Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
107

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Comparative evaluation of the Mayo Clinic Florida microdosimetric kinetic model and mMKM for carbon ion treatment planning: A matRad-based analysis.

Journal of applied clinical medical physics·2026
Same author

Impact of LET-modifying planning objectives on the optimization of mixed-modality proton-photon treatments.

Medical physics·2026
Same author

Trading robustness: A scenario-free approach to robust multi-criteria optimization for treatment planning.

Medical physics·2026
Same author

Resource sharing and open-source software in radiation oncology: From challenges to opportunities for community-wide benefit.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2026
Same author

Multi-model study of fast VMAT segment dose calculation with deep learning.

Physics in medicine and biology·2026
Same author

Direct optimization of the probability of lesion origin in proton treatment planning for low-grade glioma patients.

Medical physics·2026

相关实验视频

Updated: Sep 20, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

20.5K

针对IMRT和IMPT治疗规划的无场景强大的优化算法.

Remo Cristoforetti1,2,3, Jennifer Josephine Hardt1,2,3, Niklas Wahl1,2

  • 1Department of Medical Physics in Radiation Oncology, German Cancer Research Center - DKFZ, Heidelberg, Germany.

Medical physics
|May 25, 2025
PubMed
概括

这项研究引入了用于放射治疗的新型无场景强大的优化算法,显著减少了计算时间和内存使用. 这种方法通过有效处理众多错误场景来改善剂量递送,从而提高了治疗规划.

关键词:
4D强大的优化优化坚固性 坚固性 坚固性治疗计划 治疗计划

更多相关视频

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

15.4K

相关实验视频

Last Updated: Sep 20, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
08:34

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

20.5K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

15.4K

科学领域:

  • 医学物理 医学物理
  • 计算生物学 计算生物学
  • 辐射疗法 辐射疗法

背景情况:

  • 强度调节质子疗法 (IMPT) 和强度调节辐射疗法 (IMRT) 的强大的治疗计划算法旨在通过结合错误场景来减少剂量不确定性.
  • 由于维度的诅咒,传统方法面临着计算方面的挑战,这使得它们对复杂的治疗计划具有潜在的限制.

研究的目的:

  • 提出一种无场景的概率性强大优化算法,以解决传统强度技术的运行时间和内存限制.
  • 开发一个高效的计算方法,用于放射治疗治疗规划.

主要方法:

  • 没有场景的方法优化了基于预期剂量分布和总方差的成本函数,使用预先计算的影响矩阵.
  • 这种方法避免存储单个错误场景,减少计算和内存负担.
  • 该算法在matRad中实现,并与光子和质子计划的传统稳健和基于边际的方法进行基准测试.

主要成果:

  • 无场景算法实现了与传统可靠方法相提并论的计划质量,同时减少了特定结构中的剂量标准偏差.
  • 与传统的强大方法相比,它证明了相当大的计算时间节省 (比传统的强大方法快5-600倍).
  • 运行时间和内存使用独立于错误场景的数量,类似于非强大的方法.

结论:

  • 一种使用预先计算的概率数量的新型无场景优化方法被成功开发和验证.
  • 这种方法提供了显著的计算优势,使其适用于复杂的3D和4D强大的优化,具有众多的错误场景或CT阶段.
  • 该方法与先进的不确定性建模保持兼容性,同时满足剂量和稳定性要求.