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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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Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
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相关实验视频

Updated: Feb 24, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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加快MRI与纵向信息的潜伏后部采样加速MRI

Yonatan Urman1, Zachary Shah1, Ashwin Kumar2

  • 1Electrical Engineering, Stanford University, Stanford, California, USA.

Magnetic resonance in medicine
|February 22, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了一种新的MRI重建方法,使用先前的扫描来加速成像. 这种方法可以提高图像质量,减少扫描时间,而不需要对应的纵向数据进行训练.

关键词:
深度学习是一种深度学习.纵向的核磁共振成像 (MRI) 是一种纵向的核磁共振成像.重建的重建的重建.

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相关实验视频

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 图像重建 图像的重建

背景情况:

  • 纵向MRI非常重要,但利用先前的扫描来进行重建是困难的.
  • 现有的深度学习模型需要配对的纵向数据,这很少.
  • 扫描之间的实质性解剖变化对传统方法构成挑战.

研究的目的:

  • 通过将之前的扫描纳入重建过程中,加速MRI采集.
  • 开发一个不需要纵向配对训练数据的重建框架.
  • 为纵向MRI研究引入新的开放式临床数据集.

主要方法:

  • 提出了一个基于扩散模型的重建框架.
  • 该模型使用独立图像进行训练,将所有时间点视为同一分布的样本.
  • 预先扫描 (DICOM格式) 在推断时被整合起来,以指导后续扫描重建.

主要成果:

  • 拟议的方法在加快的笛卡尔成像中表现优于纵向和非纵向基线.
  • 图像质量在SSIM中提高了高达10%,在PSNR中提高了2dB,在与之前扫描相似的区域.
  • 与纵向基线相比,该方法显示对解剖变化和错误注册的稳定性.

结论:

  • 之前的扫描可以有效地与基于扩散的重建相结合,以改善MRI.
  • 这种方法提高了图像质量,并使扫描加速更大.
  • 这种方法避免了需要广泛的纵向配对训练数据集.