Littérature scientifique sur le sujet « Respiratory motion prediction »
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Articles de revues sur le sujet "Respiratory motion prediction"
Dürichen, R., T. Wissel, F. Ernst, A. Schlaefer et A. Schweikard. « Multivariate respiratory motion prediction ». Physics in Medicine and Biology 59, no 20 (25 septembre 2014) : 6043–60. http://dx.doi.org/10.1088/0031-9155/59/20/6043.
Texte intégralErnst, Floris, Alexander Schlaefer et Achim Schweikard. « Predicting the outcome of respiratory motion prediction ». Medical Physics 38, no 10 (22 septembre 2011) : 5569–81. http://dx.doi.org/10.1118/1.3633907.
Texte intégralRen, Qing, Seiko Nishioka, Hiroki Shirato et Ross I. Berbeco. « Adaptive prediction of respiratory motion for motion compensation radiotherapy ». Physics in Medicine and Biology 52, no 22 (26 octobre 2007) : 6651–61. http://dx.doi.org/10.1088/0031-9155/52/22/007.
Texte intégralErnst, F., R. Dürichen, A. Schlaefer et A. Schweikard. « Evaluating and comparing algorithms for respiratory motion prediction ». Physics in Medicine and Biology 58, no 11 (16 mai 2013) : 3911–29. http://dx.doi.org/10.1088/0031-9155/58/11/3911.
Texte intégralIchiji, Kei, Noriyasu Homma, Masao Sakai, Yuichiro Narita, Yoshihiro Takai, Xiaoyong Zhang, Makoto Abe, Norihiro Sugita et Makoto Yoshizawa. « A Time-Varying Seasonal Autoregressive Model-Based Prediction of Respiratory Motion for Tumor following Radiotherapy ». Computational and Mathematical Methods in Medicine 2013 (2013) : 1–9. http://dx.doi.org/10.1155/2013/390325.
Texte intégralJöhl, Alexander, Yannick Berdou, Matthias Guckenberger, Stephan Klöck, Mirko Meboldt, Melanie Zeilinger, Stephanie Tanadini-Lang et Marianne Schmid Daners. « Performance behavior of prediction filters for respiratory motion compensation in radiotherapy ». Current Directions in Biomedical Engineering 3, no 2 (7 septembre 2017) : 429–32. http://dx.doi.org/10.1515/cdbme-2017-0090.
Texte intégralRasheed, Asad, et Kalyana C. Veluvolu. « Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link ». Mathematics 12, no 4 (16 février 2024) : 588. http://dx.doi.org/10.3390/math12040588.
Texte intégralFujii, Tatsuya, Norihiro Koizumi, Atsushi Kayasuga, Dongjun Lee, Hiroyuki Tsukihara, Hiroyuki Fukuda, Kiyoshi Yoshinaka et al. « Servoing Performance Enhancement via a Respiratory Organ Motion Prediction Model for a Non-Invasive Ultrasound Theragnostic System ». Journal of Robotics and Mechatronics 29, no 2 (20 avril 2017) : 434–46. http://dx.doi.org/10.20965/jrm.2017.p0434.
Texte intégralYang, Dongrong, Yuhua Huang, Bing Li, Jing Cai et Ge Ren. « Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks : A Proof-of-Concept Study ». Cancers 15, no 24 (8 décembre 2023) : 5768. http://dx.doi.org/10.3390/cancers15245768.
Texte intégralZhang, Xiangyu, Xinyu Song, Guangjun Li, Lian Duan, Guangyu Wang, Guyu Dai, Ying Song, Jing Li et Sen Bai. « Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor ». Technology in Cancer Research & ; Treatment 21 (janvier 2022) : 153303382211432. http://dx.doi.org/10.1177/15330338221143224.
Texte intégralThèses sur le sujet "Respiratory motion prediction"
Lee, Suk Jin. « PREDICTION OF RESPIRATORY MOTION ». VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/336.
Texte intégralAlnowami, Majdi Rashed S. « Adaptive modelling and prediction of respiratory motion in external beam radiotherapy ». Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.582747.
Texte intégralPark, Seonyeong. « Respiratory Prediction and Image Quality Improvement of 4D Cone Beam CT and MRI for Lung Tumor Treatments ». VCU Scholars Compass, 2017. http://scholarscompass.vcu.edu/etd/5046.
Texte intégralLi, Yang. « Patient-specific gating scheme for thoracoabdominal tumor radiotherapy guided by magnetic resonance imaging ». Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS015.
