一个改进的双域网络用于CT图像中的金属文物减少,使用聚合的上下文转换
Hui Tang1,2, Sudong Jiang3, Yubing Lin1
1School of Computer Science and Engineering, Laboratory of Image Science and Technology, Southeast University, Nanjing, 210000, Jiangsu, People's Republic of China.
Physics in medicine and biology
|August 4, 2023
概括
这项研究引入了一种改进的双域网络,用于医学成像中的金属工件减少 (MAR). 这种新的方法有效地去除了文物,同时保留了金属物体附近的关键解剖细节.
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:
- 金属工件减少 (MAR) 在医学成像中至关重要,但由于工件移除的复杂性而具有挑战性.
- 当前的双域网络通常会因为过度依赖图像域而在受金属影响的区域丢失重要信息.
研究的目的:
- 为增强的MAR提出一个改进的双域网络框架.
- 为了解决现有方法在金属植入物附近保存解剖细节方面的局限性.
主要方法:
- 使用聚合的上下文转换网络进行增强的阴影图完成.
- 整合了基于预测的线性校正,以删除光束硬化器件.
- 单独训练sinogram和图像域网的网络.
主要成果:
- 在模拟数据集上实现了卓越的性能,平均RMSE为25.1,SSIM为0.973,PSNR为42.1.1.
- 证明有效地保护与金属物体相邻的组织结构.
结论:
- 拟议的方法通过平衡文物移除和解剖细节保存来显著改善MAR.
- 这一框架为在金属植入物存在的情况下更清晰的医学成像提供了一个有希望的解决方案.
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