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Computed Tomography

Computed Tomography

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

Updated: Jun 26, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Syn2Real:用于基于深度学习的校正,合成CT图像环文物.

Dennis Hein^1,2, Staffan Holmin^3,4, Vladimir Prochazka⁵

¹Department of Physics, KTH Royal Institute of Technology, Stockholm, Sweden.

Physics in medicine and biology

|January 22, 2025

概括

本研究介绍了Syn2Real,这是一种创建现实训练数据的新方法,用于基于深度学习的X射线计算机断层扫描 (CT) 中的环形工件校正. 这种方法使得可扩展的数据生成没有系统特定的物理,提高CT图像质量.

关键词:

这就是为什么CTCTCTCTCTCT 联合国网络联合国网络联合国网络数据合成数据的合成.深度学习是一种深度学习.用光子计数CTCT进行测试.戒指文物戒指文物

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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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相关实验视频

Last Updated: Jun 26, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

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Published on: September 28, 2018

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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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:

戒指文物降低了X射线计算机断层扫描 (CT) 图像质量,限制了临床效用.
目前的深度学习方法需要大量高质量的数据集,这些数据集的生成成本昂贵,耗时.
在图像领域合成训练数据提供了一个可扩展的解决方案,而不依赖于特定的成像系统物理.

研究的目的:

开发一种新的,计算效率高的管道"Syn2Real",用于在图像域中直接合成现实的环形工件.
为了实现可扩展的训练数据的生产,以深度学习为基础的环形工件校正方法.
为了证明合成数据训练模型在不同的CT系统和成像参数上的通用性.

主要方法:

开发了"Syn2Real",这是一个图像域管道,用于实现现实的环形象合成.
在合成和真实CT数据上训练了UNet,UNetpp (与自我注意),以及扩散模型.
在能量整合CT图像和具有不同参数的原型光子计数CT数据上评估模型性能.

主要成果:

在合成数据上训练的模型在各种光子计数CT图像上展示了有效的环形工件校正.
跨不同能量级别和切片厚度的成功泛化,验证合成数据的真实性.
这种方法被证明对各种深度学习架构和损失函数非常通用.

结论:

Syn2Real 管道为生成用于 CT 环形人工物校正的多功能训练数据提供了坚实的基础.
这种方法促进了对各种CT应用程序开发更具适应性和有效的工件校正解决方案.
这项研究强调了图像域数据合成在医学成像学深度学习方面的潜力.