Texte intégralThe ultimate aim of this paper is to develop an end-to-end gating system for real-time motion compensation during lung cancer and liver cancer treatment on the Elekta Unity. This system will monitor and automatically locate the three-dimensional spatial position of the tumor in real-time, and predict the tumor’s motion trajectory in the Superior-Inferior (SI), Left-Right (LR), and Anterior-Posterior (AP) directions in advance. Based on the set gating rules, a unique gating signal will be generated to control the beam on and off during radiotherapy, thereby compensating for the inaccuracy of dose delivery due to respiratory motion. To achieve this goal, the following steps have been carried out : 1. We proposed a tumor tracking workflow based on KCF, addressing the issues of time consumption and accuracy in tumor tracking using 2D Cine-MRI. Firstly, we verified the efficiency and accuracy of KCF in 2D Cine-MRI tumor tracking. By calculating the centroid, we improved the situation where the fixed-size template generated errors when the tumor shape changed, thus enhancing the tracking accuracy. In particular, we focused on the tracking in the SI direction by optimizing the selection of coronal slices or sagittal slices to determine the optimal position of the tumor in the SI direction. 2. We proposed a patient-specific transfer C-NLSTM model for real-time prediction of tumor motion, addressing the issue of insufficient training data. We constructed a C-NLSTM model, and introduced transfer learning to fully leverage the rich knowledge and feature representation capabilities embedded in the pretrained model, while fine-tuning is conducted based on specific patient data to achieve high-precision prediction of tumor motion. Through this approach, the model can be trained with only two minutes of patient-specific data, effectively overcoming the challenge of data acquisition. 3. We proposed an efficient gating signal prediction method, overcoming the challenge of precise predictions in 2D Cine-MRI with limited sampling frequencies. We validated the effectiveness of linear regression for predicting internal organ or tumor motion in 2D MR cine. And we proposed an online gating signal prediction scheme based on ALR to enhance the accuracy of gating radiotherapy for liver and lung cancers. 4. We proposed an end-to-end gating system based on 2D Cine-MRI for the Elekta Unity MRgRT. It enables real-time monitoring and automatic localization of the tumor’s 3D spatial position, prediction of tumor motion in three directions, and fitting an optimal cuboid (gating threshold) for each patient based on the tumor’s motion range. Additionally, we explored various approaches to derive 3D gating signals based on tumor motion in one, two, or three directions, aiming to cater to different patient treatment needs. Finally, the results of dosimetric validation demonstrate that the proposed system can effectively enhance the protection of OAR
Abdelhamid, S. « Respiratory motion modelling and predictive tracking for adaptive radiotherapy ». Thesis, Coventry University, 2010. http://curve.coventry.ac.uk/open/items/f135cb12-e9f9-1e4f-9c57-6de2fc378069/1.
Texte intégralAkimoto, Mami. « Predictive uncertainty in infrared marker-based dynamic tumor tracking with Vero4DRT ». Kyoto University, 2015. http://hdl.handle.net/2433/199176.
Texte intégralCHEN, KE-WEI, et 陳科維. « The Feasibility Analysis of Predicting Tumor Position in vivo by Respiratory Motion Track of Body Surface Markers in Radiotherapy ». Thesis, 2018. http://ndltd.ncl.edu.tw/handle/6m6u54.
Texte intégral國立中正大學
資訊工程研究所
106
According to the data of Ministry of Health and Welfare announced in 2017, malignant tumor has been the main cause of death for 36 consecutive years in Taiwan. The most common treatment for malignant tumors is radiation therapy, but during the course the tumor moves with the patient's respiratory motion, which may make the therapy less effective. How to reduce the uncertainty of the respiratory movement of a tumor has been an important issue in radiation therapy. A commonly used method in the clinic is to use a positioning marker placed on the body surface to track the respiratory state, and repeat the irradiation to the tumor at the same state to reduce the uncertainty caused by the breathing. In this study, we experimentally investigated whether an in vitro fiducial marker on the body surface (BSFM) could be used to predict the trajectory of an in vivo tumor with respiratory motions. We use an infrared camera and a visible camera to record the three-dimensional coordinates of the externally-reflected infrared-reflecting points moving in the image space with the breathing, and locate both BSFM and tumor from 10 sets of 4DCT images with different breathing depths. The corresponding mapping between the coordinates obtained from the optical images and the 4DCT images can be divided into two parts. The first part is the mapping from optical images to 4DCT images of the BSFM locations. The second part is the mapping from BSFM locations to tumor locations in the 4DCT images. Two parts of mapping changing with the time are approximated by polynomial fitting. Eventually the coordinates of tumor in 4D-CT can be estimated from the coordinates of BSFM in the image space of cameras. When patients receive radiotherapy, such technique allows us to directly infer the position and motion of an in vivo tumor from its BSFM captured by the outside video cameras.
Livres sur le sujet "Respiratory motion prediction"
Lee, Suk Jin, et Yuichi Motai. Prediction and Classification of Respiratory Motion. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41509-8.
Texte intégralLee, Suk Jin, et Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer Berlin / Heidelberg, 2013.
Trouver le texte intégralLee, Suk Jin, et Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer, 2016.
Trouver le texte intégralLee, Suk Jin, et Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer London, Limited, 2013.
