Готові списки джерел за темами / Respiratory motion prediction

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Добірка наукової літератури з теми "Respiratory motion prediction"

Автор: Grafiati

Опубліковано: 14 вересня 2024

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Статті в журналах з теми "Respiratory motion prediction"

Dürichen, R., T. Wissel, F. Ernst, A. Schlaefer, and A. Schweikard. "Multivariate respiratory motion prediction." Physics in Medicine and Biology 59, no. 20 (September 25, 2014): 6043–60. http://dx.doi.org/10.1088/0031-9155/59/20/6043.

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Ernst, Floris, Alexander Schlaefer, and Achim Schweikard. "Predicting the outcome of respiratory motion prediction." Medical Physics 38, no. 10 (September 22, 2011): 5569–81. http://dx.doi.org/10.1118/1.3633907.

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Ren, Qing, Seiko Nishioka, Hiroki Shirato, and Ross I. Berbeco. "Adaptive prediction of respiratory motion for motion compensation radiotherapy." Physics in Medicine and Biology 52, no. 22 (October 26, 2007): 6651–61. http://dx.doi.org/10.1088/0031-9155/52/22/007.

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Ernst, F., R. Dürichen, A. Schlaefer, and A. Schweikard. "Evaluating and comparing algorithms for respiratory motion prediction." Physics in Medicine and Biology 58, no. 11 (May 16, 2013): 3911–29. http://dx.doi.org/10.1088/0031-9155/58/11/3911.

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Ichiji, Kei, Noriyasu Homma, Masao Sakai, Yuichiro Narita, Yoshihiro Takai, Xiaoyong Zhang, Makoto Abe, Norihiro Sugita, and 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.

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To achieve a better therapeutic effect and suppress side effects for lung cancer treatments, latency involved in current radiotherapy devices is aimed to be compensated for improving accuracy of continuous (not gating) irradiation to a respiratory moving tumor. A novel prediction method of lung tumor motion is developed for compensating the latency. An essential core of the method is to extract information valuable for the prediction, that is, the periodic nature inherent in respiratory motion. A seasonal autoregressive model useful to represent periodic motion has been extended to take into account the fluctuation of periodic nature in respiratory motion. The extended model estimates the fluctuation by using a correlation-based analysis for adaptation. The prediction performance of the proposed method was evaluated by using data sets of actual tumor motion and compared with those of the state-of-the-art methods. The proposed method demonstrated a high performance within submillimeter accuracy. That is, the average error of 1.0 s ahead predictions was0.931±0.055 mm. The accuracy achieved by the proposed method was the best among those by the others. The results suggest that the method can compensate the latency with sufficient accuracy for clinical use and contribute to improve the irradiation accuracy to the moving tumor.

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Jöhl, Alexander, Yannick Berdou, Matthias Guckenberger, Stephan Klöck, Mirko Meboldt, Melanie Zeilinger, Stephanie Tanadini-Lang, and Marianne Schmid Daners. "Performance behavior of prediction filters for respiratory motion compensation in radiotherapy." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 429–32. http://dx.doi.org/10.1515/cdbme-2017-0090.

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AbstractIntroduction: In radiotherapy, tumors may move due to the patient’s respiration, which decreases treatment accuracy. Some motion mitigation methods require measuring the tumor position during treatment. Current available sensors often suffer from time delays, which degrade the motion mitigation performance. However, the tumor motion is often periodic and continuous, which allows predicting the motion ahead. Method and Materials: A couch tracking system was simulated in MATLAB and five prediction filters selected from literature were implemented and tested on 51 respiration signals (median length: 103 s). The five filters were the linear filter (LF), the local regression (LOESS), the neural network (NN), the support vector regression (SVR), and the wavelet least mean squares (wLMS). The time delay to compensate was 320 ms. The normalized root mean square error (nRMSE) was calculated for all prediction filters and respiration signals. The correlation coefficients between the nRMSE of the prediction filters were computed. Results: The prediction filters were grouped into a low and a high nRMSE group. The low nRMSE group consisted of the LF, the NN, and the wLMS with a median nRMSE of 0.14, 0.15, and 0.14, respectively. The high nRMSE group consisted of the LOESS and the SVR with both a median nRMSE of 0.34. The correlations between the low nRMSE filters were above 0.87 and between the high nRMSE filters it was 0.64. Conclusion: The low nRMSE prediction filters not only have similar median nRMSEs but also similar nRMSEs for the same respiration signals as the high correlation shows. Therefore, good prediction filters perform similarly for identical respiration patterns, which might indicate a minimally achievable nRMSE for a given respiration pattern.

