基于CBCT的合成CT图像生成,使用条件无声扩散概率模型
Junbo Peng1,2, Richard L J Qiu1, Jacob F Wynne1
1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Medical physics
|August 30, 2023
概括
这项研究引入了一种新的AI模型,该模型将低质量的束CT (CBCT) 扫描转换为高质量的合成CT (sCT) 图像. 这一进步提高了适应性放射治疗 (ART) 计划和剂量计算的图像质量.
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:
- 圆束计算断层扫描 (CBCT) 对于图像导向放射治疗 (IGRT) 和适应性放射治疗 (ART) 是至关重要的,因为它经常用于患者定位.
- CBCT扫描有严重的文物和不准确的Hounsfield单位 (HU) 值,限制了它们在器官细分和剂量计算等定量应用中的使用.
- 提高CBCT质量达到CT扫描标准对于实现在线ART临床实践至关重要.
研究的目的:
- 开发图像转换的条件扩散模型,通过将其转换为CT分布来提高CBCT图像质量.
- 通过提高图像保真度,使CBCT数据的定量应用成为可能.
主要方法:
- 采用了一种有条件的无声扩散概率模型 (DDPM),具有时间嵌入式U-net架构,结合剩余和注意力块.
- 该模型被训练在来自大脑和头部 (H&N) 患者研究的配对变形规划CT (dpCT) 和CBCT图像上.
- 使用平均绝对误差 (MAE),峰值信号与噪声比率 (PSNR) 和规范交叉相关性 (NCC) 评估性能,将生成的合成CT (sCT) 与CBCT和其他生成模型进行比较.
主要成果:
- 拟议的方法显著减少了人造物质,并改善了与原始CBCT扫描相比,生成的sCT图像中的HU精度,无论是在大脑还是H&N患者研究中.
- 定量指标显示出优异的表现:在大脑研究中,MAE从40.63 HU提高到25.99 HU,PSNR从27.87 dB增加到30.49 dB.
- 该方法在视觉和定量评估方面都超过了其他四种扩散模型和一个循环生成对抗网络 (Cycle GAN).
结论:
- 有条件的DDPM有效地从CBCT生成合成CT (sCT),产生精确的Hounsfield单位和减少的文物.
- 这种图像质量的提高促进了基于CBCT的精确器官细分和剂量计算.
- 开发的方法对于在线适应性放射治疗 (ART) 的推进至关重要,因为它可以直接从CBCT数据中进行可靠的定量分析.
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