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)

1.1K
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...
1.1K

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

Enhanced Quantitative Phosphocreatine MR Imaging of Skeletal Muscle Using a Global-Local Two-Branch Deep Learning Model.

Magnetic resonance in medicine·2026
Same author

A rapid and accurate guanidine CEST imaging in ischemic stroke using a machine learning approach.

Physics in medicine and biology·2026
Same author

Denoising Low-Power CEST Imaging Using a Deep Learning Approach With a Dual-Power Feature Preparation Strategy.

Magnetic resonance in medicine·2025
Same author

Interpreting amide proton transfer-weighted imaging contrast between normal and tumor brain tissues using the asymmetry analysis method at 4.7 T.

Magnetic resonance in medicine·2025
Same author

Functional contrast across the gray-white matter boundary.

Nature communications·2025
Same author

Specific APT and NOE Imaging Using DSP-CEST in Humans at 3 T.

NMR in biomedicine·2025
Same journal

Feasibility and SNR Performance of Hyperpolarized <sup>129</sup>Xe Gas Exchange Imaging Using a Balanced SSFP Sequence.

Magnetic resonance in medicine·2026
Same journal

Multi-Contrast Human Brain CEST MRI at 11.7 T: First In Vivo Demonstration.

Magnetic resonance in medicine·2026
Same journal

Suppression of Oscillation and Ghosting in RF-Spoiled Gradient-Echo-Based Dynamic Imaging.

Magnetic resonance in medicine·2026
Same journal

A Simple, Dynamic Geometric Phantom for MRI and CT Reconstruction Pipelines: Beyond Shepp-Logan.

Magnetic resonance in medicine·2026
Same journal

7T 3D-EPI PCASL With High SNR Efficiency and Robustness to Through-Plane B<sub>0</sub> Field Gradients.

Magnetic resonance in medicine·2026
Same journal

A Comparison of Tissue Property Values Estimated Using Conventional Cardiac MRF and MT-Cardiac MRF.

Magnetic resonance in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 8, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K

基于机器学习的胺基质子转移成像,使用部分合成训练数据.

Malvika Viswanathan1,2, Leqi Yin3, Yashwant Kurmi1,4

  • 1Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Magnetic resonance in medicine
|December 15, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了部分合成数据,用于训练机器学习模型,以预测胺基质子转移 (APT) 效应,提高化学交换和转移 (CEST) 成像的准确性和稳定性.

关键词:
氨基酸胺转移质子转移化学交换和转移化学交换和转移机器学习是机器学习.瘤是一个瘤.

更多相关视频

Visualization of Amyloid &#946; Deposits in the Human Brain with Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry
09:31

Visualization of Amyloid β Deposits in the Human Brain with Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry

Published on: March 7, 2019

10.7K
MALDI Imaging Mass Spectrometry of Neuropeptides in Parkinson's Disease
16:57

MALDI Imaging Mass Spectrometry of Neuropeptides in Parkinson's Disease

Published on: February 14, 2012

26.4K

相关实验视频

Last Updated: Jul 8, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.0K
Visualization of Amyloid &#946; Deposits in the Human Brain with Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry
09:31

Visualization of Amyloid β Deposits in the Human Brain with Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry

Published on: March 7, 2019

10.7K
MALDI Imaging Mass Spectrometry of Neuropeptides in Parkinson's Disease
16:57

MALDI Imaging Mass Spectrometry of Neuropeptides in Parkinson's Disease

Published on: February 14, 2012

26.4K

科学领域:

  • 生物医学成像技术 生物医学成像技术
  • 机器学习 机器学习
  • 量化MRI是指数量化的MRI.

背景情况:

  • 机器学习 (ML) 模型用于化学交换和转移 (CEST) 效应量化面临的挑战是有限的测量数据或完全模拟数据的偏差.
  • 开发强大的机器学习模型需要解决CEST成像中的这些数据限制.

研究的目的:

  • 通过结合模拟和测量组件来生成部分合成CEST数据的新平台.
  • 评估使用这些部分合成数据用于训练ML模型来预测胺基质子转移 (APT) 效应的可行性.

主要方法:

  • 通过将模拟APT效应与测量组件相结合,创建了部分合成的CEST信号.
  • 用部分合成,完全模拟和体内数据训练ML模型,以预测9L瘤大鼠大脑中的APT效应.

主要成果:

  • 在模仿组织实验中,部分合成数据能够准确地预测APT.
  • 在体内实验表明,与其他方法相比,使用部分合成数据训练的ML模型的精度和稳定性更高.

结论:

  • 部分合成的CEST数据有效地解决了传统的ML训练方法的局限性.
  • 这种方法为提高基于ML的CEST量化的准确性和可靠性提供了一个有希望的解决方案.