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Relevant bibliographies by topics / Respiratory motion simulation

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Academic literature on the topic 'Respiratory motion simulation'

Author: Grafiati

Published: 5 June 2025

Last updated: 8 July 2025

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Journal articles on the topic "Respiratory motion simulation"

1

Ohsaki, Shuji, Ryosuke Mitani, Saki Fujiwara, Hideya Nakamura, and Satoru Watano. "Numerical Simulation of Particle Motions in Cascade Impactor and Human Respiratory System." MATEC Web of Conferences 333 (2021): 02013. http://dx.doi.org/10.1051/matecconf/202133302013.

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Dry powder inhalations (DPIs) have gathered attention as a treatment for respiratory diseases due to the large effective absorption area in a human lung. A cascade impactor is generally used to investigate the inhalation performance of DPIs. For the improvement of the efficiency of DPIs, understanding the particle motion and deposition behavior in the human lung and the cascade impactor is required. In the present study, computer simulations were conducted to calculate the particle motion and deposition behavior in the human lung and the cascade impactor. As simulation methods, a coupling model of a computational fluid dynamics and a discrete phase method (CFD−DPM) and a coupling model of a CFD and a discrete element method (CFD−DEM) were used. The CFD−DEM simulation could reproduce the experimental particle deposition behavior in the cascade impactor, although it was difficult by the CFD−DPM simulation. Furthermore, the calculation results using the CFD−DEM simulation quantitatively demonstrated the higher particle reachability into the simple lung model when smaller particles were used. It was found that the CFD−DEM simulation is a powerful tool to calculate the particle motion and deposition behavior in the cascade impactor and human lung.

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Ohsaki, Shuji, Ryosuke Mitani, Saki Fujiwara, Hideya Nakamura, and Satoru Watano. "Numerical Simulation of Particle Motions in Cascade Impactor and Human Respiratory System." MATEC Web of Conferences 333 (2021): 02013. http://dx.doi.org/10.1051/matecconf/202133302013.

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Dry powder inhalations (DPIs) have gathered attention as a treatment for respiratory diseases due to the large effective absorption area in a human lung. A cascade impactor is generally used to investigate the inhalation performance of DPIs. For the improvement of the efficiency of DPIs, understanding the particle motion and deposition behavior in the human lung and the cascade impactor is required. In the present study, computer simulations were conducted to calculate the particle motion and deposition behavior in the human lung and the cascade impactor. As simulation methods, a coupling model of a computational fluid dynamics and a discrete phase method (CFD−DPM) and a coupling model of a CFD and a discrete element method (CFD−DEM) were used. The CFD−DEM simulation could reproduce the experimental particle deposition behavior in the cascade impactor, although it was difficult by the CFD−DPM simulation. Furthermore, the calculation results using the CFD−DEM simulation quantitatively demonstrated the higher particle reachability into the simple lung model when smaller particles were used. It was found that the CFD−DEM simulation is a powerful tool to calculate the particle motion and deposition behavior in the cascade impactor and human lung.

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Mohn, Silje, and Ellen Wasbø. "Simulation of respiratory motion during IMRT dose delivery." Acta Oncologica 50, no. 6 (2011): 935–43. http://dx.doi.org/10.3109/0284186x.2011.580002.

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Werner, R., M. Blendowski, J. Ortmüller, H. Handels, and M. Wilms. "Simulation of Range Imaging-based Estimation of Respiratory Lung Motion." Methods of Information in Medicine 53, no. 04 (2014): 257–63. http://dx.doi.org/10.3414/me13-01-0137.

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SummaryObjectives: A major problem associated with the irradiation of thoracic and abdominal tumors is respiratory motion. In clinical practice, motion compensation approaches are frequently steered by low-dimensional breathing signals (e.g., spirometry) and patient-specific correspondence models, which are used to estimate the sought internal motion given a signal measurement. Recently, the use of multidimensional signals derived from range images of the moving skin surface has been proposed to better account for complex motion patterns. In this work, a simulation study is carried out to investigate the motion estimation accuracy of such multidimensional signals and the influence of noise, the signal dimensionality, and different sampling patterns (points, lines, regions).Methods: A diffeomorphic correspondence modeling framework is employed to relate multidimensional breathing signals derived from simulated range images to internal motion patterns represented by diffeomorphic non-linear transformations. Furthermore, an automatic approach for the selection of optimal signal combinations/patterns within this framework is presented.Results: This simulation study focuses on lung motion estimation and is based on 28 4D CT data sets. The results show that the use of multidimensional signals instead of one-dimensional signals significantly improves the motion estimation accuracy, which is, however, highly affected by noise. Only small differences exist between different multidimensional sampling patterns (lines and regions). Automatically determined optimal combinations of points and lines do not lead to accuracy improvements compared to results obtained by using all points or lines.Conclusions: Our results show the potential of multidimensional breathing signals derived from range images for the model-based estimation of respiratory motion in radiation therapy.

