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Статті в журналах з теми "Respiratory motion prediction"

Автор: Grafiati

Опубліковано: 14 вересня 2024

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1

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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2

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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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 (October 26, 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 (May 16, 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, 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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6

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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7

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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8

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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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 (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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10

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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11

OKUSAKO, Shouta, Fumitake FUJII, and Takehiro SHIINOKI. "Prediction of respiratory tumor motion based on FIR repetitive control." Proceedings of Mechanical Engineering Congress, Japan 2019 (2019): J24110P. http://dx.doi.org/10.1299/jsmemecj.2019.j24110p.

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12

Kalet, Alan, George Sandison, Huanmei Wu, and Ruth Schmitz. "A state-based probabilistic model for tumor respiratory motion prediction." Physics in Medicine and Biology 55, no. 24 (November 26, 2010): 7615–31. http://dx.doi.org/10.1088/0031-9155/55/24/015.

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13

Ruan, Dan. "Kernel density estimation-based real-time prediction for respiratory motion." Physics in Medicine and Biology 55, no. 5 (February 4, 2010): 1311–26. http://dx.doi.org/10.1088/0031-9155/55/5/004.

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14

Chang, Panchun, Jun Dang, Jianrong Dai, and Wenzheng Sun. "Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study." Journal of Medical Internet Research 23, no. 8 (August 27, 2021): e27235. http://dx.doi.org/10.2196/27235.

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Background The dynamic tracking of tumors with radiation beams in radiation therapy requires the prediction of real-time target locations prior to beam delivery, as treatment involving radiation beams and gating tracking results in time latency. Objective In this study, a deep learning model that was based on a temporal convolutional neural network was developed to predict internal target locations by using multiple external markers. Methods Respiratory signals from 69 treatment fractions of 21 patients with cancer who were treated with the CyberKnife Synchrony device (Accuray Incorporated) were used to train and test the model. The reported model’s performance was evaluated by comparing the model to a long short-term memory model in terms of the root mean square errors (RMSEs) of real and predicted respiratory signals. The effect of the number of external markers was also investigated. Results The average RMSEs of predicted (ahead time=400 ms) respiratory motion in the superior-inferior, anterior-posterior, and left-right directions and in 3D space were 0.49 mm, 0.28 mm, 0.25 mm, and 0.67 mm, respectively. Conclusions The experiment results demonstrated that the temporal convolutional neural network–based respiratory prediction model could predict respiratory signals with submillimeter accuracy.

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15

Hillman, D. R., and K. E. Finucane. "A model of the respiratory pump." Journal of Applied Physiology 63, no. 3 (September 1, 1987): 951–61. http://dx.doi.org/10.1152/jappl.1987.63.3.951.

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The interaction of forces that produce chest wall motion and lung volume change is complex and incompletely understood. To aid understanding we have developed a simple model that allows prediction of the effect on chest wall motion of changes in applied forces. The model is a lever system on which the forces generated actively by the respiratory muscles and passively by impedances of rib cage, lungs, abdomen, and diaphragm act at fixed sites. A change in forces results in translational and/or rotational motion of the lever; motion represents volume change. The distribution and magnitude of passive relative to active forces determine the locus and degree of rotation and therefore the effect of an applied force on motion of the chest wall, allowing the interaction of diaphragm, rib cage, and abdomen to be modeled. Analysis of moments allow equations to be derived that express the effect on chest wall motion of the active component in terms of the passive components. These equations may be used to test the model by comparing predicted with empirical behavior. The model is simple, appears valid for a variety of respiratory maneuvers, is useful in interpreting relative motion of rib cage and abdomen and may be useful in quantifying the effective forces acting on the rib cage.

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16

Bazaluk, Oleg, Alim Ennan, Serhii Cheberiachko, Oleh Deryugin, Yurii Cheberiachko, Pavlo Saik, Vasyl Lozynskyi, and Ivan Knysh. "Research on Regularities of Cyclic Air Motion through a Respirator Filter." Applied Sciences 11, no. 7 (April 1, 2021): 3157. http://dx.doi.org/10.3390/app11073157.

