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相关概念视频

Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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相关实验视频

Updated: Jun 25, 2025

A Coregistered Ultrasound and Photoacoustic Imaging Protocol for the Transvaginal Imaging of Ovarian Lesions
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优化用于超声波成像的阵列编码.

Jacob Spainhour1, Korben Smart2, Stephen Becker1

  • 1Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, United States of America.

Physics in medicine and biology
|May 30, 2024
PubMed
概括
此摘要是机器生成的。

机器学习优化超声波成像序列,以获得更好的分辨率和对比度. 这种方法探索了超越传统方法的新型扫描模式,提高了B模式图像质量.

关键词:
图像质量 图像质量的质量机器学习是机器学习.数字优化数字优化空间编码 空间编码合成光圈的开口.

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3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
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相关实验视频

Last Updated: Jun 25, 2025

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科学领域:

  • 超声波成像 超声波成像
  • 医学成像物理学 医学成像物理学
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 发射编码模型对于理解合成光圈成像中的声传输效应至关重要.
  • 目前的扫描序列代表了超声波图像重建可能性的有限子集.

研究的目的:

  • 利用机器学习 (ML) 开发优化的扫描序列,以获得高质量的B模式超声波图像.
  • 在合成光圈成像中探索超越传统方法的新型编码序列.

主要方法:

  • 在PyTorch中使用来自Field II的模拟射频 (RF) 数据开发了一个定制的ML模型.
  • 该模型探测编码序列 (时间延迟,apodization权重) 以尽量减少图像质量损失的功能.
  • 对于延迟和总和束形成的新型衍生配方使得计算可行性成为可能.

主要成果:

  • 用ML优化编码序列,当与REFoCUS成像框架一起使用时,显著改善分辨率,视野和对比度.
  • 对线材目标和模仿组织的幻影进行实验验证,证实了增强的图像质量指标.
  • 与传统扫描序列相比,ML方法显示出更高的性能.

结论:

  • 机器学习可以发现和优化用于合成发射孔径成像的新型扫描序列.
  • 在ML模型中整合光束成型对于合成发射孔径成像任务是有价值的.
  • 这项工作扩大了对可用的编码方案的理解,超越了狭窄,常用的子集.