Predictive modeling of lung motion over the entire respiratory cycle using measured pressure‐volume data, 4DCT images, and finite‐element analysis

Abstract

Purpose: Predicting complex patterns of respiration can benefit the management of the respiratory motion for radiation therapy of lung cancer. The purpose of the present work was to develop a patient-specific, physiologically relevant respiratory motion model which is capable of predicting lung tumor motion over a complete normal breathing cycle. Methods: Currently employed techniques for generating the lung geometry from four-dimensional computed tomography data tend to lose details of mesh topology due to excessive surface smoothening. Some of the existing models apply displacement boundary conditions instead of the intrapleural pressure as the actual motive force for respiration, while others ignore the nonlinearity of lung tissues or the mechanics of pleural sliding. An intermediate nonuniform rational basis spline surface representation is used to avoid multiple geometric smoothing procedures used in the computational mesh preparation. Measured chest pressure-volume relationships are used to simulate pressure loading on the surface of the model for a given lung volume, as in actual breathing. A hyperelastic model, developed from experimental observations, has been used to model the lung tissue material. Pleural sliding on the inside of the ribcage has also been considered. Results:The finite-element model has been validated using landmarks from four patient CT data sets over 34 breathing phases. The average differences of end-inspiration in position between the landmarks and those predicted by the model are observed to be 0.450Ϯ 0.330 cm for Patient P1, 0.387Ϯ 0.169 cm for Patient P2, 0.319Ϯ 0.186 cm for Patient P3, and 0.204Ϯ 0.102 cm for Patient P4 in the magnitude of error vector, respectively. The average errors of prediction at landmarks over multiple breathing phases in superior-inferior direction are less than 3 mm. Conclusions:The prediction capability of pressure-volume curve driven nonlinear finite-element model is consistent over the entire breathing cycle. The biomechanical parameters in the model are physiologically measurable, so that the results can be extended to other patients and additional neighboring organs affected by respiratory motion.

Towards modeling tumor motion in the deflated lung for minimally invasive ablative procedures

“…In vivo experiments are required to fine-tune the pipeline parameters and ensure its reliability and rigor for clinical applications. Comparing the results obtained in this study with those reported by Eom et al [23] for modeling motion of the respiring lung over the entire respiratory cycle (maximum position error ¼ 5.8 mm; average position error ¼ 3.5 mm) indicates that our results are encouraging. Errors obtained in this investigation include error components from the image processing, experimental procedure and FE modeling of the pipeline.…”

“…Moreover, the biaxial tension test data used in the aforementioned work was obtained using old measurement instruments that lacked high precision, while the test data analysis involved significant sample geometry and boundary condition uncertainties. Due to the lack of other sources of lung tissue hyperelasticity data, Eom et al also used the same biaxial tension dataset in their recently presented lung biomechanical model [23]. They employed FEM to develop a respiratory motion model for predicting lung tumor motion over a complete normal breathing cycle.…”

Estimation of lung tumor position from multiple anatomical features on 4D‐CT using multiple regression analysis

“…The results in Table 2 show that the explanatory variable of the lung volume had a significant difference in the 3D direction compared with the other explanatory variables, showing that the lung volume had a significant influence on the estimated lung tumor position. In current clinical practice, a single anatomical signal and a correspondence model between a single anatomical signal and a motion of the internal anatomy are typically used to reconstruct 4D-CT 14 and to estimate target position, [15][16][17] respectively. In addition, correspondence models have been used to improve image quality in 4D-CT image reconstruction.…”

A poroelastic model coupled to a fluid network with applications in lung modelling

“…In this work, we focus on the link between tissue deformation and ventilation. Previous work of whole organ lung models has typically focused on modelling either ventilation or tissue deformation [1,2,3,4,5,6,7,8]. However, the two components are intrinsically linked; evaluating them separately does not necessarily provide an accurate description of lung function.…”