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In this paper, a solution to the problem of the change in the pressure drop in a respirator filter during cyclic air motion is suggested since the current theory of filtering is based on steady-flow processes. The theoretical dependence of the pressure drop in the respirator filter on air flow rate is determined, which is represented by the harmonic law, which characterizes the human respiration process during physical work. For the calculation, a filter model was used, which is represented by a system of parallel isolated cylinders with a length equal to the total length of the filter fibres surrounded by porous shells formed by a viscous air flow field, with a size determined by the equal velocities of the radial component of air flow and undisturbed flows. The flow-around process in the proposed model of air flow through the respirator filter is described by the Brinkman equation, which served to establish the total air flow resistance in the proposed system under conditions of velocity proportionality. It consists of two parts: the first characterizes the frictional resistance of the air flow against the surface of the cylinder, which imitates the filter fibre; the second—the inertial part—characterizes the frequency of pulsations of respiratory movements during physical performance. The divergence of the analytical results and experimental studies is no more than 20%, which allows the use of the established dependence to estimate the change in pressure drop in a respirator filter made of filter material “Elephlen” when the user carries out different physical activities. This allows the period of effective protective action of respirators with different cycles of respiration during physical activities to be specified, which is a very serious problem that is not currently regulated in health and safety regulations, and it also allows the prediction of the protective action of filters and respiratory protection in general.

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17

Jabbari, Keyvan, Nima Rostampour, Mahdad Esmaeili, Mohammad Mohammadi, and Shahabedin Nabavi. "Markerless Respiratory Tumor Motion Prediction Using an Adaptive Neuro-fuzzy Approach." Journal of Medical Signals & Sensors 8, no. 1 (2018): 25. http://dx.doi.org/10.4103/jmss.jmss_45_17.

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18

Sharp, Gregory C., Steve B. Jiang, Shinichi Shimizu, and Hiroki Shirato. "Prediction of respiratory tumour motion for real-time image-guided radiotherapy." Physics in Medicine and Biology 49, no. 3 (January 16, 2004): 425–40. http://dx.doi.org/10.1088/0031-9155/49/3/006.

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19

Ernst, Floris, Alexander Schlaefer, Sonja Dieterich, and Achim Schweikard. "A Fast Lane Approach to LMS prediction of respiratory motion signals." Biomedical Signal Processing and Control 3, no. 4 (October 2008): 291–99. http://dx.doi.org/10.1016/j.bspc.2008.06.001.

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20

Ruan, D., J. A. Fessler, and J. M. Balter. "Real-time prediction of respiratory motion based on local regression methods." Physics in Medicine and Biology 52, no. 23 (November 16, 2007): 7137–52. http://dx.doi.org/10.1088/0031-9155/52/23/024.

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21

Lee, Suk Jin, Yuichi Motai, Elisabeth Weiss, and Shumei S. Sun. "Customized prediction of respiratory motion with clustering from multiple patient interaction." ACM Transactions on Intelligent Systems and Technology 4, no. 4 (September 2013): 1–17. http://dx.doi.org/10.1145/2508037.2508050.

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22

Fan, Qi, Xiaoyang Yu, Yanqiao Zhao, and Shuang Yu. "A Respiratory Motion Prediction Method Based on Improved Relevance Vector Machine." Mobile Networks and Applications 25, no. 6 (July 26, 2020): 2270–79. http://dx.doi.org/10.1007/s11036-020-01610-7.

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23

Jöhl, Alexander, Stefanie Ehrbar, Matthias Guckenberger, Stephan Klöck, Mirko Meboldt, Melanie Zeilinger, Stephanie Tanadini‐Lang, and Marianne Schmid Daners. "Performance comparison of prediction filters for respiratory motion tracking in radiotherapy." Medical Physics 47, no. 2 (December 7, 2019): 643–50. http://dx.doi.org/10.1002/mp.13929.

