SC-GAN:结构完成生成对抗网络,用于从截断解剖的MR图像中生成合成CT图像
Xinru Chen1, Yao Zhao1, Laurence E Court1
1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
概括
一个新的结构完成生成对抗网络 (SC-GAN) 从截断的MR图像创建完整的合成CT (sCT). 这通过恢复缺失的解剖信息来提高MR导向放射治疗规划的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 辐射瘤学 辐射瘤学
背景情况:
- 磁共振 (MR) 影像对于放射治疗计划至关重要.
- 截断的MRI图像限制了合成CT (sCT) 生成的准确性.
- 不完整的sCT导致MR引导的适应性规划中剂量计算错误.
研究的目的:
- 开发一个新的结构-完成生成对抗网络 (SC-GAN) 来从截断的MR图像中生成完整的sCT.
- 为了提高基于MR的适应性辐射疗法规划的sCT的准确性.
主要方法:
- 拟议的SC-GAN包括一个身体面具和一个截断损失,用于解剖学补偿.
- 身体面具是从模拟CT生成的,并注册到MR图像中.
- 使用自动细分或边缘检测来惩罚轮差异.
主要成果:
- 与CycleGAN和条件GAN相比,SC-GAN在sCT生成中显示出显著提高的准确性.
- 该方法有效地恢复了截断区域中缺失的解剖细节.
- 在生成的sCT的截断和未截断区域中观察到更高的准确性.
结论:
- SC-GAN成功地从截断的MRI图像中生成了解剖学上完整的sCT.
- 这种方法提高了基于MR的适应性放射治疗计划的可靠性.
- 开发的方法为克服截断的MR数据的局限性提供了一个有希望的解决方案.
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