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Rasheed, Asad, and Kalyana C. Veluvolu. "Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link." Mathematics 12, no. 4 (February 16, 2024): 588. http://dx.doi.org/10.3390/math12040588.

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The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.

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Fujii, 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 (April 20, 2017): 434–46. http://dx.doi.org/10.20965/jrm.2017.p0434.

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[abstFig src='/00290002/15.jpg' width='300' text='Proposed method for tracking and following respiratory organ motion' ] High intensity focused ultrasound (HIFU) is potentially useful for treating stones and/or tumors. With respect to HIFU therapy, it is difficult to focus HIFU on the focal lesion due to respiratory organ motion, and this increases the risk of damaging the surrounding healthy tissues around the target focal lesion. Thus, this study proposes a method to cope with the fore-mentioned problem involving tracking and following the respiratory organ motion via a visual feedback and a prediction model for respiratory organ motion to realize highly accurate servoing performance for focal lesions. The prediction model is continuously updated based on the latest organ motion data. The results indicate that respiratory kidney motion of two healthy subjects is successfully tracked and followed with an accuracy of 0.88 mm by the proposed method and the constructed system.

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Yang, Dongrong, Yuhua Huang, Bing Li, Jing Cai, and Ge Ren. "Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study." Cancers 15, no. 24 (December 8, 2023): 5768. http://dx.doi.org/10.3390/cancers15245768.

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In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs.

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Zhang, Xiangyu, Xinyu Song, Guangjun Li, Lian Duan, Guangyu Wang, Guyu Dai, Ying Song, Jing Li, and Sen Bai. "Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor." Technology in Cancer Research & Treatment 21 (January 2022): 153303382211432. http://dx.doi.org/10.1177/15330338221143224.

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Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the light gradient boosting machine (LightGBM)-based cross-validation (the recursive elimination method) was used for feature selection. The LightGBM framework forecasting models with classification thresholds 0.7, 0.8, and 0.9 are established using stratified 5-fold cross-validation. Model performance was assessed using receiver operating characteristics, sensitivity, and specificity. Results: There were 16, 18, and 13 features selected for models 0.7, 0.8, and 0.9, respectively. Texture features are of great importance in external/internal correlation prediction compared to other features in all models. The sensitivities of the predictions in models 0.7, 0.8, and 0.9 were 0.800 ± 0.126, 0.829 ± 0.140, and 0.864 ± 0.086, respectively. The specificities were 0.771 ± 0.114, 0.936 ± 0.0581, and 0.839 ± 0.101, whereas the area under the curve (AUC) was 0.837, 0.946, and 0.877, respectively. Conclusions: Our findings indicate that radiomics is an effective tool for respiratory motion correlation prediction, which can extract tumor motion characteristics. We proposed a machine learning framework for correlation prediction in the motion management strategy for lung tumor patients.

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Більше джерел

Дисертації з теми "Respiratory motion prediction"

Lee, Suk Jin. "PREDICTION OF RESPIRATORY MOTION." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/336.

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Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier.

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Alnowami, 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.

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The latter two decades of the last century saw significant improvements in External Beam Radiotherapy (EBRT), moved primarily by the advances in imaging modalities and computer-based treatment planning. These advances led to introducing the addition of a fourth dimension, time, to the three-dimensional EBRT arena. This new era in EBRT brings with it challenges and opportunities, in particular to compensate for the effect of respiratory-induced target motion and enhancing treatment delivery. Thus, characterising and modelling respiratory motion is of major importance in this research area. This thesis aims to enhance the understanding and control the effect of respiratory motion. As part of this work, the first principal component analysis (PCA) of respiratory motion is presented, as a basis for compactly and visually representing respiratory style and variation. These studies can be divided into two main aspects: firstly, understanding and characterising respiratory motion as the basis of any further steps towards compensating respiratory motion and secondly, utilising this knowledge in predicting and correlating internal and external respiratory motion in the abdominal thoracic region. This work has been developed starting with a piecewise sinusoidal model in an Eigenspace for modelling, Adaptive kernel density estimation (AKDE) for prediction and finally Canonical Correlation Analysis (CCA) for external-internal target correlation. A comparative study between these proposed approaches and state-of-the-art prior works showed promising results in terms of accuracy and computational efficiency: 20% error reduction compared to support vector regression (SVR) and kernel density estimation (KDE) and a significant reduction in computation speed during training stage. This journey into modelling and predicting respiratory behaviour has naturally raised questions of how best to track external motion. The need to track the surface with more than one marker, established within the aforementioned PCA analysis, motivates the desire for markerless tracking. Therefore, two different markerless systems have been studied, as potential solutions for this area, combined with a mesh model of the anterior surface. This suggests that the Microsoft Kinect camera is a promising low-cost technology for makerless respiratory tracking with less than 3.1 ± 0.6 mm accuracy.

