Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy
Author:
Affiliation:
1. Department of Radiation Oncology The University of Texas Health Science Center at San Antonio San Antonio Texas USA
2. Department of Management Science and Statistics The University of Texas at San Antonio San Antonio Texas USA
Publisher
Wiley
Subject
Radiology, Nuclear Medicine and imaging,Instrumentation,Radiation
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/acm2.13667
Reference38 articles.
1. Complexity in Radiation Therapy: It's Complicated
2. Evaluation of the Elekta Agility MLC performance using high-resolution log files
3. Prediction of VMAT delivery accuracy with textural features calculated from fluence maps
4. Analysis of direct clinical consequences of MLC positional errors in volumetric-modulated arc therapy using 3D dosimetry system
5. Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research;Journal of Critical Care;2024-08
2. Quantification of Treatment Plan Deliverability in Breast Volumetric-modulated Arc Therapy With Agility Multi-leaf Collimator;In Vivo;2024
3. Dosimetric impact of MLC positional errors on dose distribution in IMRT;Journal of Applied Clinical Medical Physics;2023-09-18
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