Influence of the liver vascular distribution on its overall mechanical behavior: A first approach to multiscale fluid-structure homogenization

George, D.; Baniassadi, Majid; Hoarau, Yannick; Kugler, Michaël; Rémond, Yves

doi:10.1016/j.jocit.2018.09.008

Cited by 6 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…having the same properties in all directions) being −1 ⩽ ν ⩽ 0.5 (Gatt et al , 2015). It might be an uncommon behaviour to be compared with soft tissues that have commonly reported a value between 0.3 and 0.5 (George et al , 2018; Zhao et al , 2017). However, the most important parameters in the tissue-mimicking field are viscoelastic properties.…”

Section: Discussionmentioning

confidence: 99%

Soft-tissue-mimicking using silicones for the manufacturing of soft phantoms by fresh 3D printing

Tejo-Otero¹,

Colly

Courtial

et al. 2021

RPJ

View full text Add to dashboard Cite

Purpose The purpose of this study is to use the Freeform Reversible Embedding of Suspended Hydrogels (FRESH) additive manufacturing (AM) technique for manufacturing a liver phantom which can mimic the corresponding soft living tissue. One of the possible applications is surgical planning. Design/methodology/approach A thermo-reversible Pluronic® F-127-based support bath is used for the FRESH technique. To verify how three-dimensional (3D)-printed new materials can mimic liver tissue, dynamic mechanical analysis and oscillation shear rheometry tests are carried out to identify mechanical characteristics of different 3D printed silicone samples. Additionally, the differential scanning calorimetry was done on the silicone samples. Then, a validation of a 3D printed silicone liver phantom is performed with a 3D scanner. Finally, the surface topography of the 3D printed liver phantom was fulfiled and microscopy analysis of its surface. Findings Silicone samples were able to mimic the liver, therefore obtaining the first soft phantom of the liver using the FRESH technique. Practical implications Because of the use of soft silicones, surgeons could practice over these improved phantoms which have an unprecedented degree of living tissue mimicking, enhancing their rehearsal experience before surgery. Social implications An improvement in surgeons surgery skills would lead to a bettering in the patient outcome. Originality/value The first research study was carried out to mimic soft tissue and apply it to the 3D printing of organ phantoms using AM FRESH technique.

show abstract

Section: Discussionmentioning

confidence: 99%

Soft-tissue-mimicking using silicones for the manufacturing of soft phantoms by fresh 3D printing

Tejo-Otero¹,

Colly

Courtial

et al. 2021

RPJ

View full text Add to dashboard Cite

show abstract

“…Werner ( 2017 ) Pa Lamé constant, cf. George et al ( 2018 ), Evans et al ( 2013 ) Pa Lamé constant, cf. George et al ( 2018 ), Evans et al ( 2013 ) 280 K Temperature 8.3144 J/molK Gas constant m/s Darcy coefficient …”

Section: Numerical Solutionmentioning

confidence: 99%

Quantifying fat zonation in liver lobules: an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach

Lambers,

Waschinsky,

Schleicher

et al. 2024

Biomech Model Mechanobiol

View full text Add to dashboard Cite

Metabolic zonation refers to the spatial separation of metabolic functions along the sinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepatic metabolism to physiological requirements in health and disease (e.g., metabolic dysfunction-associated steatotic liver disease/MASLD). Zonated metabolic functions are influenced by zonal morphological abnormalities in the liver, such as periportal fibrosis and pericentral steatosis. We aim to analyze the interplay between microperfusion, oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule. Therefore we developed a continuum biomechanical, tri-phasic, bi-scale, and multicomponent in silico model, which allows to numerically simulate coupled perfusion-function-growth interactions two-dimensionally in liver lobules. The developed homogenized model has the following specifications: (i) thermodynamically consistent, (ii) tri-phase model (tissue, fat, blood), (iii) penta-substances (glycogen, glucose, lactate, FFA, and oxygen), and (iv) bi-scale approach (lobule, cell). Our presented in silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion, metabolic pathways and fat accumulation. The model thus allows the prediction of fat development in the liver lobule, depending on perfusion, oxygen and plasma concentration of free fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs). The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus, we will investigate how changes at the cellular scale affect perfusion at the lobular scale and vice versa. This allows to predict the zonation of fat distribution (periportal or pericentral) depending on initial conditions, as well as external and internal boundary value conditions.

show abstract

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue

Hashemi

Baniassadi

Baghani

et al. 2019

Biomech Model Mechanobiol

View full text Add to dashboard Cite

Influence of the liver vascular distribution on its overall mechanical behavior: A first approach to multiscale fluid-structure homogenization

Cited by 6 publications

References 5 publications

Soft-tissue-mimicking using silicones for the manufacturing of soft phantoms by fresh 3D printing

Soft-tissue-mimicking using silicones for the manufacturing of soft phantoms by fresh 3D printing

Quantifying fat zonation in liver lobules: an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue

Contact Info

Product

Resources

About