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相关概念视频

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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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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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相关实验视频

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Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
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通过深度学习减少前列腺扩散加权MRI检查时间重建重建.

Rory L Cochran1, Eugene Milshteyn2, Soumyadeep Ghosh1

  • 1Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.

Clinical imaging
|November 12, 2024
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概括
此摘要是机器生成的。

扩散加权图像 (DWI) 的深度学习重建 (DLR) 降低了噪音,并可以缩短前列腺MRI检查时间. 这种技术即使在减少数据采集的情况下也保持了诊断图像质量,从而大大节省了时间.

关键词:
深度学习是一种深度学习.扩散加权成像技术的使用.这就是为什么MRI是MRI.前列腺癌是什么意思 前列腺癌是什么意思

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科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能在医学中的应用

背景情况:

  • 扩散权重成像 (DWI) 对于前列腺MRI至关重要.
  • 在不影响图像质量的情况下优化DWI获取时间是一个持续的挑战.
  • 深度学习重建 (DLR) 算法在提高图像质量和效率方面表现有前途.

研究的目的:

  • 通过使用商业DLR算法,评估从标准和减少数据集中获得的高b值DWI的诊断图像质量.
  • 评估DLR对前列腺MRI图像噪声和信号噪声比 (SNR) 的影响.
  • 为了确定前列腺MRI使用DLR与减少数据集的时间缩短潜力.

主要方法:

  • 对52名接受前列腺核磁共振 (MRI) 的患者进行了回顾性分析.
  • 使用传统的2D卡特西亚和DLR算法重建DWI.
  • 通过使用更少激发的DLR (NEX) 模拟缩短采集时间.
  • 独立的放射科医生对图像质量的评估 (4点利克尔特尺度) 和SNR分析.

主要成果:

  • DLR图像显示,与标准NEX的常规重建相比,噪音降低.
  • 通过使用减少的NEX数据集,DLR保留了诊断图像质量.
  • 限制FOV DLR图像显示了SNR的改善.
  • 使用DLR减少的NEX数据集实现了高达68%的时间缩短 (限制FOV) 和39%的时间缩短 (大FOV).

结论:

  • 在标准的NEX DWI中,DLR有效地降低了图像噪声.
  • 使用减少NEX数据集的DLR可以显著减少前列腺MRI检查时间.
  • 当使用DLR与减少的NEX数据集时,诊断图像质量保持不变.