在基于CBCT的自适应性放射治疗和质子疗法中探索双能量CT合成:无噪声扩散概率模型的应用
David Viar-Hernandez1,2, Juan Manuel Molina-Maza2, Shaoyan Pan1
1Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America.
Physics in medicine and biology
|October 9, 2024
概括
这项研究引入了一种新的3D扩散模型,用于从形光束CT扫描中合成双能CT图像,改善适应性放射治疗规划和剂量计算的组织特征.
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:
- 适应性放射治疗 (ART) 需要精确的组织特征,以优化治疗和节省有风险的器官.
- 像束CT (CBCT) 这样的传统成像缺乏在质子疗法中准确剂量测量的分辨率.
- 增强成像对于提高辐射输送效率和患者安全至关重要.
研究的目的:
- 开发一种创新的方法来合成来自CBCT扫描的双能CT (DECT) 图像.
- 在适应性放射治疗规划中改进剂量计算和组织特征.
- 为了利用一种新的3D有条件无色化扩散概率模型 (DDPM) 来进行增强的图像合成.
主要方法:
- 使用基于3D DDPM的多解码器Swin-UNET架构.
- 该模型在54名头癌患者的CBCT-DECT配对数据集上进行了训练和验证.
- 扩散过程逐渐减少了CBCT扫描中的噪声和文物,以合成高分辨率DECT图像.
主要成果:
- 与基于GAN的方法相比,DDPM方法实现了优异的DECT图像合成,改善了信号噪声比和减少了文物.
- 在合成的高DECT (MAE 39.582 ± 0.855) 和低DECT (MAE 48.540 ± 1.833) 图像中,定量结果显示出高精度.
- 观察到组织特征和解剖学相似性的显著改善,对于精确的治疗规划至关重要.
结论:
- 这项研究提出了一种新的DDPM方法,用于CBCT-to-DECT图像合成在适应性放射治疗中.
- 合成的DECT图像与基本真相具有很高的相似性,使得精确的剂量计算成为可能.
- 这种技术为改进适应性质子和辐射疗法规划提供了一个有前途的途径.
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