从CBCT使用深度学习生成合成CT,用于前列腺癌适应性放射治疗
Mustafa Çağlar1, Kerime Selin Ertaş1,2, Mehmet Sıddık Cebe3
1Department of Health Physics, Graduate School of Health Sciences, İstanbul Medipol University, İstanbul, Türkiye.
Frontiers in radiology
|December 1, 2025
概括
像U-Net和ResU-Net这样的深度学习模型可以准确地从Cone Beam CT (CBCT) 来生成合成CT (sCT) 对于前列腺癌患者. 这支持适应性放射治疗和精确的剂量计算.
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:
- 传统的圆束计算机断层扫描 (CBCT) 在放射治疗期间提供图像,但缺乏用于剂量计算的CT (pCT) 规划的准确性.
- 从CBCT生成合成CT (sCT) 可以弥合这一差距,使适应性放射治疗成为可能.
- 精确的sCT生成对于改善治疗计划和前列腺癌管理中的患者结果至关重要.
研究的目的:
- 评估深度学习模型 (U-Net,ResU-Net) 的准确性,用于在前列腺癌患者中从CBCT生成合成CT (sCT).
- 评估生成的sCTs在治疗规划中的临床适用性.
- 调查sCTs在支持适应性放射治疗决策方面的潜力.
主要方法:
- 从10名前列腺癌患者的50个CBCT-CT映射进行了回顾性分析.
- 在预处理和解剖匹配的CBCT图像上使用PyTorch训练U-Net和ResU-Net模型.
- 使用SSIM,PSNR和MAE等指标,量化比较生成的sCT与计划的CT.
主要成果:
- 与CBCT相比,U-Net和ResU-Net都显著提高了图像相似性 (SSIM) 和信号噪声比 (PSNR).
- 在统计学上,ResU-Net的PSNR比U-Net的PSNR要高得多.
- 平均绝对误差 (MAE) 被两种模型显著降低,ResU-Net显示的误差略低 (61.8 HU) 与U-Net (65.3 HU) 相比.
结论:
- 深度学习模型,特别是U-Net和ResU-Net,为从CBCT中生成临床相关的sCT提供了有效和可行的解决方案.
- 生成的sCTs支持准确的剂量计算,这对于有效的放射治疗至关重要.
- 这些模型促进了前列腺癌管理中的自适应性放射治疗工作流程.
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