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在基于深度学习的合成CT生成中评估多个MRI序列,用于仅用于MR的头癌辐射疗法
Jacob Antunes1, Tony Young2, Dane Pittock1
1MIM Software Inc, Cleveland, OH, United States.
概括
使用前对比T1和迪克森MRI序列的深度学习模型为头癌放射治疗计划创建最好的合成CT图像. 这种方法可能使仅使用MRI的治疗规划成为可能,从而改善临床可行性.
科学领域:
- 医疗成像医学成像
- 放射治疗规划 放射治疗规划
- 人工智能在医学中的应用
背景情况:
- 合成计算机断层扫描 (sCT) 生产旨在从MRI数据中创建类似CT的图像,用于放射治疗规划.
- 优化输入MR序列对于提高基于深度学习的sCT的准确性和临床实用性至关重要.
研究的目的:
- 调查各种磁共振 (MR) 序列对头部和部区域深度学习生成的sCT质量的影响.
- 确定MR序列的最佳组合,以产生临床可行的sCTs.
主要方法:
- 训练了14个基于U-Net的深度学习模型,使用来自26名头癌患者的12个不同的MR系列 (T1pre-,T1post-contrast,T2和Dixon图像).
- 使用平均绝对误差 (MAE),子相似系数 (DSC) 和相比实际CT扫描的Gamma通过率来评估sCT质量.
主要成果:
- 虽然使用所有MR系列最小化了像素智能误差 (MAE = 80.5 HU),但导致文物增加和剂量计精度降低.
- T2加权成像通常导致质量较差的sCTs.
- 最佳模型使用了前对比T1和所有迪克森图像,在质量指标上实现了高性能 (例如,DSC=80.0%,Gamma Pass Rate=97.9%).
结论:
- 结合前对比T1和迪克森MRI图像的深度学习模型为头癌提供了最适合临床的sCT.
- 这种多道MRI输入策略显示,它有望在临床实践中实现仅MRI放射治疗的规划.
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