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相关概念视频

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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基于任务的可转移深度学习分散校正在形束计算机断层扫描:一个模拟研究研究.

Juan P Cruz-Bastida1, Fernando Moncada1, Arnulfo Martínez-Dávalos1

  • 1Instituto de Física, Universidad Nacional Autónoma de México, Ciudad Universitaria, Coyoacán, Mexico City, Mexico.

Journal of medical imaging (Bellingham, Wash.)
|March 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种使用卷积神经网络 (CNN) 的快速方法,用于束计算机断层扫描 (CBCT) 中的X射线散射校正. 这种方法提高了使用较少数据的图像质量,并提高了医疗成像任务的模型通用性.

关键词:
圆束计算机断层扫描技术深度学习是一种深度学习.转移学习转移学习一个X射线散射器.

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Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
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科学领域:

  • 医学物理 医学物理
  • 放射学 放射学是一门学科.
  • 图像处理 图像处理

背景情况:

  • 在形束计算断层扫描 (CBCT) 中,X射线散射会降低图像质量.
  • 卷积神经网络 (CNN) 显示了分散校正的潜力,但面临着大数据集和通用性的挑战.
  • 基于任务的范式提供了一个解决方案,以提高CNN在散射校正中的应用.

研究的目的:

  • 在CBCT中引入基于CNN的X射线散射校正的基于任务的范式.
  • 为了克服广泛数据集的局限性和CNN散射校正中的模型概括性.
  • 加强CNN的应用,以提高CBCT的图像质量.

主要方法:

  • 采用U-net架构的CNN采用了两阶段的培训过程.
  • 在几何幻影投影方面,CNN已经接受了预先培训,并在人类形象投影上使用转移学习 (TL) 进行了微调.
  • 2D散射比 (SR) 地图被用作散射预测和CNN针对特定任务的再培训的目标.

主要成果:

  • 预训练实现了准确的散射比率 (SR) 预测.
  • 转移学习 (TL) 进一步提高了SR预测准确度,数据少得多,再培训时间更快.
  • 美国有线电视新闻网 (CNN) 模型在人类形状结构中成功地进行了X射线散射校正.

结论:

  • 建立了一种快速,低成本的方法,用于在CBCT散射校正中针对特定任务的CNN开发.
  • 拟议的方法允许在CBCT中使用最小数据进行有效的分散校正.
  • 开发的CNN模型显示,它有望在以前未见的解剖结构中纠正分散.