertainty in Medical Imaging Pipelines
In this paper, we propose a method to propagate uncertainty through cascades of deep learning models in medical imaging pipelines.
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions.
