基于扩散模型的对比增强CT合成用于乳腺癌放射治疗:继续进行无对比成像
Chengjian Xiao1, Chaozhe Cen2, Ji Zhang3
1Department of Radiation Oncology, Ganzhou Cancer Hospital, Ganzhou, People's Republic of China.
Science progress
|November 27, 2025
概括
新的SynDiff深度学习模型有效地将非对比计算机断层扫描 (NCCT) 转换为对比增强计算机断层扫描 (CECT) 图像,用于乳腺癌放射治疗. 这种方法表现出卓越的性能,提高了治疗计划的图像质量.
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:
- 准确的成像对于有效的乳腺癌放射治疗至关重要.
- 从非对比CT (NCCT) 过渡到对比增强CT (CECT) 可以改善可视化,但需要对比剂.
- 深度学习为图像合成提供了潜在的可能性,以弥合这一差距.
研究的目的:
- 开发和评估一个新的深度学习框架,用于从NCCT到CECT的图像对图像翻译.
- 评估该框架在乳腺癌放射治疗的背景下的有效性.
- 为了比较不同深度学习模型的性能,包括SynDiff,Pix2Pix,CycleGAN和RegGAN.
主要方法:
- 利用了194名NCCT和CECT扫描对患者的数据集.
- 训练并验证了四种深度学习模型:Pix2Pix,CycleGAN,RegGAN和SynDiff.
- 采用了定量指标,如峰值信号与噪声比 (PSNR),结构相似度指数 (SSIM) 和规范平均绝对误差 (NMAE) 进行性能评估.
主要成果:
- 在内部和外部验证队列中,SynDiff表现出卓越的性能.
- 与其他模型相比,SynDiff实现了最高的PSNR和SSIM值,以及最低的NMAE.
- 在大多数比较中,统计分析证实了SynDiff的显著优势 (p < 0.05).
结论:
- SynDiff模型显示了从NCCT数据中合成CECT图像的显著前景.
- 这种深度学习方法可能有助于提高乳腺癌放射治疗的规划和实施.
- SynDiff为改善放射治疗工作流程中的图像质量提供了一个可行的替代方案.
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