深度学习MRI仅用于骨盆,大脑和头癌的合成CT生成
David Bird1, Richard Speight1, Sebastian Andersson2
1Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.
概括
这项研究表明,深度学习生成的合成CT (sCT) 图像对于在各种癌症部位进行MRI-only辐射治疗的规划具有剂量准确性. 经过验证的循环-GAN模型为使用各种MRI数据进行准确的剂量计算提供了高临床实用性.
科学领域:
- 医学物理 医学物理
- 辐射疗法 辐射疗法
- 人工智能在医学中的应用
背景情况:
- 仅用MRI进行放射治疗的规划需要准确的合成CT (sCT) 生成用于剂量计算.
- 正在探索深度学习算法,以从MRI数据中创建剂量精确的sCT.
研究的目的:
- 为了验证通过深度学习算法生成的合成CTs (sCTs) 对骨盆,大脑和头部 (H&N) 癌症的剂量测量精度.
- 评估使用多个扫描仪的可变MRI数据用于sCT生成的可行性.
主要方法:
- 循环-GAN算法训练在配对的MRI-CT患者数据上.
- 输入MRI序列包括T2 (骨盆),T1Gd/T2 FLAIR (大脑) 和T1 (H&N).
- 在规划CT上计算了VMAT计划,并在生成的sCT上进行重新计算以进行验证.
主要成果:
- 平均绝对霍恩斯菲尔德单位 (HU) 误差在所有站点的可接受范围内.
- 平均初级计划目标体积 (PTV) D95%的剂量差异为<0.2%.
- 马指数通过率 (2%/2mm和1%/1mm) 分别超过99.6%和97.3%,风险器官 (OAR) 剂量差异最小 (<0.4%).
结论:
- 深度学习生成的sCT显示出多个癌症部位和MRI序列的卓越剂量测量准确性.
- 循环-GAN模型是sCT生成的临床可行方法,可适应来自不同扫描仪和序列的可变输入数据.
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