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31 results on '"Mao, Ronghu"'

3. Generating Synthesized CT from Cone-Beam Computed Tomography (CBCT) Using Artifact Disentanglement Network for Image-Guided Radiotherapy (IGRT)

Author

Cheng, Hanlin, Liu, Jiwei, Liu, Jianfei, Mao, Ronghu, Sun, Pengjian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Zhang, Weicun, editor, and Fu, Yongling, editor

Published
2021
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4. Study on Dose Distribution Prediction of Esophageal Cancer Patients Using U-Net Model

Author

Wang, Jing, Mao, Ronghu, Liu, Jiwei, Liu, Jianfei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2020
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5. Superficial Dosimetry Study of the Frequency of Bolus Using in Volumetric Modulated Arc Therapy after Modified Radical Mastectomy.

Author

Tian, Lingling, Mao, Ronghu, Li, Dingjie, Guo, Wei, Li, Bing, Lou, Zhaoyang, and Guo, Leiming

Subjects
VOLUMETRIC-modulated arc therapy, BREAST, DOSE-response relationship (Radiation), MEDICAL dosimetry, MASTECTOMY, BREAST cancer, COMPUTED tomography
Abstract

Objective: To investigate the effect of various frequencies of bolus use on the superficial dose of volumetric modulated arc therapy after modified radical mastectomy for breast cancer. Methods: Based on the computed tomography images of a female anthropomorphic breast phantom, a 0.5 cm silicone-based 3D-printed bolus was created. Nine points evenly distributed on the breast skin were selected for assessing the skin dose, and a volume of subcutaneous lymphatic drainage of the breast (noted as ROI2-3) was delineated for assessing the chest wall dose. The treatment plans with and without bolus (plan_wb and plan_nb) were separately designed using the prescription of 50 Gy in 25 fractions following the standard dose constraints of the adjacent organ at risk. To characterize the accuracy of treatment planning system (TPS) dose calculations, the doses of the nine points were measured five times by thermoluminescence dosimeters (TLDs) and then were compared with the TPS calculated dose. Results: Compared with Plan_nb (144.46 ± 10.32 cGy), the breast skin dose for plan_wb (208.75 ± 4.55 cGy) was significantly increased (t = −18.56, P < 0.001). The deviation of skin dose was smaller for Plan_wb, and the uniformity was significantly improved. The calculated value of TPS was in good agreement with the measured value of TLD, and the maximum deviation was within 5%. Skin and ROI2-3 doses were significantly increased with increasing frequencies of bolus applications. The mean dose of the breast skin and ROI2-3 for 15 and 23 times bolus applications were 45.33 Gy, 50.88 Gy and 50.36 Gy, 52.39 Gy, respectively. Conclusion: 3D printing bolus can improve the radiation dose and the accuracy of the planned dose. Setting Plan_wb to 15 times for T1-3N+ breast cancer patients and 23 times for T4N+ breast cancer patients can meet the clinical need. Quantitative analysis of the bolus application frequency for different tumor stages can provide a reference for clinical practice. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Automatic Cell Segmentation and Signal Detection in Fluorescent in Situ Hybridization

Author

Wang, Jing, Liu, Jiwei, Liu, Jianfei, Yan, Hui, Mao, Ronghu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2019
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7. A Planning Evaluation Method for Esophageal VMAT Based on Machine Learning

Author

Liu, Muya, Liu, Jiwei, Liu, Jianfei, Yan, Hui, Mao, Ronghu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2019
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8. Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions

Author

Zhang, Jingjing, Liu, Shuolin, Li, Teng, Mao, Ronghu, Du, Chi, Liu, Jianfei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Dan, editor, Xing, Lei, editor, and Jiang, Steve, editor

Published
2019
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9. One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning

Author

Jiang, Dashan, Li, Teng, Mao, Ronghu, Du, Chi, Liu, Yongbin, Liu, Shuolin, Liu, Jianfei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nguyen, Dan, editor, Xing, Lei, editor, and Jiang, Steve, editor

Published
2019
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11. Multi-omics and Multi-VOIs to Predict Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy

Author

Guo, Wei, primary, Li, Bing, additional, Xu, Wencai, additional, Cheng, Chen, additional, Qiu, Chengyu, additional, Sam, Sai-kit, additional, Zhang, Jiang, additional, Teng, Xinzhi, additional, Meng, Lingguang, additional, Zheng, Xiaoli, additional, Wang, Yuan, additional, Lou, Zhaoyang, additional, Mao, Ronghu, additional, Lei, Hongchang, additional, Zhang, Yuanpeng, additional, Zhou, Ta, additional, Cai, Jing, additional, and Ge, Hong, additional

Published
2023
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23. Improvement Using Planomics Features on Prediction and Classification of Patient-Specific Quality Assurance Using Head and Neck Volumetric Modulated Arc Therapy Plan

Author

Li, Bing, primary, Chen, Junying, additional, Guo, Wei, additional, Mao, Ronghu, additional, Zheng, Xiaoli, additional, Cheng, Xiuyan, additional, Cui, Tiantian, additional, Lou, Zhaoyang, additional, Wang, Ting, additional, Li, Dingjie, additional, Tao, Hongyan, additional, Lei, Hongchang, additional, and Ge, Hong, additional

Published
2021
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31. Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning.

Author

Jiang D, Li T, Mao R, Du C, and Liu J

Subjects
Humans, Radiometry, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted, Deep Learning, Organs at Risk, Radiotherapy, Intensity-Modulated
Abstract

Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly guided by the dose-volume histogram (DVH), which evaluates dosimetry at planning target volume (PTV) and organs at risk (OARs). DVH is highly correlated with the geometrical relationship between PTV and OARs, which motivates us to explore deep learning techniques to model such correlation and predict DVHs of different OARs. Distance to target histogram (DTH) is chosen to measure the geometrical relationship between PTV and OARs. DTH and DVH features are then undergone dimension reduction by autoencoder. The reduced feature vectors are finally imported into deep belief network to model the correlation between DTH and DVH. This correlation can be used to predict DVH of the corresponding OAR for new patients. Validation results revealed that the relative dose difference of the predicted and clinical DVHs on four different OARs were less than 3%. These promising results suggested that the predicted DVH could provide near-optimal parameters to significantly reduce the planning time.

Published
2019
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