Deep unfolding as iterative regularization for imaging inverse problems
Deep unfolding methods have gained significant popularity in the field of inverse problems
as they have driven the design of deep neural networks (DNNs) using iterative algorithms. In …
as they have driven the design of deep neural networks (DNNs) using iterative algorithms. In …
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems
Recently, data-driven techniques have demonstrated remarkable effectiveness in
addressing challenges related to MR imaging inverse problems. However, these methods …
addressing challenges related to MR imaging inverse problems. However, these methods …
Physics-informed DeepMRI: Bridging the gap from heat diffusion to k-space interpolation
In the field of parallel imaging (PI), alongside image-domain regularization methods,
substantial research has been dedicated to exploring $ k $-space interpolation. However …
substantial research has been dedicated to exploring $ k $-space interpolation. However …
A multimodal data-driven framework for anxiety screening
H Mo, SC Hui, X Liao, Y Li, W Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Early screening for anxiety and the implementation of appropriate interventions are crucial in
preventing self-harm and suicide among patients. While multimodal real-world data provides …
preventing self-harm and suicide among patients. While multimodal real-world data provides …
A Structured Pruning Algorithm for Model-based Deep Learning
There is a growing interest in model-based deep learning (MBDL) for solving imaging
inverse problems. MBDL networks can be seen as iterative algorithms that estimate the …
inverse problems. MBDL networks can be seen as iterative algorithms that estimate the …
DARCS: Memory-Efficient Deep Compressed Sensing Reconstruction for Acceleration of 3D Whole-Heart Coronary MR Angiography
Z Xue, F Yang, J Gao, Z Chen, H Peng, C Zou… - arXiv preprint arXiv …, 2024 - arxiv.org
Three-dimensional coronary magnetic resonance angiography (CMRA) demands
reconstruction algorithms that can significantly suppress the artifacts from a heavily …
reconstruction algorithms that can significantly suppress the artifacts from a heavily …
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI
Recently, regularization model-driven deep learning (DL) has gained significant attention
due to its ability to leverage the potent representational capabilities of DL while retaining the …
due to its ability to leverage the potent representational capabilities of DL while retaining the …
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging
Long scan time significantly hinders the widespread applications of three-dimensional multi-
contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate …
contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate …