Modeling Airflow Using Subject-Specific 4DCT-Based Deformable Volumetric Lung Models

Ilegbusi, Olusegun J.; Li, Zhiliang; Seyfi, Behnaz; Min, Yugang; Meeks, Sanford L.; Kupelian, Patrick A.; Santhanam, Anand P.

doi:10.1155/2012/350853

Cited by 8 publications

(3 citation statements)

References 14 publications

(16 reference statements)

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“…Currently, in silico studies addressed to respiratory lung modeling have been covering detailed patient-specific 3D lung reconstruction [13,14], nasopharynx-trachea ventilation features [6,7,15], 3D-1D coupling of upper and middle airway segments, and FSI simulations [16]. The lumped models operate with lung mechanics in terms of tidal volume and pressure with lower details [17,18].…”

Section: Discussionmentioning

confidence: 99%

Multiscale CT-Based Computational Modeling of Alveolar Gas Exchange during Artificial Lung Ventilation, Cluster (Biot) and Periodic (Cheyne-Stokes) Breathings and Bronchial Asthma Attack

Golov¹,

Simakov²,

Soe³

et al. 2017

Computation

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An airflow in the first four generations of the tracheobronchial tree was simulated by the 1D model of incompressible fluid flow through the network of the elastic tubes coupled with 0D models of lumped alveolar components, which aggregates parts of the alveolar volume and smaller airways, extended with convective transport model throughout the lung and alveolar components which were combined with the model of oxygen and carbon dioxide transport between the alveolar volume and the averaged blood compartment during pathological respiratory conditions. The novel features of this work are 1D reconstruction of the tracheobronchial tree structure on the basis of 3D segmentation of the computed tomography (CT) data; 1D−0D coupling of the models of 1D bronchial tube and 0D alveolar components; and the alveolar gas exchange model. The results of our simulations include mechanical ventilation, breathing patterns of severely ill patients with the cluster (Biot) and periodic (Cheyne-Stokes) respirations and bronchial asthma attack. The suitability of the proposed mathematical model was validated. Carbon dioxide elimination efficiency was analyzed in all these cases. In the future, these results might be integrated into research and practical studies aimed to design cyberbiological systems for remote real-time monitoring, classification, prediction of breathing patterns and alveolar gas exchange for patients with breathing problems.

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Section: Discussionmentioning

confidence: 99%

Multiscale CT-Based Computational Modeling of Alveolar Gas Exchange during Artificial Lung Ventilation, Cluster (Biot) and Periodic (Cheyne-Stokes) Breathings and Bronchial Asthma Attack

Golov¹,

Simakov²,

Soe³

et al. 2017

Computation

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“…Some models only focus on the human upper airway, but small particles like viruses deposit on the lower airway, which requires the lower airway model. Beyond the 7th or 9th generation, the bifurcation model is hardly provided only based on the inadequate clarity of the scanned images, so four-dimensional CT [15,16], 3-D U-Net [17], and digital topologic [18] are used to reconstruct more distal bifurcation. Compared with the bifurcation structure of higher generation, the deposition in different lobes seems more worthy to study since those small pathogen-laden aerosols always rush deep into the lobes.…”

Section: Introductionmentioning

confidence: 99%

Various pathogen-laden aerosol deposition in the realistic human airway during inhalation

Luo

Zheng²,

Qian³

2022

E3S Web Conf.

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Studying the deposition of different pathogens with various sizes and shapes is vital for understanding various respiratory infectious diseases. Few studies focus on the deposition of pathogen-laden aerosol during inhalation, especially for different respiratory infectious pathogens. This paper studied the depositions of H3N2, SAR-CoV-2, Ebola virus, Escherichia coli, and different sizes of droplets in the realistic human respiratory airway during inhalation. And results show that large droplets are mainly deposited in the upper respiratory tract, while most of the small particles, especially viruses, will transmit to somewhere further than bronchi-G7 and be deposited into the deep lobes of the lungs. Over 90% of single virus particles will inhale into lobes. The deposition efficiency of pathogens in the right lobes is significantly higher than that in the left, and this phenomenon is more obvious in the superior lobes, which may also explain why lung carcinomas are more likely to develop in the right lung. Compared with other viruses, SARS-CoV-2 is more inhaled into the right superior lobe, which should be paid attention to. This paper may help learn about various respiratory infectious diseases and provide references for treatment methods and drug delivery locations.

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“…One solution to account for the breathing-induced lung tissue motion has been to employ lung motion models for planning and treatment. [2][3][4][5][6][7][8][9][10][11][12][13] Lung and lung tumor motion models have been developed based on lung tissue elasticity, 2,8,[11][12][13] respiratory phase, 3,[5][6][7]9 and biomechanical tissue properties. 4,10 These models, however, did not address the lung motion induced by the heart's pulsatile motion.…”

Section: Introductionmentioning

confidence: 99%

Modeling and incorporating cardiac‐induced lung tissue motion in a breathing motion model

et al. 2014

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Purpose:The purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model. Methods:The authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement γ ( X 0 ) of each voxel ( X 0 ) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow. Results: The average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement. Conclusions:The authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.

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Modeling Airflow Using Subject-Specific 4DCT-Based Deformable Volumetric Lung Models

Cited by 8 publications

References 14 publications

Multiscale CT-Based Computational Modeling of Alveolar Gas Exchange during Artificial Lung Ventilation, Cluster (Biot) and Periodic (Cheyne-Stokes) Breathings and Bronchial Asthma Attack

Multiscale CT-Based Computational Modeling of Alveolar Gas Exchange during Artificial Lung Ventilation, Cluster (Biot) and Periodic (Cheyne-Stokes) Breathings and Bronchial Asthma Attack

Various pathogen-laden aerosol deposition in the realistic human airway during inhalation

Modeling and incorporating cardiac‐induced lung tissue motion in a breathing motion model

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