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Relevant bibliographies by topics / Respiratory motion prediction

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Journal articles

Academic literature on the topic 'Respiratory motion prediction'

Author: Grafiati

Published: 14 September 2024

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Journal articles on the topic "Respiratory motion prediction"

1

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

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2

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

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3

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 (2007): 6651–61. http://dx.doi.org/10.1088/0031-9155/52/22/007.

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4

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 (2013): 3911–29. http://dx.doi.org/10.1088/0031-9155/58/11/3911.

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5

Ichiji, Kei, Noriyasu Homma, Masao Sakai, et al. "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 a

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6

Jöhl, Alexander, Yannick Berdou, Matthias Guckenberger, et al. "Performance behavior of prediction filters for respiratory motion compensation in radiotherapy." Current Directions in Biomedical Engineering 3, no. 2 (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 (med

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7

Rasheed, Asad, and Kalyana C. Veluvolu. "Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link." Mathematics 12, no. 4 (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. I

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8

Fujii, Tatsuya, Norihiro Koizumi, Atsushi Kayasuga, 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 (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

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9

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 (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

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10

Zhang, Xiangyu, Xinyu Song, Guangjun Li, et al. "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 e

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