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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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相关实验视频

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Sparsier2Sparse:基于自主监督的卷积神经网络的条纹文物减少稀疏视图CT图像的稀疏视图.

Seongjun Kim1, Byeongjoon Kim2, Jooho Lee2

  • 1School of Integrated Technology, Yonsei University, Incheon, South Korea.

Medical physics
|June 12, 2023
PubMed
概括

这项研究引入了一种新的自我监督的深度学习方法,用于在稀疏视图计算机断层扫描 (CT) 成像中减少条纹文物. 该技术有效地提高了图像质量,仅使用稀疏视图数据,克服了以前方法的局限性.

关键词:
计算机断层扫描 (CT) 是一种计算机断层扫描.卷积神经网络是一种卷积神经网络.自主监督学习学习稀疏视野的CTCT可以使用.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 稀疏视图计算机断层扫描 (CT) 减少了扫描时间和辐射剂量,但引入了条纹文物.
  • 现有的完全监督的方法需要配对的全视图和稀疏视图数据,这在临床上是不可行的.
  • 需要新的自我监督学习方法来减少稀疏视图CT中的工件.

研究的目的:

  • 提出一种新的自主监督卷积神经网络 (CNN) 方法,用于在稀疏视图CT图像中减少条纹文物.
  • 开发一种不需要全视图CT数据进行培训的方法.
  • 为了提高CT图像的诊断质量,从有限的投影数据重建CT图像.

主要方法:

  • 一个自我监督的CNN只使用稀疏视图CT数据进行训练.
  • 之前的图像是通过代地应用训练有素的网络来估计条纹文物来生成的.
  • 从稀疏视图图像中减去估计的文物,以获得文物减少的结果.

主要成果:

  • 拟议的方法在XCAT和AAPM低剂量CT大挑战数据集上得到了验证.
  • 视觉检查和调制转移函数 (MTF) 分析表明,解剖结构的有效保存.
  • 与现有的工件减少技术相比,该方法实现了更高的图像分辨率.

结论:

  • 开发了一个新的框架,用于仅使用稀疏视图CT数据来减少条纹文物.
  • 自主监督的方法在没有完整视图数据的情况下保存细节方面取得了卓越的性能.
  • 这种框架克服了完全监督方法的数据集限制,并有可能用于医学成像应用.