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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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...
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Atomic Nuclei: Magnetic Resonance01:05

Atomic Nuclei: Magnetic Resonance

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The number of nuclear spins aligned in the lower energy state is slightly greater than those in the higher energy state. In the presence of an external magnetic field, as the spins precess at the Larmor frequency, the excess population results in a net magnetization oriented along the z axis. When a pulse or a short burst of radio waves at the Larmor frequency is applied along the x axis, the coupling of frequencies causes resonance and flips the nuclear spins of the excess population from the...
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相关实验视频

Updated: Jul 19, 2025

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging
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使用自主监督深度学习与模型强化进行磁共振参数映射.

Wanyu Bian1, Albert Jang1, Fang Liu1

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129 USA.

ArXiv
|August 7, 2023
PubMed
概括
此摘要是机器生成的。

RELAX-MORE是一种新的自我监督学习方法,可以快速准确地进行定量MRI重建. 这种特定主题的方法需要最小的数据,提高了MR参数映射的效率和稳定性.

关键词:
模型的增强件 模型增强件优化优化 优化优化量化MRI是指数量化的MRI.自主监督学习学习

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相关实验视频

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Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging
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科学领域:

  • 医疗成像医学成像
  • 机器学习 机器学习
  • 生物物理学的生物物理.

背景情况:

  • 定量MRI (qMRI) 对于精确的组织表征至关重要.
  • 当前的qMRI重建方法面临着速度,准确性和数据要求的挑战.
  • 在qMRI中加速成像通常会导致文物和图像质量降低.

研究的目的:

  • 介绍RELAX-MORE,一种用于加速qMRI重建的新型自我监督学习方法.
  • 为了证明RELAX-MORE在生成准确和强大的MR参数地图方面的能力.
  • 为QMRI研究建立一种实用且数据效率高的方法.

主要方法:

  • RELAX-MORE将基于模型的qMRI重建展开到使用优化算法的深度学习框架中.
  • 该方法采用自主监督学习,允许对单个主题数据进行培训.
  • 量化绘制被用作脑,膝盖和幻象实验的代表性应用.

主要成果:

  • 在重建MR参数,纠正文物和减少噪音方面,RELAX-MORE取得了出色的性能.
  • 该方法即使在不完美的成像条件下也表现出强度和准确性.
  • 对比分析显示,与现有方法相比,效率,准确性,稳定性和通用性显著提高.

结论:

  • RELAX-MORE为快速MR参数映射提供了一种可行和有效的自我监督学习解决方案.
  • 主体特异性和最小数据要求提高了qMRI的实际应用性.
  • 这种方法具有很大的潜力,可以促进定量MRI技术的临床转化.