Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

962
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
962

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Theoretical Prediction of Bias in Model-Based Material Decomposition.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

One-Step Material Decomposition Using Spectral Diffusion Posterior Sampling in Sparse-View Dual-Layer CT.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Joint Estimation of Scatter Distribution and Material Maps in Volumetric Dual-Layer Cone-Beam CT.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Evaluation of Fluence Reduction versus Sparsity for Diffusion Posterior Sampling Reconstruction in Low-Dose CT.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Diffusion Posterior Sampling for Tomographic Reconstruction with Mixed Resolution Priors.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Using a Physics-Based Approach to Standardize Radiomics Values: Experimental Validation in an Anthropomorphic Phantom on a Clinical CT Scanner Using a Range of Dose Levels and Reconstruction Kernels.

Proceedings of SPIE--the International Society for Optical Engineering·2026

相关实验视频

Updated: May 15, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.3K

使用光谱扩散后面采样进行体积材料分解,采用压缩多色前置模型进行后面采样.

Xiao Jiang, Grace J Gang, J Webster Stayman

    ArXiv
    |April 8, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了用于CT扫描中高效材料分解的3D光谱扩散后面采样 (光谱DPS). 新方法在管理记忆的同时实现了准确的结果,优于其他深度学习技术.

    更多相关视频

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K
    Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
    09:57

    Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

    Published on: July 25, 2022

    3.8K

    相关实验视频

    Last Updated: May 15, 2025

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
    07:05

    Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

    Published on: June 18, 2021

    2.3K
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K
    Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
    09:57

    Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

    Published on: July 25, 2022

    3.8K

    科学领域:

    • 医疗成像医学成像
    • 计算成像技术的成像
    • 人工智能在医学中的应用

    背景情况:

    • 在光谱CT中精确的材料分解对于定量成像至关重要.
    • 上一篇2D光谱DPS框架集成分析模型与数据驱动的先验.
    • 大量的内存要求限制了3D光谱CT材料分解的应用.

    研究的目的:

    • 为了将2D光谱DPS算法扩展到3D,用于体积材料分解.
    • 为了解决处理临床相关扫描卷的内存限制.
    • 在3D光谱CT中提高一步材料分解的准确性和性能.

    主要方法:

    • 开发了一种高效的3D光谱DPS,使用预先训练的2D扩散模型进行切片处理.
    • 实现了一个压缩的多色前置模型,用于准确的物理建模.
    • 通过对临床显著体积大小的模拟研究验证了该方法.

    主要成果:

    • 记忆效率高的3D光谱DPS成功实现了大型体积数据集的材料分解.
    • 与InceptNet和条件DDPM相比,光谱DPS表现出优异的性能.
    • 在对比度量化,切片间连续性和分辨率保存方面表现优于其他深度学习算法.

    结论:

    • 拟议的3D光谱DPS为推进体积光谱CT的一步材料分解提供了基础.
    • 这种记忆效率高的方法使得3D材料分解在临床应用中变得可行.
    • 与现有的深度学习方法相比,光谱DPS提供了更好的准确性和图像质量.