Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients

Wang C.; Li X.; Chang Y.; Sheng Y.; Zhang J.; Yin F. F.; Wu Q. J. J.

首页> 外文期刊>International Journal of Radiation Oncology, Biology, Physics >Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients

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Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients

机译：快速自动IMRT规划，使用级联密集卷积神经网络（CDCNN）：使用深层学习前列腺IMRT患者的流量地图预测的可行性研究

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《International Journal of Radiation Oncology, Biology, Physics》 |2019年第1suppla期|共2页
作者
Wang C.; Li X.; Chang Y.; Sheng Y.; Zhang J.; Yin F. F.; Wu Q. J. J.;
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Duke Univ Med Ctr Durham NC USA;

Duke Univ Med Ctr Durham NC USA;

Duke Univ Med Ctr Durham NC USA;

Duke Univ Med Ctr Durham NC USA;

Duke Univ Med Ctr Durham NC USA;

Duke Univ Med Ctr Durham NC USA;

Duke Univ Med Ctr Durham NC USA;

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正文语种 eng
中图分类放射医学;
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1. Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients [J] . Wang C., Li X., Chang Y., International Journal of Radiation Oncology, Biology, Physics . 2019,第1Suppla期

机译：快速自动IMRT规划，使用级联密集卷积神经网络（CDCNN）：使用深层学习前列腺IMRT患者的流量地图预测的可行性研究
2. A Deep Convolutional Neural Network Approach for IMRT Dose Distribution Prediction in Prostate Cancer Patients [J] . Kajikawa T., Kadoya N., Ito K., Medical Physics . 2018,第6期

机译：前列腺癌患者IMRT剂量分布预测的深度卷积神经网络方法
3. Automatic IMRT Planning Via Static Field Fluence Prediction (AIP-SFFP): A Deep Learning Method for Real-Time Prostate Treatment Planning [J] . Li X., Zhang J., Sheng Y., Medical Physics . 2019,第6期

机译：通过静态场测量预测（AIP-SFFP）自动IMRT规划：实时前列腺治疗规划的深度学习方法
4. A warm-start optimization strategy for IMRT parameters modification in fluence map optimization (FMO) of template-based autoplanning platform: a feasibility study [C] . Xiaotian Huang, Lei Deng, Hong Quan, International Conference on Medical Imaging Physics and Engineering . 2019

机译：基于模板的自动计划平台的注量图优化（FMO）中IMRT参数修改的热启动优化策略：可行性研究
5. On-line re-optimization of prostate IMRT plan for adaptive radiation therapy: A feasibility study and implementation. [D] . Thongphiew, Danthai. 2008

机译：用于适应性放射治疗的前列腺IMRT计划的在线重新优化：可行性研究和实施。
6. A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients [O] . Tomohiro Kajikawa, Noriyuki Kadoya, Kengo Ito, 2019

机译：一种卷积神经网络方法预测前列腺癌患者的IMRT剂量分布
7. Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients [O] . C. Wang, X. Li, Y. Chang, 2019

机译：快速自动IMRT规划，使用级联密集卷积神经网络（CDCNN）：使用深层学习前列腺IMRT患者的流量地图预测的可行性研究

Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients

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