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Park, 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.

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Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT IGRT requires precise respiratory signal extraction to avoid burry edges, inaccurate tumor shape, and motion-induced artifacts on the reconstructed CBCT image. 4D MRIs used in MRI-guided radiation therapy typically have low resolution as a tradeoff with field of view, image acquisition time, and image quality. To predict the tumor motion and guarantee the superior quality of the imaging techniques, the dissertation is divided into three parts. The first part describes a new prediction method for respiration-related tumor movements, called Intra- and Inter-fractional variation prediction using Fuzzy Deep Learning (IIFDL). IIFDL clusters the respiratory movements based on breathing similarities, and estimates patients' breathing motion using the proposed predictor, called fuzzy deep learning. The second part of the dissertation includes a novel marker-less binning method for 4D CBCT projections, called Image Registration-based Projection Binning (IRPB), which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing periodicity of tissue feature point trajectories. The third part the dissertation explains a novel Super-Resolution (SR) method for 4D MRI, called Recurrent Deep Learning-based SR (RDLS), comprised of feature extraction, recurrent nonlinear mapping, and reconstruction. RDLS estimates high-resolution MRIs from low-resolution MRIs according to a specified magnification power.

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Li, 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.

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L’objectif ultime de thèse est de développer un système de synchronisation de bout en bout pour la compensation en temps réel des mouvements lors du traitement du cancer du poumon et du foie sur l’Elekta Unity. Ce système surveillera et localisera automatiquement en temps réel la position spatiale tridimensionnelle de la tumeur, et prédira sa trajectoire dans 0.5 secondes. Un signal de synchronisation sera généré pour contrôler l’activation et la désactivation du faisceau pendant la radiothérapie, réduisant ainsi l’inexactitude dans la délivrance de la dose due au mouvement respiratoire. Pour atteindre cet objectif, les étapes suivantes ont été réalisées : 1. Validation de l’efficacité de KCF dans le suivi des tumeurs en 2D sur des images en IRM cine, plus efficace et précise par rapport aux méthodes traditionnelles (TM). La précision est améliorée en calculant le centroïde des pixels, et la sélection des plans (coronales vs sagittales) pour localiser les tumeurs dans la direction SI. 2. Proposition d’un modèle C-NLSTM spécifique au patient qui combine la préformation du modèle C-NLSTM et l’optimisation de la cible pour obtenir une meilleure prédiction du mouvement de tumeurs. Le transfer learning, en utilisant efficacement le modèle préformé sur un ensemble de données limité, est une solution pertinente face au manque de données de l’Elekta Unity. Le modèle montre une performance satisfaisante dans la prédiction en temps réel pour la compensation du movement spécifique au patient. 3. Validation de la régression linéaire dans la prédiction du mouvement des organs ou des tumeurs en utilisant des images MR ciné 2D et proposition d’un schéma de prédiction en ligne pour les signaux de gating. Les signaux de gating sont déclenchés àl’aide de modèles prédictifs, prouvant son efficacité dans la MRgRT en comparant avec des modèles RNN. 4. Intégration des travaux susmentionnés, proposition d’une solution complète de compensation des mouvements respiratoires basée sur la IRM cine orthogonale. En optimisant un modèle de pavé et en explorant différents scénarios, des signaux de gating sont générés pour répondre aux besoins de traitement des différents patients. La validation par étude dosimétrique confirme que l’efficacité de la solution proposée dans la protection des organes environnants à risque. En résumé, le système proposé est robuste et fiable, réalisant une adaptation en temps réel au mouvement des tumeurs en MRgRT. Il fournit un solide soutien pour la compensation du mouvement respiratoire dans le traitement des cancers thoraciques et abdominaux, servant d’outil essentiel pour la radiothérapie de précision
The 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

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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.

