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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.2K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
5.2K
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

807
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
807

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

J-Score: Joint Distribution Learning With Score-Based Diffusion for Accelerating T1ρ Mapping.

IEEE transactions on medical imaging·2025
Same author

Continuous-wave terahertz quantum cascade lasers based on quasi-flatband BIC and 2D dual-patch arrays.

Optics express·2025
Same author

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI.

IEEE transactions on medical imaging·2024
Same author

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

IEEE transactions on medical imaging·2024
Same author

Synthesizing PET images from high-field and ultra-high-field MR images using joint diffusion attention model.

Medical physics·2024
Same author

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection.

IEEE journal of biomedical and health informatics·2024
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
查看所有相关文章

相关实验视频

Updated: Jul 6, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

19.6K

高频空间扩散模型用于加速MRI.

Chentao Cao, Zhuo-Xu Cui, Yue Wang

    IEEE transactions on medical imaging
    |January 9, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的扩散模型,用于更快,更准确的磁共振 (MR) 图像重建. 高频空间SDE方法提高了图像质量,减少了重建时间.

    更多相关视频

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
    17:06

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

    Published on: November 8, 2012

    26.2K
    A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
    09:59

    A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

    Published on: September 16, 2017

    14.1K

    相关实验视频

    Last Updated: Jul 6, 2025

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
    09:30

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

    Published on: December 18, 2016

    19.6K
    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
    17:06

    Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

    Published on: November 8, 2012

    26.2K
    A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
    09:59

    A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

    Published on: September 16, 2017

    14.1K

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 计算科学 计算科学

    背景情况:

    • 使用连续随机微分方程 (SDEs) 的扩散模型在图像生成方面表现出色,并且可以解决磁共振 (MR) 重建中的反向问题.
    • 目前应用于MR重建的扩散模型与完全采样的低频k空间数据扎,导致重建不确定性和缓慢的融合.
    • 现有的方法需要多次代,使得MR图像重建耗时.

    研究的目的:

    • 开发一种针对MR重建的新型SDE,解决现有扩散模型的局限性.
    • 提高快速MRI成像中的重建精度和稳定性.
    • 为了加速MRI图像重建过程.

    主要方法:

    • 提出一种新型的SDE,用于MR重建的高频空间扩散过程 (HFS-SDE).
    • 确保完全采样的低频区域的确定性,并加快反向扩散采样.
    • 利用公开可用的快速MRI数据集进行实验验证.

    主要成果:

    • 与传统并行成像,监督深度学习和现有扩散模型相比,HFS-SDE方法显示出更高的重建精度和稳定性.
    • HFS-SDE方法的快速收特性在理论和实验上都得到了验证.
    • 拟议的方法有效地处理k空间数据中低频区域的重建.

    结论:

    • HFS-SDE方法在MR图像重建方面取得了重大进展,克服了现有扩散模型的关键挑战.
    • 这种方法为快速的MRI成像提供了更准确,更稳定,更快的解决方案.
    • 开发的技术有可能提高MR成像中的临床工作流程效率.