Auswahl der wissenschaftlichen Literatur zum Thema „Respiratory motion prediction“
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Zeitschriftenartikel zum Thema "Respiratory motion prediction"
Dürichen, R., T. Wissel, F. Ernst, A. Schlaefer und A. Schweikard. „Multivariate respiratory motion prediction“. Physics in Medicine and Biology 59, Nr. 20 (25.09.2014): 6043–60. http://dx.doi.org/10.1088/0031-9155/59/20/6043.
Der volle Inhalt der QuelleErnst, Floris, Alexander Schlaefer und Achim Schweikard. „Predicting the outcome of respiratory motion prediction“. Medical Physics 38, Nr. 10 (22.09.2011): 5569–81. http://dx.doi.org/10.1118/1.3633907.
Der volle Inhalt der QuelleRen, Qing, Seiko Nishioka, Hiroki Shirato und Ross I. Berbeco. „Adaptive prediction of respiratory motion for motion compensation radiotherapy“. Physics in Medicine and Biology 52, Nr. 22 (26.10.2007): 6651–61. http://dx.doi.org/10.1088/0031-9155/52/22/007.
Der volle Inhalt der QuelleErnst, F., R. Dürichen, A. Schlaefer und A. Schweikard. „Evaluating and comparing algorithms for respiratory motion prediction“. Physics in Medicine and Biology 58, Nr. 11 (16.05.2013): 3911–29. http://dx.doi.org/10.1088/0031-9155/58/11/3911.
Der volle Inhalt der QuelleIchiji, Kei, Noriyasu Homma, Masao Sakai, Yuichiro Narita, Yoshihiro Takai, Xiaoyong Zhang, Makoto Abe, Norihiro Sugita und 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.
Der volle Inhalt der QuelleJöhl, Alexander, Yannick Berdou, Matthias Guckenberger, Stephan Klöck, Mirko Meboldt, Melanie Zeilinger, Stephanie Tanadini-Lang und Marianne Schmid Daners. „Performance behavior of prediction filters for respiratory motion compensation in radiotherapy“. Current Directions in Biomedical Engineering 3, Nr. 2 (07.09.2017): 429–32. http://dx.doi.org/10.1515/cdbme-2017-0090.
Der volle Inhalt der QuelleRasheed, Asad, und Kalyana C. Veluvolu. „Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link“. Mathematics 12, Nr. 4 (16.02.2024): 588. http://dx.doi.org/10.3390/math12040588.
Der volle Inhalt der QuelleFujii, 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, Nr. 2 (20.04.2017): 434–46. http://dx.doi.org/10.20965/jrm.2017.p0434.
Der volle Inhalt der QuelleYang, Dongrong, Yuhua Huang, Bing Li, Jing Cai und Ge Ren. „Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study“. Cancers 15, Nr. 24 (08.12.2023): 5768. http://dx.doi.org/10.3390/cancers15245768.
Der volle Inhalt der QuelleZhang, Xiangyu, Xinyu Song, Guangjun Li, Lian Duan, Guangyu Wang, Guyu Dai, Ying Song, Jing Li und Sen Bai. „Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor“. Technology in Cancer Research & Treatment 21 (Januar 2022): 153303382211432. http://dx.doi.org/10.1177/15330338221143224.
Der volle Inhalt der QuelleDissertationen zum Thema "Respiratory motion prediction"
Lee, Suk Jin. „PREDICTION OF RESPIRATORY MOTION“. VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/336.
Der volle Inhalt der QuelleAlnowami, 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.
Der volle Inhalt der QuellePark, 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.
Der volle Inhalt der QuelleLi, 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.
Der volle Inhalt der QuelleThe 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.
Der volle Inhalt der QuelleAkimoto, Mami. „Predictive uncertainty in infrared marker-based dynamic tumor tracking with Vero4DRT“. Kyoto University, 2015. http://hdl.handle.net/2433/199176.
Der volle Inhalt der QuelleCHEN, KE-WEI, und 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.
Der volle Inhalt der Quelle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.
Bücher zum Thema "Respiratory motion prediction"
Lee, Suk Jin, und 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.
Der volle Inhalt der QuelleLee, Suk Jin, und Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer Berlin / Heidelberg, 2013.
Den vollen Inhalt der Quelle findenLee, Suk Jin, und Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer, 2016.
Den vollen Inhalt der Quelle findenLee, Suk Jin, und Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer London, Limited, 2013.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Respiratory motion prediction"
Lee, Suk Jin, und Yuichi Motai. „Review: Prediction of Respiratory Motion“. In 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.