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Biederer, J., C. Plathow, M. Schoebinger, et al. "Reproducible Simulation of Respiratory Motion in Porcine Lung Explants." RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 178, no. 11 (2006): 1067–72. http://dx.doi.org/10.1055/s-2006-927149.

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Lee, Donghoon, Ellen Yorke, Masoud Zarepisheh, Saad Nadeem, and Yu-Chi Hu. "RMSim: Controlled respiratory motion simulation on static patient scans." Physics in Medicine & Biology 68, no. 4 (2023): 045009. https://doi.org/10.1088/1361-6560/acb484.

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<em>Objective.</em> This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground truth for validating deformable image registration (DIR) algorithms and driving more accurate deep learning based DIR. <em>Approach.</em> We present a novel 3D Seq2Seq deep learning respiratory motion simulator (RMSim) that learns from 4D-CT images and predicts future breathing phases given a static CT image. The predicted respiratory patterns, represented by time-varying displacement vector fields (DVFs) at different breathing phases, are modulated through auxiliary inputs of 1D breathing traces so that a larger amplitude in the trace results in more significant predicted deformation. Stacked 3D-ConvLSTMs are used to capture the spatial-temporal respiration patterns. Training loss includes a smoothness loss in the DVF and mean-squared error between the predicted and ground truth phase images. A spatial transformer deforms the static CT with the predicted DVF to generate the predicted phase image. 10-phase 4D-CTs of 140 internal patients were used to train and test RMSim. The trained RMSim was then used to augment a public DIR challenge dataset for training VoxelMorph to show the effectiveness of RMSim-generated deformation augmentation. <em>Main results.</em> We validated our RMSim output with both private and public benchmark datasets (healthy and cancer patients). The structure similarity index measure (SSIM) for predicted breathing phases and ground truth 4D CT images was 0.92 ± 0.04, demonstrating RMSim's potential to generate realistic respiratory motion. Moreover, the landmark registration error in a public DIR dataset was improved from 8.12 ± 5.78 mm to 6.58mm ± 6.38 mm using RMSim-augmented training data. <em>Significance.</em> The proposed approach can be used for validating DIR algorithms as well as for patient-specific augmentations to improve deep learning DIR algorithms. The code, pretrained models, and augmented DIR validation datasets will be released at https://github.com/nadeemlab/SeqX2Y.

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Bögel, Marco, Hannes G. Hofmann, Joachim Hornegger, Rebecca Fahrig, Stefan Britzen, and Andreas Maier. "Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: A Simulation and a Phantom Study." International Journal of Biomedical Imaging 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/520540.

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Long acquisition times lead to image artifacts in thoracic C-arm CT. Motion blur caused by respiratory motion leads to decreased image quality in many clinical applications. We introduce an image-based method to estimate and compensate respiratory motion in C-arm CT based on diaphragm motion. In order to estimate respiratory motion, we track the contour of the diaphragm in the projection image sequence. Using a motion corrected triangulation approach on the diaphragm vertex, we are able to estimate a motion signal. The estimated motion signal is used to compensate for respiratory motion in the target region, for example, heart or lungs. First, we evaluated our approach in a simulation study using XCAT. As ground truth data was available, a quantitative evaluation was performed. We observed an improvement of about 14% using the structural similarity index. In a real phantom study, using the artiCHEST phantom, we investigated the visibility of bronchial tubes in a porcine lung. Compared to an uncompensated scan, the visibility of bronchial structures is improved drastically. Preliminary results indicate that this kind of motion compensation can deliver a first step in reconstruction image quality improvement. Compared to ground truth data, image quality is still considerably reduced.

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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 (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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PUNZALAN, Florencio Rusty, Tetsuo SATO, Tomohisa OKADA, Shigehide KUHARA, Kaori TOGASHI, and Kotaro MINATO. "Respiratory Motion and Correction Simulation Platform for Coronary MR Angiography." IEICE Transactions on Information and Systems E96.D, no. 1 (2013): 111–19. http://dx.doi.org/10.1587/transinf.e96.d.111.