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24

Nabavi, Shahabedin, Monireh Abdoos, MohsenEbrahimi Moghaddam, and Mohammad Mohammadi. "Respiratory motion prediction using deep convolutional long short-term memory network." Journal of Medical Signals & Sensors 10, no. 2 (2020): 69. http://dx.doi.org/10.4103/jmss.jmss_38_19.

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25

Chen, Yumiao, and Zhongliang Yang. "GEP-based predictive modeling of breathing resistances of wearing respirators on human body via sEMG and RSP sensors." Sensor Review 39, no. 4 (July 15, 2019): 439–48. http://dx.doi.org/10.1108/sr-08-2018-0210.

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PurposeBreathing resistance is the main factor that influences the wearing comfort of respirators. This paper aims to demonstrate the feasibility of using the gene expression programming (GEP) for the purpose of predicting subjective perceptions of breathing resistances of wearing respirators via surface electromyography (sEMG) and respiratory signals (RSP) sensors.Design/methodology/approachThe authors developed a physiological signal monitoring system with a specific garment. The inputs included seven physical measures extracted from (RSP) and (sEMG) signals. The output was the subjective index of breathing resistances of wearing respirators derived from the category partitioning-100 scale with proven levels of reliability and validity. The prediction model was developed and validated using data collected from 30 subjects and 24 test combinations (12 respirator conditions × 2 motion conditions). The subjects evaluated 24 conditions of breathing resistances in repeated measures fashion.FindingsThe results show that the GEP model can provide good prediction performance (R2= 0.71, RMSE = 0.11). This study demonstrates that subjective perceptions of breathing resistance of wearing respirators on the human body can be predicted using the GEP via sEMG and RSP in real-time, at little cost, non-invasively and automatically.Originality/valueThis is the first paper suggesting that subjective perceptions of subjective breathing resistances can be predicted from sEMG and RSP sensors using a GEP model, which will remain helpful to the scientific community to start further human-centered research work and product development using wearable biosensors and evolutionary algorithms.

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26

Kim, Moo-Sub, Joo-Young Jung, Do-Kun Yoon, Han-Back Shin, Tae Suk Suh, and Jae-Hong Jung. "The first step towards a respiratory motion prediction for natural-breathing by using a motion generator." Journal of the Korean Physical Society 70, no. 6 (March 2017): 621–28. http://dx.doi.org/10.3938/jkps.70.621.

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27

Ernst, Floris, Ralf Bruder, Alexander Schlaefer, and Achim Schweikard. "Forecasting pulsatory motion for non-invasive cardiac radiosurgery: an analysis of algorithms from respiratory motion prediction." International Journal of Computer Assisted Radiology and Surgery 6, no. 1 (April 30, 2010): 93–101. http://dx.doi.org/10.1007/s11548-010-0424-9.

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28

Wu, H., G. Sharp, B. Salzberg, D. Kaeli, H. Shirato, and S. Jiang. "SU-DD-A3-06: Model-Based Probabilistic Prediction of Tumor Respiratory Motion." Medical Physics 32, no. 6Part2 (May 26, 2005): 1894. http://dx.doi.org/10.1118/1.1997429.

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29

Rasheed, Asad, A. T. Adebisi, and Kalyana C. Veluvolu. "Respiratory Motion Prediction with Random Vector Functional Link (RVFL) Based Neural Networks." Journal of Physics: Conference Series 1626 (October 2020): 012022. http://dx.doi.org/10.1088/1742-6596/1626/1/012022.

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30

Kakar, Manish, Håkan Nyström, Lasse Rye Aarup, Trine Jakobi Nøttrup, and Dag Rune Olsen. "Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)." Physics in Medicine and Biology 50, no. 19 (September 21, 2005): 4721–28. http://dx.doi.org/10.1088/0031-9155/50/19/020.

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31

Tatinati, Sivanagaraja, Kianoush Nazarpour, Wei Tech Ang, and Kalyana C. Veluvolu. "Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications." Medical Engineering & Physics 38, no. 8 (August 2016): 749–57. http://dx.doi.org/10.1016/j.medengphy.2016.04.021.

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32

Preiswerk, Frank, Valeria De Luca, Patrik Arnold, Zarko Celicanin, Lorena Petrusca, Christine Tanner, Oliver Bieri, Rares Salomir, and Philippe C. Cattin. "Model-guided respiratory organ motion prediction of the liver from 2D ultrasound." Medical Image Analysis 18, no. 5 (July 2014): 740–51. http://dx.doi.org/10.1016/j.media.2014.03.006.

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33

Yu, Shumei, Jiateng Wang, Jinguo Liu, Rongchuan Sun, Shaolong Kuang, and Lining Sun. "Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network." IEEE Access 8 (2020): 49424–35. http://dx.doi.org/10.1109/access.2020.2980002.

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34

Putra, Devi, Olivier C. L. Haas, John A. Mills, and Keith J. Burnham. "A multiple model approach to respiratory motion prediction for real-time IGRT." Physics in Medicine and Biology 53, no. 6 (February 25, 2008): 1651–63. http://dx.doi.org/10.1088/0031-9155/53/6/010.

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35

Ruan, Dan, and Paul Keall. "Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning." Physics in Medicine and Biology 55, no. 11 (May 4, 2010): 3011–25. http://dx.doi.org/10.1088/0031-9155/55/11/002.

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36

Wimmert, L., M. Nielsen, T. Gauer, C. Hofmann, and R. Werner. "PO-1886 Respiratory motion prediction based on LSTM and linear regression models." Radiotherapy and Oncology 182 (May 2023): S1629—S1630. http://dx.doi.org/10.1016/s0167-8140(23)66801-x.

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37

Özbek, Yusuf, Zoltán Bárdosi, and Wolfgang Freysinger. "respiTrack: Patient-specific real-time respiratory tumor motion prediction using magnetic tracking." International Journal of Computer Assisted Radiology and Surgery 15, no. 6 (April 28, 2020): 953–62. http://dx.doi.org/10.1007/s11548-020-02174-3.

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38

Lombardo, Elia, Moritz Rabe, Yuqing Xiong, Lukas Nierer, Davide Cusumano, Lorenzo Placidi, Luca Boldrini, et al. "Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy." Physics in Medicine & Biology 67, no. 9 (April 19, 2022): 095006. http://dx.doi.org/10.1088/1361-6560/ac60b7.

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Abstract Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.

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39

Wu, Yuwen, Zhisen Wang, Yuyi Chu, Renyuan Peng, Haoran Peng, Hongbo Yang, Kai Guo, and Juzhong Zhang. "Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review." Biomimetics 9, no. 3 (March 12, 2024): 170. http://dx.doi.org/10.3390/biomimetics9030170.

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Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.

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40

Balasubramanian, A., R. Shamsuddin, B. Prabhakaran, and A. Sawant. "Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts." Physics in Medicine and Biology 62, no. 5 (February 9, 2017): 1791–809. http://dx.doi.org/10.1088/1361-6560/aa58c3.

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41

Li, G., A. Yuan, and J. Wei. "TU-F-17A-03: An Analytical Respiratory Perturbation Model for Lung Motion Prediction." Medical Physics 41, no. 6Part27 (May 29, 2014): 473. http://dx.doi.org/10.1118/1.4889330.

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42

Ernst, F., R. Bruder, A. Schlaefer, and A. Schweikard. "TH-C-BRC-06: Performance Measures and Pre-Processing for Respiratory Motion Prediction." Medical Physics 38, no. 6Part35 (June 2011): 3857. http://dx.doi.org/10.1118/1.3613523.

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43

Kotoku, J., S. Kumagai, A. Haga, S. Nakabayashi, N. Arai, and T. Kobayashi. "TU-F-CAMPUS-J-03: Prediction of Respiratory Motion Using State Space Models." Medical Physics 42, no. 6Part35 (June 2015): 3638. http://dx.doi.org/10.1118/1.4925793.