Der volle Inhalt der QuelleLee, Suk Jin, und Yuichi Motai. „Customized Prediction of Respiratory Motion“. In 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.
Der volle Inhalt der QuelleLee, Suk Jin, und Yuichi Motai. „Respiratory Motion Estimation with Hybrid Implementation“. In 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.
Der volle Inhalt der QuelleVedam, Sastry. „Respiratory Motion Prediction in Radiation Therapy“. In 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.
Der volle Inhalt der QuelleLee, Suk Jin, und Yuichi Motai. „Introduction“. In 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.
Der volle Inhalt der QuelleLee, Suk Jin, und Yuichi Motai. „Phantom: Prediction of Human Motion with Distributed Body Sensors“. In 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.
Der volle Inhalt der QuelleLee, Suk Jin, und Yuichi Motai. „Irregular Breathing Classification from Multiple Patient Datasets“. In 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.
Der volle Inhalt der QuelleLee, Suk Jin, und Yuichi Motai. „Conclusions and Contributions“. In 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.
Der volle Inhalt der QuelleKlinder, Tobias, Cristian Lorenz und Jörn Ostermann. „Prediction Framework for Statistical Respiratory Motion Modeling“. In 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.
Der volle Inhalt der QuelleFuerst, B., T. Mansi, Jianwen Zhang, P. Khurd, J. Declerck, T. Boettger, Nassir Navab, J. Bayouth, Dorin Comaniciu und A. Kamen. „A Personalized Biomechanical Model for Respiratory Motion Prediction“. In 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Respiratory motion prediction"
Shoujun Zhou, Qubo Zheng, Hongliang Li, Yueqian Zhou und Yuan Hong. „Probabilistic respiratory motion prediction“. In 2010 International Conference of Medical Image Analysis and Clinical Application (MIACA). IEEE, 2010. http://dx.doi.org/10.1109/miaca.2010.5528497.
Der volle Inhalt der QuelleYang, Jun, Zhengbo Zhang, Shoujun Zhou und Hongnan Yin. „Respiratory Motion Prediction Based on Maximum Posterior Probability“. In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5163345.
Der volle Inhalt der QuelleTanner, Christine, Koen Eppenhof, Jaap Gelderblom und Gabor Szekely. „Decision fusion for temporal prediction of respiratory liver motion“. In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6867966.
Der volle Inhalt der QuelleBao, Xuezhi, Deqiang Xiao, Baochun He, Wenchao Gao, Junliang Wang und Fucang Jia. „Prediction of Liver Respiratory Motion Based on Machine Learning“. In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. http://dx.doi.org/10.1109/robio49542.2019.8961688.
Der volle Inhalt der QuelleAlnowam, M. R., E. Lewis, K. Wells und M. Guy. „Respiratory motion modelling and prediction using probability density estimation“. In 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference (2010 NSS/MIC). IEEE, 2010. http://dx.doi.org/10.1109/nssmic.2010.5874231.
Der volle Inhalt der QuelleSundarapandian, Manivannan, Ramakrishnan Kalpathi und R. Alfredo Siochi. „Respiratory motion prediction from CBCT image observations using UKF“. In 2015 14th IAPR International Conference on Machine Vision Applications (MVA). IEEE, 2015. http://dx.doi.org/10.1109/mva.2015.7153254.
Der volle Inhalt der QuelleEhrhardt, Jan, René Werner, Alexander Schmidt-Richberg und Heinz Handels. „A statistical shape and motion model for the prediction of respiratory lung motion“. In SPIE Medical Imaging, herausgegeben von Benoit M. Dawant und David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844263.
Der volle Inhalt der QuelleDurichen, Robert, Tobias Wissel und Achim Schweikard. „Prediction of respiratory motion using wavelet based support vector regression“. In 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349742.
Der volle Inhalt der QuelleKlinder, Tobias, Cristian Lorenz und Jörn Ostermann. „Free-breathing intra- and intersubject respiratory motion capturing, modeling, and prediction“. In SPIE Medical Imaging, herausgegeben von Josien P. W. Pluim und Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.811990.
Der volle Inhalt der QuelleYu, Shumei, Meng Dou, Rongchuan Sun und Lining Sun. „Respiratory Motion Compensation Based on Phase Prediction for Spine Surgery Robots*“. In 2018 IEEE International Conference on Information and Automation (ICIA). IEEE, 2018. http://dx.doi.org/10.1109/icinfa.2018.8812386.
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