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使用潜在扩散模型进行对比增强的图像合成,用于精确的在线瘤划分,用于脑转移的MRI导向适应性放射治疗

Xiangyu Ma¹, Yuchao Ma¹, Yu Wang¹

¹Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China.

Physics in medicine and biology

|June 25, 2025

概括

一种新方法产生了合成对比增强的MRI图像,提高了适应性放射治疗中大脑转移线划分的准确性. 这提高了治疗精度,并减少了接受长期放射治疗的患者的毒性.

科学领域:

医疗成像医学成像
辐射疗法辐射疗法
人工智能的人工智能

背景情况:

磁共振成像指导的适应性放射治疗 (MRIgART) 对于治疗大体积大脑转移 (BM) 是至关重要的.
精确的BM划分依赖于对比度增强的T1加权 (T1CE) MRI,在线治疗期间通常无法使用.
这种限制阻碍了适应性放射治疗的及时调整.

研究的目的:

开发一种合成T1CE (sT1CE) 图像生成方法.
为了实现MRIgART的准确的在线自适应性BM划定.
为了改善治疗结果,并尽量减少BM患者的毒性.

主要方法:

开发了一个新的ControlNet合潜伏扩散模型 (CTN-LDM).
采用了个性化转移学习和扩散模型反转.
sT1CE图像是从T2加权或FLAIRMRI扫描中生成的.

主要成果:

与其他深度学习模型相比,CTN-LDM生成了优质的st1CE图像.
放射瘤学家通过使用st1CE图像 (迪斯得分0.93±0.02对比0.86±0.04) 在BM划分方面取得了显著更高的精度.
该方法在图像质量和划线精度方面显示了定量和质量的改进.

结论:

拟议的CTN-LDM方法产生高质量的st1CE图像.
这显著提高了MRIgART在线适应性瘤划分的准确性,用于大容量的BM.
这种方法有可能提高治疗疗效和降低毒性.

关键词:

以MRI为指导的自适应性放射治疗.大脑转移是大脑转移.使用对比度增强的图像生成.扩散模型的扩散模型.瘤细分瘤细分

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