Towards Non-Invasive Lung Tumor Tracking Based on Patient Specific Model of Respiratory System

Ladjal, Hamid; Beuve, Michaël; Giraud, P.; Shariat, Behzad

doi:10.1109/tbme.2021.3053321

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…In radiotherapy, by measuring respiratory airflow and chest movement to calculate complex internal respiration and tumor movement, H. L. et al [ 26 ] proposed a method for tracking lung tumors based on a patient-specific biomechanical model that takes into account the physiology of respiratory movement to simulate true, non-repeatable movement. The behavior of the lungs is directly driven by the simulated action of the breathing muscles, namely, the diaphragm and intercostal muscles (thorax).…”

Section: Resultsmentioning

confidence: 99%

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Wu,

Wang,

Chu

et al. 2024

Biomimetics

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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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Section: Resultsmentioning

confidence: 99%

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Wu,

Wang,

Chu

et al. 2024

Biomimetics

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“…This reduced-order in-silico model of the lung facilitates a novel and much-needed class of inverse modeling approaches for the respiratory system. Current pulmonary biomechanical models of the lung can be categorized into two classes: 1) models primarily based on in-vivo kinematics data obtained from computed tomography (CT) or magnetic resonance (MR) images ( Al-Mayah et al, 2010 ; Eom et al, 2010 ; Li et al, 2013 ; Ilegbusi et al, 2016 ; Ladjal et al, 2021 ), and 2) classic models idealizing the lung as single/multi resistive compartments calibrated with pressure-volume data ( Bates, 2009 ). While these methods have been quite insightful, their shortcomings have motivated the novel approach put forward in this study.…”

Section: Discussionmentioning

confidence: 99%

Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework

Maghsoudi-Ganjeh

Mariano

Sattari

et al. 2021

Front. Bioeng. Biotechnol.

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Pulmonary diseases, driven by pollution, industrial farming, vaping, and the infamous COVID-19 pandemic, lead morbidity and mortality rates worldwide. Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hindered by the lung’s complex and hierarchical structure, and the lack of mechanical experiments linking the load-bearing organ-level response to local behaviors. In this study we address these impedances by introducing a novel reduced-order surface model of the lung, combining the response of the intricate bronchial network, parenchymal tissue, and visceral pleura. The inverse finite element analysis (IFEA) framework is developed using 3-D digital image correlation (DIC) from experimentally measured non-contact strains and displacements from an ex-vivo porcine lung specimen for the first time. A custom-designed inflation device is employed to uniquely correlate the multiscale classical pressure-volume bulk breathing measures to local-level deformation topologies and principal expansion directions. Optimal material parameters are found by minimizing the error between experimental and simulation-based lung surface displacement values, using both classes of gradient-based and gradient-free optimization algorithms and by developing an adjoint formulation for efficiency. The heterogeneous and anisotropic characteristics of pulmonary breathing are represented using various hyperelastic continuum formulations to divulge compound material parameters and evaluate the best performing model. While accounting for tissue anisotropy with fibers assumed along medial-lateral direction did not benefit model calibration, allowing for regional material heterogeneity enabled accurate reconstruction of lung deformations when compared to the homogeneous model. The proof-of-concept framework established here can be readily applied to investigate the impact of assorted organ-level ventilation strategies on local pulmonary force and strain distributions, and to further explore how diseased states may alter the load-bearing material behavior of the lung. In the age of a respiratory pandemic, advancing our understanding of lung biomechanics is more pressing than ever before.

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“…Therefore, in this paper, we used the Ogden_N6 model, which is most satisfactory for stability and the least squares method. Additionally, the elastic modulus and Poisson's ratio were used as elastic materials for the properties of the bronchus on the basis of previous studies (Table 2) [19].…”

Section: Methodsmentioning

confidence: 99%

“…This study aimed to develop a biomechanical model of the human lung by targeting only lung bronchi (or tumors) and predicting changes in respiratory movements to diagnose lung diseases at an early stage. A model that considers all contributing factors, including the patient's speci c material properties, shape, load, and boundary conditions, is needed for accurate tracking of lung deformation [19]. Considering these factors, results of lung deformation were presented as quantitative values for each location through the comparison of computed tomography (CT) and FEM results.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computer Modeling and Simulation Technique for Bronchi Motion Tracking in Human Lungs under Respiration

kim

Ahn

Song

et al. 2023

Preprint

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In this work, we proposed a novel computer modeling and simulation (CM&S) technique for motion tracking of lung bronchi (or tumors) under respiration using 9 cases of computed tomography (CT)-based patient-specific finite element (FE) models and the Ogden’s hyperelastic constitutive model. In the fabrication of patient-specific FE models for respiratory system, various organs such as mediastinum, diaphragm, and thorax that could affect the lung motions during breathing were considered. In order to describe the nonlinear material/mechanical behavior of human lung tissue (lung parenchyma), the comparative simulation for biaxial tension-compression of lung tissue were carried out using several hyperelastic models, and then, the Ogden’s model was adopted to as the optimal model. Based on the aforementioned FE models and Ogden’s material model, the 9 cases of respiration simulation were carried out from exhalation to inhalation, and the motion of lung bronchi (or tumors) was tracked. In addition, the changes of lung volume, lung cross-sectional area on the axial plane during breathing were calculated. Finally, the simulation results were quantitatively compared to the inhalation/exhalation CT images of 9 objects to validate the proposed technique. The relative errors of the simulation to the clinical data are able to predict the lung lesion motion with an average landmark error: anterior/posterior, 2.67%; right/left, 2.10%; and superior/inferior direction 1.10% error and confirmed to be well matched within the range of 0.20–5.00% of the total average relative error in the lung superior-inferior cross-sectional area. Additionally, the range of volume error was within 1.29–9.23%.

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Towards Non-Invasive Lung Tumor Tracking Based on Patient Specific Model of Respiratory System

Cited by 14 publications

References 41 publications

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework

A Novel Computer Modeling and Simulation Technique for Bronchi Motion Tracking in Human Lungs under Respiration

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