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监督与无监督GAN用于脑MR指导放射治疗中的伪CT合成

Milad Zeinali Kermani¹, Mohamad Bagher Tavakoli², Amir Khorasani³

¹Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Physical and engineering sciences in medicine

|July 22, 2025

概括

监督深度学习 (Pix2Pix) 准确地合成了用于放射治疗的MRI的伪CT图像,优于无监督方法 (CycleGAN). 这种只有MR的方法支持脑瘤治疗规划的临床可行性.

科学领域:

医学成像和人工智能医学成像和人工智能
放射治疗治疗计划放射治疗治疗计划
用于医学图像合成的深度学习.

背景情况:

放射治疗对于治疗脑瘤至关重要.
基于CT的规划存在局限性;仅MR的放射治疗是一种新兴的替代方案.
准确的MR-to-CT合成对于MR-only放射治疗至关重要.

研究的目的:

为了比较MR-to-CT合成的监督 (Pix2Pix) 和无监督 (CycleGAN) 深度学习.
评估由MRI生成的伪CT图像的质量和临床可行性.
评估用于PCT生成的T1和T2加权MR序列的性能.

主要方法:

训练Pix2Pix (监督) 和CycleGAN (无监督) 模型在3270张配对的MRI和CT图像上.
从T1和T2权重的MR序列生成的pCT图像.
使用SSIM,PSNR和MAE评估图像质量;进行了剂量测量评估.

主要成果:

在生成高保真性pCT图像方面,Pix2Pix显著优于CycleGAN (p < 0.05).
在T1图像上的Pix2Pix的平均SSIM,PSNR和MAE为0.964±0.03,32.812±5.21和79.681±9.52 HU.
在T1和T2MR序列之间的pCT生成效率没有显著差异 (p > 0.05);剂量计评估显示了可比的剂量分布.

结论:

监督和无监督的方法都可以从传统的MR序列生成准确的pCT图像.
监督的Pix2Pix提供了更高的准确性,而无监督的CycleGAN则为未配对的数据场景提供了灵活性.
使用合成的pCT图像进行MR-only放射治疗的计划在临床上是可行的.

关键词:

深度学习是一种深度学习.生成性的对抗性网络.辐射疗法辐射疗法合成CTCT是一种合成的CTCT.治疗计划治疗计划

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