Research Papers

Analytic Intermodel Consistent Modeling of Volumetric Human Lung Dynamics

[+] Author and Article Information
Olusegun Ilegbusi

Department of Mechanical and
Aerospace Engineering,
University of Central Florida,
4000 Central Florida Blvd.,
Orlando, FL 32826
e-mail: Ilegbusi@ucf.edu

Behnaz Seyfi

Department of Mechanical and
Aerospace Engineering,
University of Central Florida,
4000 Central Florida Blvd.,
Orlando, FL 32826
e-mail: bsn@knights.ucf.edu

John Neylon

Department of Radiation Oncology,
University of California,
Los Angeles, CA 90095
e-mail: jneylon@mednet.ucla.edu

Anand P. Santhanam

Department of Radiation Oncology,
University of California,
Los Angeles, CA 90095
e-mail: ASanthanam@mednet.ucla.edu

1Corresponding author.

Manuscript received October 16, 2014; final manuscript received July 27, 2015; published online September 7, 2015. Assoc. Editor: Guy M. Genin.

J Biomech Eng 137(10), 101005 (Sep 07, 2015) (9 pages) Paper No: BIO-14-1518; doi: 10.1115/1.4031349 History: Received October 16, 2014; Revised July 27, 2015

Human lung undergoes breathing-induced deformation in the form of inhalation and exhalation. Modeling the dynamics is numerically complicated by the lack of information on lung elastic behavior and fluid–structure interactions between air and the tissue. A mathematical method is developed to integrate deformation results from a deformable image registration (DIR) and physics-based modeling approaches in order to represent consistent volumetric lung dynamics. The computational fluid dynamics (CFD) simulation assumes the lung is a poro-elastic medium with spatially distributed elastic property. Simulation is performed on a 3D lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of a human subject. The heterogeneous Young’s modulus (YM) is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The deformation obtained from the CFD is then coupled with the displacement obtained from the 4D lung DIR by means of the Tikhonov regularization (TR) algorithm. The numerical results include 4DCT registration, CFD, and optimal displacement data which collectively provide consistent estimate of the volumetric lung dynamics. The fusion method is validated by comparing the optimal displacement with the results obtained from the 4DCT registration.

