医学图像Diffusion Models相关论文

Learning from DAE to DSM

Significantly Under Sampled Magnetic Resonance Imaging (MRI) Reconstruction Employing Autoencoder-Based Priors

A novel High-Dimensional Embedding Structure is proposed, incorporating a derived prior mechanism, specifically designed to enhance the accuracy of Compressed Sensing Magnetic Resonance Imaging reconstruction.

Denoising autoencoder-based priors in undecimated wavelet domain for magnetic resonance image reconstruction

REDAEP: Resilient and Improved Denoising autoencoder-based prior, aimed at Sparse-View CT reconstruction

Repetitive reconstruction in low-dose CT within the framework of a generative model, utilizing deep gradient priors.

Learning from Image Domain to K-space

Homogeneous Gradient Fields within Generative Density Priors in Magnetic Resonance Imaging (MRI)

Generative Model for Calibration-free Parallel MR Imaging Techniques

该模型是一种用于并行成像重构的K空间加权生成模型

该模型结合低秩分解技术辅助生成K空间数据以实现并行成像重建技术

Universal Generative Modeling in Dual-domain for Dynamic MR Imaging

The reconstruction of sparse-view CT scans is addressed via generative modeling within the sinogram domain.

Learning from Large to Small Dataset

Single-step Generative Prior Model in the Hankel-k-space for reconstructing parallel imaging data

Generative modeling in the Structural-Hankel domain is widely utilized for color image inpainting.

该领域专家在现代信号处理方面有着深厚的造诣，在小波分析系列方法、变分模态分解技术以及经验小波变换等方面均进行了深入研究与系统性优化升级等；同时在机器学习算法与深度学习模型构建方面也取得了显著进展；此外，在机械故障诊断相关问题研究中有着独特见解；时间序列分析技术上则着重于金融信号特征提取与心电信号模式识别等方面