腹部合成CT生成仅用于MR放射治疗,使用结构保护损失和基于变压器的循环-GANAN
Chanwoong Lee1,2, Young Hun Yoon1,2,3, Jiwon Sung4
1Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
Frontiers in oncology
|January 20, 2025
概括
这项研究引入了一种改进的深度学习方法,用于从腹部癌症患者的MRI扫描中生成合成CT (sCT),解决运动挑战. 基于变压器的循环GAN显著提高了腹部sCT生成的图像质量和剂量测量精度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 辐射疗法 辐射疗法
背景情况:
- 从磁共振 (MR) 图像中生成基于深度学习的合成计算机断层扫描 (sCT) 显示出有希望的结果.
- 腹部sCT生成面临由于患者运动 (呼吸,周围静止) 的挑战.
- 现有的方法难以处理腹部区域复杂的解剖学和运动.
研究的目的:
- 调查腹部sCT生成的无监督学习方法.
- 使用基于变压器的循环GAN与结构保护损失来解决患者运动挑战.
- 为改善腹部癌症患者的sCT质量.
主要方法:
- 从腹部癌症患者收集了120张T2MR和相应的CT图像.
- 采用了一个循环-GAN架构,修改了Swin-UNETR发生器和Modality-Independent Neighborhood Descriptor (MIND) 损失.
- 评估图像质量 (MAE,PSNR,SSIM,KL差异) 和剂量测量精度 (马分析,DVH差异).
主要成果:
- 与原始循环-GAN.相比,提出的方法实现了较低的MAE (79.52 HU) 和更高的PSNR (27.05 dB).
- 观察到MAE和PSNR的统计学上显著的改善.
- 拟议方法的全球马传导率为86.1% (1%/1毫米),97.1% (2%/2毫米) 和98.9% (3%/3毫米).
结论:
- 基于变压器的循环GAN具有结构保护损失,显著改善腹部sCT图像质量和剂量评估.
- 该方法即使在腹部癌症患者中存在复杂的解剖学和运动,也证明了它的有效性.
- 这种方法为使用仅MR数据的增强放射治疗规划提供了一个有希望的解决方案.
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