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Assessment of Diffusion and Perfusion

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...

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Updated: May 3, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

STEMDiff:一个波形增强的扩散模型,用于基于物理的STEM图像生成.

Yihui Bao¹, Xinyi Lu¹, Yanyan Xia¹

¹School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|September 27, 2025

概括

此摘要是机器生成的。

STEMDiff从晶体结构中生成现实的扫描传输电子显微镜 (STEM) 图像,克服机器学习的数据限制. 这种方法改善了材料科学中的原子检测,并有助于开发用于水处理的新材料.

关键词:

这是一个声音.在STEM图像生成过程中,不同的声音不同.扩散模型的扩散模型.设计材料设计材料的设计.

更多相关视频

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

相关实验视频

Last Updated: May 3, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

科学领域:

材料科学材料科学材料科学
计算科学计算科学

背景情况:

机器学习 (ML) 在STEM图像分析中的应用受到稀缺的注释数据的限制.
深度生成模型难以在实验性STEM图像中复制高频细节.

研究的目的:

介绍STEMDiff,一种条件扩散模型,用于生成现实的STEM图像.
解决用于ML应用的STEM图像分析中的数据瓶问题.

主要方法:

采用了一个有条件扩散模型 (STEMDiff) 与物理信息嵌入.
开发了基于离散波段变换的跳过连接架构,以保持高频组件和噪声特征.
利用来自晶体结构的二进制标签来生成合成STEM图像.

主要成果:

产生的STEM图像在数量上几乎无法与实验数据区分,比以前的方法改进了17倍.
合成图像保留了地面真相结构信息和实验噪声特征.
在合成数据上训练的完全卷积网络在实验性WSe2和石墨烯STEM图像中实现了高精度的原子柱检测.

结论:

在STEM图像分析中,STEMDiff有效地克服了数据稀缺问题.
该模型可以在没有手动注释的情况下准确检测原子列,适用于杂的实验数据.
该方法可扩展到其他科学成像模式,有可能加速用于水处理等应用的材料设计.