Mathematical Modelling of Tissue Bubble Dynamics During Decompression

Gernhardt, ML

doi:10.1007/978-94-009-1299-1_16

Cited by 1 publication

(2 citation statements)

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“…denser tissue will contain larger bubbles that persist for a longer time; tissues with lower surface tension will contain bubbles with a larger radius; finally, more lipid rich tissues (higher solubility coefficient) will also tend to contain larger bubbles. Support for these conclusions is found widely in the modelling literature [ 34 , 40 , 47 , 48 ] and to a certain degree in the experimental literature [ 22 , 28 , 49 ]. However, for the majority of data from in vivo experiments, it is difficult or even impossible to separate the individual effects of material parameters from each other and also from those of perfusion.…”

Section: Mathematical and Computational Methodologymentioning

confidence: 78%

“…The Bulk Modulus form [ 34 ] where M = K / V aff , K is the tissue bulk modulus and V aff is the affected tissue volume, which is the most widely applied tissue elasticity form in the literature, appearing in several bubble models [ 35 – 37 ]. It has also been incorporated into the commercial Bubble Volume Model (BVM) [ 38 ], a probabilistic model that bases the risk of DCS on the total bubble volume.…”

Section: Mathematical and Computational Methodologymentioning

confidence: 99%

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A combined three-dimensional in vitro–in silico approach to modelling bubble dynamics in decompression sickness

Walsh

Stride

Cheema³

et al. 2017

J. R. Soc. Interface.

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The growth of bubbles within the body is widely believed to be the cause of decompression sickness (DCS). Dive computer algorithms that aim to prevent DCS by mathematically modelling bubble dynamics and tissue gas kinetics are challenging to validate. This is due to lack of understanding regarding the mechanism(s) leading from bubble formation to DCS. In this work, a biomimetic in vitro tissue phantom and a three-dimensional computational model, comprising a hyperelastic strain-energy density function to model tissue elasticity, were combined to investigate key areas of bubble dynamics. A sensitivity analysis indicated that the diffusion coefficient was the most influential material parameter. Comparison of computational and experimental data revealed the bubble surface's diffusion coefficient to be 30 times smaller than that in the bulk tissue and dependent on the bubble's surface area. The initial size, size distribution and proximity of bubbles within the tissue phantom were also shown to influence their subsequent dynamics highlighting the importance of modelling bubble nucleation and bubble–bubble interactions in order to develop more accurate dive algorithms.

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