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基于深度学习的超级分辨率用于放射治疗中CBCT剂量减少.

Adrian Thummerer1, Lukas Schmidt1, Jan Hofmaier1

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深度学习超分辨率提高了放射治疗的低剂量形束计算断层扫描 (CBCT) 图像质量. 与投影域处理相比,图像域处理产生了更好的结果,使得更安全,更低辐射的扫描成为可能.

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圆束计算机断层扫描 (Cone Beam Computed Tomography) 是一种计算机断层扫描.深度学习是一种深度学习.辐射疗法 辐射疗法超级分辨率的超级分辨率

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科学领域:

  • 医疗成像医学成像
  • 放射治疗 物理 物理
  • 人工智能的人工智能

背景情况:

  • 圆束计算机断层扫描 (CBCT) 在放射治疗中至关重要,但由于电离辐射,尤其是在儿科患者中,它构成二次癌症风险.
  • 深度学习超分辨率 (SR) 已经提高了图像分辨率,但尚未应用于降低CBCT辐射剂量.

研究的目的:

  • 通过使用增强的超分辨率生成对抗网络 (ESRGAN) 来降低CBCT成像剂量.
  • 在低剂量CBCT中使用ESRGAN在投影和图像领域恢复图像质量.

主要方法:

  • 在2997个头癌CBCT扫描中训练了两个ESRGAN模型:一个在投影域 (CBCTSRpro) 和一个在图像域 (CBCTSRimg).
  • 对图像相似性,噪音,空间分辨率和注册准确性进行评估的SR CBCT与原始高剂量CBCT (CBCTHR) 相比.
  • 进行视觉图灵测试,以评估原始和SR CBCT之间的感知差异.

主要成果:

  • 投影和图像域SR都改善了低剂量CBCT的质量;视觉图灵测试显示了最小的感知差异.
  • 在视觉图灵测试中,CBCTSRimg略高于CBCTSRpro.
  • 与低剂量CBCT (CBCTLR: 0.66 lp/mm) 相比,SR方法显著提高了空间分辨率 (CBCTSRpro: 0.88 lp/mm,CBCTSRimg: 0.95 lp/mm),接近高剂量水平 (CBCTHR: 1.01 lp/mm).
  • 对SR CBCT的噪声特征和注册准确性与高剂量CBCT相似.

结论:

  • 深度学习超分辨率是减少放射治疗中CBCT剂量的可行方法.
  • 图像域SR处理产生比低剂量CBCT的投影域处理更高质量的图像.
  • 这种方法可以获得低剂量的CBCT,同时保持诊断图像质量,这对患者安全至关重要.