|
- Texture-preserving diffusion model for CBCT-to-CT synthesis
In this study, we propose a novel texture-preserving diffusion model for CBCT-to-CT synthesis that incorporates adaptive high-frequency optimization and a dual-mode feature fusion module Our method aims to enhance high-frequency details, effectively fuse cross-modality features, and preserve fine image structures
- CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on . . .
In this study, we propose a hybrid U-Net diffusion model (HUDiff) based on Vision Transformer (ViT) and the information bottleneck theory to improve CBCT image quality
- 保持纹理的扩散模型用于CBCT到CT的合成|文献速递-生成式模型与transformer在医学影像中的应用 - 知乎
从cbct数据合成类似ct的图像是一个快速发展的领域，它提供了一种利用ct的优势，同时避免cbct技术限制的方法。 该合成技术能够提高图像质量，区分不同的组织，并提供患者解剖结构的更全面理解，从而在各种临床环境中实现更准确的诊断和更好的治疗计划。
- zyj15416 TPDM-CBCT2CT: CBCT2CT - GitHub
This repository implements a texture-preserving diffusion model for CBCT-to-CT synthesis, designed to improve the quality and accuracy of medical image synthesis
- [2303. 02649] CBCT-Based Synthetic CT Image Generation Using Conditional . . .
To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan Purpose: This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT domain for the image quality improvement of CBCT
- 【技术追踪】用于 CBCT 到 CT 合成的纹理保持扩散模型（MIA-2025）-CSDN博客
Title题目Texture-preserving diffusion model for CBCT-to-CT synthesis保持纹理的扩散模型用于CBCT到CT的合成01文献速递介绍锥形束计算机断层扫描（CBCT）因其较低的辐射剂量和适用于局部解剖评估的特点，成为各种临床情境中的基础工具，能够为复杂的手术操作，如图像引导
- CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on . . .
The diffusion model operates in two phases: first incorporating Gaussian noise progressively into the CT image, followed by a reverse phase that estimates and eliminates the introduced noise, facilitating CBCT to CT image conversion
- Energy-guided diffusion model for CBCT-to-CT synthesis
In this study, we introduce EGDiff, an energy-guided diffusion model for generating synthetic CT (sCT) images from CBCT images Our method consists of two stages: the first stage is a forward noise addition process, where we add Gaussian noise to the CT images T times, disrupting their original distribution
|
|
|