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44

Li, G., H. Xie, D. A. Miller, Y. Zhuge, E. E. Klein, D. Low, H. Ning, D. Citrin, K. Camphausen, and R. W. Miller. "Investigation of using Optical Surface Imaging for Volumetric Prediction of Respiratory Organ Motion." International Journal of Radiation Oncology*Biology*Physics 75, no. 3 (November 2009): S578. http://dx.doi.org/10.1016/j.ijrobp.2009.07.1321.

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45

Liu, Wenyang, Amit Sawant, and Dan Ruan. "Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach." Physics in Medicine and Biology 61, no. 13 (June 14, 2016): 4989–99. http://dx.doi.org/10.1088/0031-9155/61/13/4989.

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46

Pollock, S., D. Lee, P. Keall, and T. Kim. "WE-G-213CD-07: Enhancing Respiratory Motion Prediction Accuracy Using Audiovisual (AV) Biofeedback." Medical Physics 39, no. 6Part28 (June 2012): 3972. http://dx.doi.org/10.1118/1.4736208.

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47

Jeong, Sangwoon, Wonjoong Cheon, Sungkoo Cho, and Youngyih Han. "Clinical applicability of deep learning-based respiratory signal prediction models for four-dimensional radiation therapy." PLOS ONE 17, no. 10 (October 18, 2022): e0275719. http://dx.doi.org/10.1371/journal.pone.0275719.

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For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated. Among the 540 respiration signals, 60 signals are used as test data. Each of the remaining 480 signals was spilt into training and validation data in a 7:3 ratio. A total of 1000 ms of the signal sequence (Ts) is entered to the models, and the signal at 500 ms afterward (Pt) is predicted (standard training condition). The accuracy measures are: (1) root mean square error (RMSE) and Pearson correlation coefficient (CC), (2) accuracy dependency on Ts and Pt, (3) respiratory pattern dependency, and (4) error for 30% and 70% of the respiration gating for a 5 mm tumor motion for latencies of 300, 500, and 700 ms. Under standard conditions, the Transformer model exhibits the highest accuracy with an RMSE and CC of 0.1554 and 0.9768, respectively. An increase in Ts improves accuracy, whereas an increase in Pt decreases accuracy. An evaluation of the regularity of the respiratory signals reveals that the lowest predictive accuracy is achieved with irregular amplitude patterns. For 30% and 70% of the phases, the average error of the three models is <1.4 mm for a latency of 500 ms and >2.0 mm for a latency of 700 ms. The prediction accuracy of the Transformer is superior to LSTM and Bi-LSTM. Thus, the three models have clinically applicable accuracies for a latency <500 ms for 10 mm of regular tumor motion. The clinical acceptability of the deep learning models depends on the inherent latency and the strategy for reducing the irregularity of respiration.

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48

Lin, Hui, Chengyu Shi, Brian Wang, Maria F. Chan, Xiaoli Tang, and Wei Ji. "Towards real-time respiratory motion prediction based on long short-term memory neural networks." Physics in Medicine & Biology 64, no. 8 (April 10, 2019): 085010. http://dx.doi.org/10.1088/1361-6560/ab13fa.

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49

Jöhl, A., M. Schmid Daners, S. Ehrbar, M. Guckenberger, S. Klöck, and S. Lang. "PO-0925: Respiratory motion prediction filters for real time tumor tracking during radiation treatment." Radiotherapy and Oncology 115 (April 2015): S481—S482. http://dx.doi.org/10.1016/s0167-8140(15)40917-x.

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Mauro, Gianfranco, Maria De Carlos Diez, Julius Ott, Lorenzo Servadei, Manuel P. Cuellar, and Diego P. Morales-Santos. "Few-Shot User-Adaptable Radar-Based Breath Signal Sensing." Sensors 23, no. 2 (January 10, 2023): 804. http://dx.doi.org/10.3390/s23020804.

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Vital signs estimation provides valuable information about an individual’s overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.

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