Trouver le texte intégralChapitres de livres sur le sujet "Respiratory motion prediction"
Lee, Suk Jin, et Yuichi Motai. « Review : Prediction of Respiratory Motion ». Dans Prediction and Classification of Respiratory Motion, 7–37. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_2.
Texte intégralLee, Suk Jin, et Yuichi Motai. « Customized Prediction of Respiratory Motion ». Dans Prediction and Classification of Respiratory Motion, 91–107. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_5.
Texte intégralLee, Suk Jin, et Yuichi Motai. « Respiratory Motion Estimation with Hybrid Implementation ». Dans Prediction and Classification of Respiratory Motion, 67–89. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_4.
Texte intégralVedam, Sastry. « Respiratory Motion Prediction in Radiation Therapy ». Dans 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, 285–96. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36441-9_12.
Texte intégralLee, Suk Jin, et Yuichi Motai. « Introduction ». Dans Prediction and Classification of Respiratory Motion, 1–5. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_1.
Texte intégralLee, Suk Jin, et Yuichi Motai. « Phantom : Prediction of Human Motion with Distributed Body Sensors ». Dans Prediction and Classification of Respiratory Motion, 39–66. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_3.
Texte intégralLee, Suk Jin, et Yuichi Motai. « Irregular Breathing Classification from Multiple Patient Datasets ». Dans Prediction and Classification of Respiratory Motion, 109–33. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_6.
Texte intégralLee, Suk Jin, et Yuichi Motai. « Conclusions and Contributions ». Dans Prediction and Classification of Respiratory Motion, 135–37. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_7.
Texte intégralKlinder, Tobias, Cristian Lorenz et Jörn Ostermann. « Prediction Framework for Statistical Respiratory Motion Modeling ». Dans Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 327–34. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15711-0_41.
Texte intégralFuerst, B., T. Mansi, Jianwen Zhang, P. Khurd, J. Declerck, T. Boettger, Nassir Navab, J. Bayouth, Dorin Comaniciu et A. Kamen. « A Personalized Biomechanical Model for Respiratory Motion Prediction ». Dans Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 566–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33454-2_70.
Texte intégralActes de conférences sur le sujet "Respiratory motion prediction"
Shoujun Zhou, Qubo Zheng, Hongliang Li, Yueqian Zhou et Yuan Hong. « Probabilistic respiratory motion prediction ». Dans 2010 International Conference of Medical Image Analysis and Clinical Application (MIACA). IEEE, 2010. http://dx.doi.org/10.1109/miaca.2010.5528497.
Texte intégralYang, Jun, Zhengbo Zhang, Shoujun Zhou et Hongnan Yin. « Respiratory Motion Prediction Based on Maximum Posterior Probability ». Dans 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5163345.
Texte intégralTanner, Christine, Koen Eppenhof, Jaap Gelderblom et Gabor Szekely. « Decision fusion for temporal prediction of respiratory liver motion ». Dans 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6867966.
Texte intégralBao, Xuezhi, Deqiang Xiao, Baochun He, Wenchao Gao, Junliang Wang et Fucang Jia. « Prediction of Liver Respiratory Motion Based on Machine Learning ». Dans 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. http://dx.doi.org/10.1109/robio49542.2019.8961688.
Texte intégralAlnowam, M. R., E. Lewis, K. Wells et M. Guy. « Respiratory motion modelling and prediction using probability density estimation ». Dans 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference (2010 NSS/MIC). IEEE, 2010. http://dx.doi.org/10.1109/nssmic.2010.5874231.
Texte intégralSundarapandian, Manivannan, Ramakrishnan Kalpathi et R. Alfredo Siochi. « Respiratory motion prediction from CBCT image observations using UKF ». Dans 2015 14th IAPR International Conference on Machine Vision Applications (MVA). IEEE, 2015. http://dx.doi.org/10.1109/mva.2015.7153254.
Texte intégralEhrhardt, Jan, René Werner, Alexander Schmidt-Richberg et Heinz Handels. « A statistical shape and motion model for the prediction of respiratory lung motion ». Dans SPIE Medical Imaging, sous la direction de Benoit M. Dawant et David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844263.
Texte intégralDurichen, Robert, Tobias Wissel et Achim Schweikard. « Prediction of respiratory motion using wavelet based support vector regression ». Dans 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349742.
Texte intégralKlinder, Tobias, Cristian Lorenz et Jörn Ostermann. « Free-breathing intra- and intersubject respiratory motion capturing, modeling, and prediction ». Dans SPIE Medical Imaging, sous la direction de Josien P. W. Pluim et Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.811990.
Texte intégralYu, Shumei, Meng Dou, Rongchuan Sun et Lining Sun. « Respiratory Motion Compensation Based on Phase Prediction for Spine Surgery Robots* ». Dans 2018 IEEE International Conference on Information and Automation (ICIA). IEEE, 2018. http://dx.doi.org/10.1109/icinfa.2018.8812386.
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