Search Results for author:
Found 10 papers, 2 papers with code
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection
However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution.
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.
Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model
JPD of MRI and noise-added PET was learned in the diffusion process.
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
Diffusion models are a leading method for image generation and have been successfully applied in magnetic resonance imaging (MRI) reconstruction.
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.
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction
Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction.
High-Frequency Space Diffusion Models for Accelerated MRI
Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation.
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.
PS-Net: Learned Partially Separable Model for Dynamic MR Imaging
In this paper, we propose a learned low-rank method for dynamic MR imaging.