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Schwenke, Michael, Joachim Georgii, and Tobias Preusser. "Fast Numerical Simulation of Focused Ultrasound Treatments During Respiratory Motion With Discontinuous Motion Boundaries." IEEE Transactions on Biomedical Engineering 64, no. 7 (2017): 1455–68. http://dx.doi.org/10.1109/tbme.2016.2619741.

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Dissertations / Theses on the topic "Respiratory motion simulation"

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Schwenke, Michael [Verfasser], Tobias [Akademischer Betreuer] Preußer, Tobias [Gutachter] Preußer, Andreas [Gutachter] Birk, Haar Gail [Gutachter] ter, and Joachim [Gutachter] Georgii. "Focused Ultrasound Therapy for Abdominal Organs During Respiratory Motion: Numerical Modeling and Simulation and In-Silico First-Stage Evaluation of a Novel Treatment System / Michael Schwenke ; Gutachter: Tobias Preußer, Andreas Birk, Gail ter Haar, Joachim Georgii ; Betreuer: Tobias Preußer." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2018. http://d-nb.info/115678039X/34.

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Books on the topic "Respiratory motion simulation"

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Office, General Accounting. Air pollution: Air quality and respiratory problems in and near the Great Smoky Mountains : briefing report to Congressional requesters. The Office, 2001.

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Book chapters on the topic "Respiratory motion simulation"

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Bitarafan-Rajabi, Ahmad, Hossein Rajabi, Feridoon Rustgou, et al. "Respiratory Motion Influence on ECG-Gated SPET: A Simulation Study." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_577.

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Panettieri, Vanessa, Craig Lancaster, Chuan-Dong Wen, and Trevor Ackerly. "Monte Carlo Simulation of Respiratory Motion Induced Penumbral Broadening in Dose Distribution Using PENELOPE." In IFMBE Proceedings. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29305-4_507.

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Rijkhorst, Erik-Jan, Daniel Heanes, Freddy Odille, David Hawkes, and Dean Barratt. "Simulating Dynamic Ultrasound Using MR-derived Motion Models to Assess Respiratory Synchronisation for Image-Guided Liver Interventions." In Information Processing in Computer-Assisted Interventions. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13711-2_11.

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Villard Pierre-Frédéric, Jacob Mathieu, Gould Derek, and Bello Fernando. "Haptic Simulation of the Liver with Respiratory Motion." In Studies in Health Technology and Informatics. IOS Press, 2009. https://doi.org/10.3233/978-1-58603-964-6-401.

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During a standard procedure of liver biopsy, the motion due to respiration may be difficult to handle. The patient is often requested to hold his breath or to breathe shallowly. Ideally, this physiological behaviour should be taken into account in a virtual reality biopsy simulator. This paper presents a framework that accurately simulates respiratory motion, allowing for the fine tuning of relevant parameters in order to produce a patient-specific breathing pattern that can then be incorporated into a simulation with real-rime haptic interaction. This work has been done as part of the CRaIVE collaboration [1], which aims to build interventional radiology simulators.

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Courtecuisse Hadrien, Peterlik Igor, Trivisonne Raffaella, Duriez Christian, and Cotin Stéphane. "Constraint-Based Simulation for Non-Rigid Real-Time Registration." In Studies in Health Technology and Informatics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-375-9-76.

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In this paper we propose a method to address the problem of non-rigid registration in real-time. We use Lagrange multipliers and soft sliding constraints to combine data acquired from dynamic image sequence and a biomechanical model of the structure of interest. The biomechanical model plays a role of regularization to improve the robustness and the flexibility of the registration. We apply our method to a pre-operative 3D CT scan of a porcine liver that is registered to a sequence of 2D dynamic MRI slices during the respiratory motion. The finite element simulation provides a full 3D representation (including heterogeneities such as vessels, tumor,...) of the anatomical structure in real-time.

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Branco, Susana, Pedro Almeida, and Sebastien J. "Evaluation of the Respiratory Motion Effect in Small Animal PET Images with GATE Monte Carlo Simulations." In Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science. InTech, 2011. http://dx.doi.org/10.5772/15040.

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Conference papers on the topic "Respiratory motion simulation"

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Lan, W., H. P. Vo, C. Pommranz, et al. "A Simulation Framework to Establish Ground Truth for Motion Correction in Total-Body PET: Initial Evaluation for Complex Respiratory Motion." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10656116.