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Keall, P. J. , Mageras, G. S. , Balter, J. M. , Emery, R. S. , Forster, K. M. , Jiang, S. B. , Kapatoes, J. M. , Low, D. A. , Murphy, M. J. , and Murray, B. R. , 2006, “ The Management of Respiratory Motion in Radiation Oncology Report of AAPM Task Group 76,” Med. Phys., 33(10), pp. 3874–3938. [CrossRef] [PubMed]
Santhanam, A. P. , Min, Y. , Rolland, J. P. , Imielinska, C. , and Kupelian, P. A. , 2011, “ Four-Dimensional Computed Tomography Lung Registration Methods,” Lung Imaging and Computer Aided Diagnosis, A. EI-Baz and J. S. Suri , eds., CRC Press, Boca Raton, FL, pp. 85–108.
Plathow, C. , Ley, S. , Fink, C. , Puderbach, M. , Hosch, W. , Schmähl, A. , Debus, J. , and Kauczor, H.-U. , 2004, “ Analysis of Intrathoracic Tumor Mobility During Whole Breathing Cycle by Dynamic MRI,” Int. J. Radiat. Oncol., 59(4), pp. 952–959. [CrossRef]
Ilegbusi, O. J. , Seyfi, B. , and Salvin, R. , 2013, “ Patient-Specific Model of Lung Deformation Using Spatially Dependent Constitutive Parameters,” Math. Comput. Modell. Dyn., 20(6), pp. 546–556. [CrossRef]
Al-Mayah, A. , Moseley, J. , and Brock, K. , 2008, “ Contact Surface and Material Nonlinearity Modeling of Human Lungs,” Phys. Med. Biol., 53(1), pp. 305–317. [CrossRef] [PubMed]
Ilegbusi, O. J. , Li, Z. , Seyfi, B. , Min, Y. , Meeks, S. , Kupelian, P. , and Santhanam, A. P. , 2012, “ Modeling Airflow Using Subject-Specific 4DCT-Based Deformable Volumetric Lung Models,” J. Biomed. Imaging, 2012, pp. 4–14.
Seyfi, B. , Santhanam, A. P. , and Ilegbusi, O. J. , “ Application of Fusion Algorithm to Human Lung Dynamics,” ASME Paper No. IMECE2012-86407.
Brock, K. , Sharpe, M. , Dawson, L. , Kim, S. , and Jaffray, D. , 2005, “ Accuracy of Finite Element Model-Based Multi-Organ Deformable Image Registration,” Med. Phys., 32(6), pp. 1647–1695. [CrossRef] [PubMed]
Werner, R. , Ehrhardt, J. , Schmidt, R. , and Handels, H. , “ Modeling Respiratory Lung Motion: A Biophysical Approach Using Finite Element Methods,” Proc. SPIE, 6916, p. 69160N.
Eom, J. , Xu, X. G. , De, S. , and Shi, C. , 2010, “ Predictive Modeling of Lung Motion Over the Entire Respiratory Cycle Using Measured Pressure-Volume Data, 4DCT Images, and Finite-Element Analysis,” Med. Phys., 37(8), pp. 4389–4423. [CrossRef] [PubMed]
Chang, J. Y. , “ Guidelines and Techniques for Image-Guided Radiotherapy for Non-Small Cell Lung Cancer,” Image-Guided Radiotherapy of Lung Cancer, J. D. Cox , J. Y. Chang , and R. Komaki , eds., CRC Press, Boca Raton, FL, p. 25.
Tikhonov, A. N. , and Arsenin, V. Y. , 1977, Solutions of Ill-Posed Problems, Winston, Halsted Press, Washington, NY.
Vaina, L. M. , Beardsley, S. A. , and Rushton, S. K. , 2004, Optic Flow and Beyond, Springer, Dordrecht.
Min, Y. , Neylon, J. , Shah, A. , Meeks, S. , Lee, P. , Kupelian, P. , and Santhanam, A. P. , 2014, “ 4D-CT Lung Registration Using Anatomy-Based Multi-Level Multi-Resolution Optical Flow Analysis and Thin-Plate Splines,” Int. J. Comput. Assisted Radiol. Surg., 9(5), pp. 875–889. [CrossRef]
Materialise, 2013, “Mimics® 13.1, Medical Image Segmentation for Engineering on AnatomyTM,” Materialise HQ, Leuven, Belgium, http://www.biomedical.materialise.com/mimics
Materialise, 2013, “ 3-matic® 5.01, 3-matic puts the ‘Engineering’ in Engineering on Anatomy™,” Materialise HQ, Leuven, Belgium, http://biomedical.materialise.com/3-matic-0
Zhang, H. , Zhang, X. , Ji, S. , Guo, Y. , Ledezma, G. , Elabbasi, N. , and deCougny, H. , 2003, “ Recent Development of Fluid–Structure Interaction Capabilities in the Adina System,” Comput. Struct., 81(8), pp. 1071–1085. [CrossRef]
Sobin, S. , Fung, Y. , and Tremer, H. , 1988, “ Collagen and Elastin Fibers in Human Pulmonary Alveolar Walls,” J. Appl. Physiol., 64(4), pp. 1659–1675. [PubMed]
Toshima, M. , Ohtani, Y. , and Ohtani, O. , 2004, “ Three-Dimensional Architecture of Elastin and Collagen Fiber Networks in the Human and Rat Lung,” Arch. Histol. Cytol., 67(1), pp. 31–40. [CrossRef] [PubMed]
ADINA, 2013, “ ADINA 9.0: Automatic Dynamic Incremental Nonlinear Analysis,” ADINA R&D Inc., Watertown, MA, http://www.adina.com
Santhanam, A. P. , Min, Y. , Mudur, S. P. , Rastogi, A. , Ruddy, B. H. , Shah, A. , Divo, E. , Kassab, A. , Rolland, J. P. , and Kupelian, P. , 2010, “ An Inverse Hyper-Spherical Harmonics-Based Formulation for Reconstructing 3d Volumetric Lung Deformations,” C. R. Méc., 338(7), pp. 461–473. [CrossRef]
Villard, P.-F. , Beuve, M. , Shariat, B. , Baudet, V. , and Jaillet, F. , 2005, “ Simulation of Lung Behaviour With Finite Elements: Influence of Bio-Mechanical Parameters,” Third International Conference on Medical Information Visualisation–Biomedical Visualisation (MediVis 2005), London, July 5–7, pp. 9–14.
Chhatkuli, S. , Koshizuka, S. , and Uesaka, M. , 2009, “ Dynamic Tracking of Lung Deformation During Breathing by Using Particle Method,” Model. Simul. Eng., 2009, pp. 7–14.


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Fig. 1

Three dimensional lung shapes generated from 4D-CT scan: (a) surface meshes (b) cutout of a section of reconstructed lungs, typical computational volume meshes generated and applied in the finite-element model

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Fig. 2

Prescribe inlet pressure

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Fig. 3

3DCT lung anatomy used as the reference geometry for the CFD analysis

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Fig. 6

Lung deformation along the (i) x axis, (ii) y axis, and (iii) z axis computed for the (a) left lung and (b) right lung using CFD model

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Fig. 7

Optimal lung deformation along the (i) x axis, (ii) y axis, and (iii) z axis computed for the (a) left lung and (b) right lung

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Fig. 8

Differences in the displacement observed between the optimal lung deformation and the deformation observed in the 4D-CT DIR is shown for left and right lungs

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Fig. 9

Location of selected landmarks in (a) left and (b) right lung

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Fig. 4

Lung deformation along the (i) x axis, (ii) y axis, and (iii) z axis computed for the (a) left lung and (b) right lung using a multiresolution optical flow algorithm

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Fig. 5

Three-dimensional volume rendering of the YM values for the (a) right and (b) left lung obtained from the 4DCT lung registration and a linear deformation model



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