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Anyone who has used a microwave oven knows there is energy in electromagnetic waves. Sometimes, this energy is obvious, such as in the summer sun's warmth. At other times, it is subtle, such as the unfelt energy of gamma rays, which can destroy living cells. Electromagnetic waves bring energy into a system through their electric and magnetic fields. These fields can exert forces and move charges in the system and, thus, do work on them. However, there is energy in an electromagnetic wave,...
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The work done to bring a charge through a distance r is given by the potential difference between the initial and the final position. To assemble a collection of point charges, the total work done can be expressed in terms of the product of each pair of charges divided by their separation distance, defined with respect to a suitable origin. Solving this expression gives the energy stored in a point charge distribution.
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An electric dipole is a system of two equal but opposite charges, separated by a fixed distance. This system is used to model many real-world systems, including atomic and molecular interactions. One of these systems is the water molecule, but only under certain circumstances. These circumstances are met inside a microwave oven, where electric fields with alternating directions make the water molecules change orientation. This vibration is equivalent to heat at the molecular level.
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基于深度学习的线性能量转移计算用于质子疗法.

Xueyan Tang1, Hok Wan Chan Tseung1, Douglas Moseley1

  • 1Department of Radiation Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States of America.

Physics in medicine and biology
|May 7, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型准确计算了质子治疗的剂量平均线性能量转移 (LETd),克服了传统方法的局限性. 这一进步使得更快,实时的生物剂量评估和治疗计划的优化成为可能.

关键词:
深度学习是一种深度学习.线性能量转移是线性的.质子疗法是一种质子疗法.

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科学领域:

  • 医学物理 医学物理
  • 辐射疗法 辐射疗法
  • 机器学习 机器学习

背景情况:

  • 计算线性能量转移 (LET) 的传统方法对于相对生物效率 (RBE) 至关重要,面临准确性与速度的权衡.
  • 蒙特卡洛模拟提供准确性,但计算密集,阻碍实时剂量优化.
  • 分析近似速度更快,但缺乏精度,影响治疗规划.

研究的目的:

  • 开发和原型一个深度学习模型来计算剂量平均LET (LETd).
  • 在质子疗法规划中实现实时生物剂量评估和LET优化.
  • 使用患者解剖学和剂量对水 (DW) 数据来准确生成LETd分布.

主要方法:

  • 开发了一个3D级联UNet深度学习模型.
  • 该模型处理了CT图像和剂量对水 (DW) 数据,以生成LETd分布.
  • 用于培训,验证和测试,使用了275个前列腺质子立体性身体放射治疗计划.

主要成果:

  • 深度学习模型准确地推断了LETd分布,计算在NVIDIA A100 GPU上每场大约需要100 ms.
  • 该模型实现了0.94 ± 0.14 MeV cm-1的平均平均绝对误差 (MAE) 和与蒙特卡洛模拟相比的97.4% ± 1.3%的马通过率.
  • 差异很小,主要是在高剂量梯度和低计数统计数据的场边观察到的.

结论:

  • 深度学习模型可以有效和准确地计算LETd作为快速前进的方法.
  • 开发的模型显示了在质子治疗计划中优化相对生物效率 (RBE) 的巨大潜力.
  • 未来的工作重点是提高模型性能,并评估其适应多种临床环境的适应性.