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Tong, Lei, Chaomin Chen, Kailian Kang, and Zihai Xu. "The Study on Predicting Respiratory Motion with Support Vector Regression." In 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/cmsa-18.2018.47.

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Jianfeng He, G. J. O'Keefe, T. Ackerly, S. J. Gong, and M. Geso. "Respiratory motion correction utilizing geometric sensitivity in 3D PET: A simulation study." In 2009 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2009). IEEE, 2009. http://dx.doi.org/10.1109/nssmic.2009.5402375.

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He, Jianfeng, Graeme J. O'Keefe, Gareth Jones, et al. "The Application of GATE and NCAT to Respiratory Motion Simulation in Allegro PET." In 2006 IEEE Nuclear Science Symposium Conference Record. IEEE, 2006. http://dx.doi.org/10.1109/nssmic.2006.354437.

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He, Jianfeng, Graeme J. O'Keefe, Gareth Jones, et al. "Evaluation of Geometrical Sensitivity for Respiratory Motion Gating by GATE and NCAT Simulation." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353254.

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Fu, Lei, Yu Hong, Yunhai Ji, and Jianfeng He. "A Simulation Study for the attenuation correction of Respiratory Motion with the PET/CT Gating data." In 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/icsma-16.2016.93.

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Pfanner, Florian, Thomas Allmendinger, Thomas Flohr, and Marc Kachelrieß. "Modelling and simulation of a respiratory motion monitor using a continuous wave Doppler radar in near field." In SPIE Medical Imaging, edited by Robert M. Nishikawa and Bruce R. Whiting. SPIE, 2013. http://dx.doi.org/10.1117/12.2007716.

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Eom, Jaesung, Chengyu Shi, George Xu, and Suvranu De. "Development of a Patient-Specific Nonlinear Finite Element Model for the Simulation of Lung Motion During Cancer Radiation Therapy." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206169.

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Respiratory motion causes either over-dose to the tumor or under-dose to the organ at risk in radiation therapy treatment for cancer. In order to characterize the motion, a nonlinear finite element model of the lungs has been developed based on 4D computed tomography (CT) data of a cancer patient with a tumor in the right lung. Pressure-volume (PV) curve data was applied to deform the model in real time. Realistic results are obtained when contact conditions are imposed between the pleura and the thoracic cavity.

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Kiasadegh, Morteza, Zahra Dehghani, Arash Naseri, Omid Abouali, and Goodarz Ahmadi. "Numerical Simulation of Airflow and Ellipsoidal Particle Deposition in Human Upper and Central Respiratory Tract." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-4966.

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Abstract Steady airflow pattern during a full breathing cycle in human upper and central respiratory tract was simulated by solving the Navier-Stokes and continuity equations. For ellipsoidal fiber trajectory analysis under cyclic breathing condition, several user defined functions (UDFs) were developed and coupled to the ANSYS-Fluent discrete phase model (DPM). The developed model accounted for solving the coupled translational and rotational equations of motion of ellipsoidal fibers. The airway passage model was extended from the vestibule to the fifth generation of the bronchial bifurcations obtained mostly from computed tomography (CT) scan. A constant flow rate of 15 L/min was used to simulate the normal breathing condition. The velocity and pressure fields for different regions of the respiratory track were evaluated and used for Lagrangian particle trajectory analysis. Total and regional depositions of each region for a range of ellipsoidal particle diameter and aspect ratios were evaluated and the results compared with the experimental data.

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Kiasadegh, Morteza, Omid Abouali, Homayoun Emdad, and Goodarz Ahmadi. "Numerical Simulation of Airflow and Ellipsoidal Particle Deposition in Human Upper Respiratory Tract." In ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83380.

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In this study, unsteady flow field and fibrous particle deposition in a realistic model of human upper airway system including vestibule to the end of trachea were investigated using the CFD technique. The airway passage model was constructed from the CT image of a 24 year old healthy woman. Unsteady airflow patterns during a full breathing cycle were simulated by solving the Navier-Stokes and continuity equations. For ellipsoidal fiber trajectory analysis under cyclic breathing condition, several user defined functions (UDFs) were coupled to the ANSYS-Fluent discrete phase model (DPM). The presented formulation accounted for solving the coupled translational and rotational equations of motion of ellipsoidal fibers. Total and regional depositions for a range of fiber sizes were evaluated. The transient particle deposition fraction was compared with those obtained from the equivalent steady flow condition. The presented results showed that the steady simulation can predict the total fibrous particle deposition during cyclic breathing with reasonable accuracy but cannot properly predict the regional deposition of particles.

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