从CBCT中生成合成CT的贝叶斯无监督生成,使用基于患者特定得分的先前结果
Junbo Peng1, Yuan Gao1, Chih-Wei Chang1
1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Medical physics
|December 12, 2024
概括
这项研究开发了一个无监督的扩散模型,从形光束CT (CBCT) 扫描中创建合成CT (sCT) 图像,显著减少工件并改善适应性放射治疗 (ART) 的图像质量. 该方法增强了CBCT,以便更好地进行定量分析和治疗规划.
科学领域:
- 医疗成像医学成像
- 放射治疗 物理 物理
- 医疗保健中的机器学习
背景情况:
- 圆束计算断层扫描 (CBCT) 对于图像导向放射治疗 (IGRT) 是至关重要的,但它遭受了文物和不准确的霍恩斯菲尔德单位 (HU) 的损害.
- 这些局限性阻碍了定量应用,如细分和剂量计算,这对于适应性放射治疗 (ART) 是必不可少的.
- 改善CBCT图像质量对于实施在线ART协议至关重要.
研究的目的:
- 开发一种无监督的,针对患者的扩散模型,用于从CBCT生成合成CT (sCT) 图像.
- 目标是提高CBCT图像质量,以改善放射治疗中的定量分析.
主要方法:
- 一个无监督的框架,使用患者特定的基于分数的扩散模型作为图像先验.
- 定制的总变化 (TV) 正规化强制执行切片连贯性.
- 该模型在患者的计划CT (pCT) 上接受训练,以学习独特的解剖特征.
主要成果:
- 在头部和部,胰腺和肺癌患者数据中,CBCT人工物显著减少.
- 在肺SBRT中,平均绝对误差 (MAE) 从47HU提高到13HU,不均性 (NU) 从45HU提高到14dB,SSIM从0.58提高到0.67.
- 提出的方法表现优于其他基于无监督学习的CBCT校正算法.
结论:
- 无监督扩散模型有效地从CBCT生成合成CT (sCT),具有减少的文物和精确的HU值.
- 这使得在线适应性放射治疗 (ART) 的精确CBCT引导细分和重新规划成为可能.
- 该方法有望在临床放射治疗工作流程中推进定量成像.
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