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External beam radiation therapy (EBRT) is the most common form of radiation therapy (RT) that uses controlled energy sources to eradicate a predefined tumour volume, known as the planning target volume (PTV), whilst at the same time attempting to minimise the dose delivered to the surrounding healthy tissues. Tumours in the thoracic and abdomen regions are susceptible to motion caused mainly by the patient respiration and movement that may occur during the treatment preparation and delivery. Usually, an adaptive approach termed adaptive radiation therapy (ART), which involves feedback from imaging devices to detect organ/surrogate motion, is considered. The feasibility of such techniques is subject to two main problems. First, the exact position of the tumour has to be estimated/detected in real-time and second, the delay that can arise from the tumour position acquisition and the motion tracking compensation. The research work described in this thesis is part of the European project entitled ‘Methods and advanced equipment for simulation and treatment in radiation oncology’ (MAESTRO), see Appendix A. The thesis presents both theoretical and experimental work to model and predict the respiratory surrogate motion. Based on a widely investigated clinical internal and external respiratory surrogate motion data, two new approaches to model respiratory surrogate motion were developed. The first considers the lung as a bilinear model that replicates the motion in response to a virtual input signal that can be seen as a signal generated by the nervous system. This model and a statistical model of the respiratory period and duty cycle were used to generate a set of realistic respiratory data of varying difficulties. The aim of the latter was to overcome the lack of test data for a researcher to evaluate their algorithms. The second approach was based on an online polynomial function that was found to adequately replicate the breathing cycles of regular and irregular data, using the same number of parameters as a benchmark sinusoidal model.

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Akimoto, Mami. "Predictive uncertainty in infrared marker-based dynamic tumor tracking with Vero4DRT." Kyoto University, 2015. http://hdl.handle.net/2433/199176.

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CHEN, KE-WEI, and 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.

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碩士
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.

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Книги з теми "Respiratory motion prediction"

Lee, Suk Jin, and 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.

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Lee, Suk Jin, and Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer Berlin / Heidelberg, 2013.

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Lee, Suk Jin, and Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer, 2016.

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Lee, Suk Jin, and Yuichi Motai. Prediction and Classification of Respiratory Motion. Springer London, Limited, 2013.

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Частини книг з теми "Respiratory motion prediction"

Lee, Suk Jin, and 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.

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Lee, Suk Jin, and 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.

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Lee, Suk Jin, and 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.

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Vedam, 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.

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Lee, Suk Jin, and 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.

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Lee, Suk Jin, and 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.

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Lee, Suk Jin, and 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.

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Lee, Suk Jin, and 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.

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Klinder, Tobias, Cristian Lorenz, and 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.

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Fuerst, B., T. Mansi, Jianwen Zhang, P. Khurd, J. Declerck, T. Boettger, Nassir Navab, J. Bayouth, Dorin Comaniciu, and 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.

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Тези доповідей конференцій з теми "Respiratory motion prediction"

Shoujun Zhou, Qubo Zheng, Hongliang Li, Yueqian Zhou, and 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.

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Yang, Jun, Zhengbo Zhang, Shoujun Zhou, and 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.

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Tanner, Christine, Koen Eppenhof, Jaap Gelderblom, and 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.

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Bao, Xuezhi, Deqiang Xiao, Baochun He, Wenchao Gao, Junliang Wang, and 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.

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Alnowam, M. R., E. Lewis, K. Wells, and 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.

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Sundarapandian, Manivannan, Ramakrishnan Kalpathi, and 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.

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Ehrhardt, Jan, René Werner, Alexander Schmidt-Richberg, and Heinz Handels. "A statistical shape and motion model for the prediction of respiratory lung motion." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844263.

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Durichen, Robert, Tobias Wissel, and 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.

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Klinder, Tobias, Cristian Lorenz, and Jörn Ostermann. "Free-breathing intra- and intersubject respiratory motion capturing, modeling, and prediction." In SPIE Medical Imaging, edited by Josien P. W. Pluim and Benoit M. Dawant. SPIE, 2009. http://dx.doi.org/10.1117/12.811990.

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Yu, Shumei, Meng Dou, Rongchuan Sun, and 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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