Abstract
In this chapter, we evaluate the 3D tumor trajectories from patient-specific biomechanical model of the respiratory system, which takes into account the physiology of respiratory motion to simulate irregular motion. The behaviour of the lungs, driving directly by simulated actions of the breathing muscles, i.e. the diaphragm and the intercostal muscles (the rib cage). In this chapter, the lung model is monitored and controlled by a personalized lung pressure-volume relationship during a whole respiratory cycle. The lung pressure is patient specific and calculated by an optimization framework based on inverse finite element analysis. We have evaluated the motion estimation accuracy on two selected patients, with small and large breathing amplitudes (Patient 1 = 10.9 mm, Patient 10 = 26.06 mm). In this order, the lung tumor trajectories identified from 4D CT scan images were used as reference and compared with the 3D lung tumor trajectories estimated from finite element simulation during the whole cycle of breathing. Over all phases of respiration, the average mean error is less than 1.8 ± 1.3 mm. We believe that this model, despite of others takes into account the challenging problem of the respiratory variabilities and can potentially be incorporated effectively in Treatment Planning System (TPS) and as lung tumor motion tracking system during radiation treatment.
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Notes
- 1.
ITK-SNAP is a software application used to segment structures in 3D medical images.
- 2.
EV: element volume and OEV: Optimal element volume is the volume of an equilateral tetrahedron with the same circumradius as the element. (The circumradius is the radius of the sphere passing through the four vertices of the tetrahedron.)
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Acknowledgements
This research is supported by the LABEX PRIMES (ANR-11-LABX-0063), within the program Investissements dAvenir(ANR-11-IDEX- 0007) operated by the French National Research Agency (ANR) and by France Hadron.
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Ladjal, H., Beuve, M., Shariat, B. (2020). Lung Tumor Tracking Based on Patient-Specific Biomechanical Model of the Respiratory System. In: Miller, K., Wittek, A., Joldes, G., Nash, M., Nielsen, P. (eds) Computational Biomechanics for Medicine. MICCAI MICCAI 2019 2018. Springer, Cham. https://doi.org/10.1007/978-3-030-42428-2_2
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