基于隐式神经表示的方法,用于X射线CT成像中的金属诱导光束硬化工件减少
Hyoung Suk Park1, Jin Keun Seo2, Kiwan Jeon1
1National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Medical physics
|January 31, 2025
概括
使用隐性神经表示的新无参数方法在X射线计算机断层扫描 (CT) 图像中纠正金属诱导的光束硬化工件,而不需要材料细分或参数估计.
科学领域:
- 医疗成像医学成像
- 计算成像技术的成像
- 图像重建 图像的重建
背景情况:
- 金属诱导光束硬化器件降低了X射线CT图像质量.
- 现有的校正方法需要精确的材料细分和参数估计.
- 这些局限性阻碍了在涉及金属,骨头和牙的复杂场景中进行文物校正.
研究的目的:
- 开发一个无参数的金属诱导光束硬化校正 (MBHC) 方法.
- 通过消除细分和参数估计,克服现有的MBHC方法的局限性.
- 提供CT中金属工件减少的通用解决方案.
主要方法:
- 使用隐式神经表示 (INR) 来生成单色衰减和非线性光束硬化图像.
- 使用损失函数以非线性地建模预测的投影数据.
- 消除对金属的几何和参数估计的需要.
主要成果:
- 有效地减少了金属,骨头和牙之间的相互作用造成的光束硬化器件.
- 在应对数据不足挑战方面表现出潜力,例如光子饥饿和截断的视野.
- 在数值和幻影实验中实现了高质量的图像重建.
结论:
- 拟议的一般化MBHC方法提供了强大的工件减少,没有参数估计或细分.
- 这种基于INR的方法为CT成像中的金属工件校正提供了重大进步.
- 通过在金属植入物或密集材料的存在下提高图像质量,使更准确的诊断成为可能.
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