之前的FOVNet:一个多模式的深度学习框架,用于大电压计算机断层扫描截断文物纠正和视野扩展
Long Tang1,2, Mengxun Zheng1,2, Peiwen Liang1,2
1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了Prior-FOVNet用于修复电压巨大的计算机断层扫描 (MVCT) 图像截断. 该方法增强了视野 (FOV),以便更好地规划适应性放射治疗.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 人工智能在医学中的应用
背景情况:
- 大电压计算机断层扫描 (MVCT) 在断层治疗中对患者的定位和剂量重建至关重要.
- 在MVCT中,有限的扫描视野 (sFOV) 会导致投影数据的切断,影响注册准确性和自适应规划.
- 截断工件损害了MVCT在放射治疗中的临床实用性.
研究的目的:
- 开发和验证一种新的方法,即Prior-FOVNet,用于纠正切断文物和扩展MVCT图像中的视野 (eFOV).
- 为了利用患者特定的千伏计算断层扫描 (KVCT) 数据进行人工物校正和FOV扩展.
- 提高MVCT适应性放射治疗的准确性和可靠性.
主要方法:
- 拟议的Prior-FOVNet使用KVCT的材料和形状先验来纠正MVCT的截断.
- 一个基于对比学习的生成对抗网络 (GAN),TransNet,合成了KVCT的MVCT (sMVCT) 图像,以解决强度差异.
- 一个基于Swin变压器的绘制网络处理sMVCT和sinogram-extrapolated MVCT用于文物校正和FOV扩展.
主要成果:
- 拟议的方法有效地减少了切断文物,并重建了超出原始sFOV的解剖结构.
- 在测试数据上,平均绝对误差 (MAE) 为 23.8 ± 5.6 HU,结构相似度指数 (SSIM) 为 97.8 ± 0.6.
- 与现有的切断校正技术相比,在模拟和真实患者数据中表现出优异的性能.
结论:
- 之前的FOVNet成功地纠正了MVCT截断文物,并扩展视野,提高图像质量.
- 结合KVCT先验和先进的深度学习模型,提高了MVCT适应性放射治疗的可靠性.
- 该方法在改善适应性放射治疗工作流程方面显示出临床应用的巨大潜力.
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