Skip to main content
Log in

A coupled finite-volume immersed boundary method for the simulation of bioheat transfer in 3D complex tumor

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Numerical schemes based on the immersed boundary method (IBM) offer the advantages of avoiding the body-conformal structured or unstructured grid generation process in the complex tumor morphology. This study uses the finite-volume immersed boundary method (FV-IBM) to solve bioheat physics in actual liver tumor tissue. IBM is employed in this methodology to enforce the boundary effect on the non-body conformal Cartesian grid. The finite-volume method (FVM) is used as a numerical technique to discretize the governing equations. The validation and verification of the FV-IB method have shown that the scheme is second-order accurate. Furthermore, the numerical results in the spherical tumor model are in good agreement with previously reported results for steady and transient cases. Results for MNPs-based hyperthermia investigation with two heat source (Gaussian and uniform) distribution patterns in the liver tumor are in good agreement with the numerical solution of COMSOL Multiphysics. Thus, a simple and robust FV-IBM-based numerical scheme is proposed to solve the bioheat models in arbitrary tissue shapes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability statement

All data generated or analysed during this study are included in this published article.

References

  1. Becker SM, Kuznetsov AV (2015) Heat transfer and fluid flow in biological processes. Elsevier Science Publishing, Cambridge. https://doi.org/10.1016/C2012-0-03651-4

    Book  Google Scholar 

  2. Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001

    Article  Google Scholar 

  3. Bellizzi G, Buci O (2018) Magnetic nanoparticle hyperthermia. In: Crocco L, Karanasiou I, James M, Conceição R (eds) Emerging electromagnetic technologies for brain diseases diagnostics, monitoring and therapy, 1st edn. Springer, Cham, pp 129–219. https://doi.org/10.1007/978-3-319-75007-1_6

    Chapter  Google Scholar 

  4. Krawczyk PM, Eppink B, Essers J et al (2011) Mild hyperthermia inhibits homologous recombination, induces BRCA2 degradation, and sensitizes cancer cells to poly (ADP-ribose) polymerase-1 inhibition. Proc Natl Acad Sci 108:9851–9856. https://doi.org/10.1073/pnas.1101053108

    Article  Google Scholar 

  5. Horsman MR, Overgaard J (2007) Hyperthermia: a potent enhancer of radiotherapy. J Clin Oncol 19:418–426. https://doi.org/10.1016/j.clon.2007.03.015

    Article  Google Scholar 

  6. Feng Y, Fuentes D, Hawkins A, Bass J, Rylander MN, Elliott A, Shetty A, Stafford RJ, Oden JT (2008) Nanoshell-mediated laser surgery simulation for prostate cancer treatment. Eng Comput 25:3–13. https://doi.org/10.1007/s00366-008-0109-y

    Article  Google Scholar 

  7. Kumar CSSR, Mohammad F (2011) Magnetic nanomaterials for hyperthermia-based therapy and controlled drug delivery. Adv Drug Deliv Rev 63:789–808. https://doi.org/10.1016/j.addr.2011.03.008

    Article  Google Scholar 

  8. Yarmolenko PS, Moon EJ, Landon C, Manzoor A, Hochman DW, Viglianti BL, Dewhirst MW (2011) Thresholds for thermal damage to normal tissues: an update. Int J Hyperthermia 27:320–343. https://doi.org/10.3109/02656736.2010.534527

    Article  Google Scholar 

  9. Mittal ER, Iaccarino G (2005) Immersed boundary methods. Annu Rev Fluid Mech 37:239–261. https://doi.org/10.1146/annurev.fluid.37.061903.175743

    Article  MathSciNet  Google Scholar 

  10. Péron S, Benoit C, Renaud T, Mary I (2020) An immersed boundary method on Cartesian adaptive grids for the simulation of compressible flows around arbitrary geometries. Eng Comput 37:2419–2437. https://doi.org/10.1007/s00366-020-00950-y

    Article  Google Scholar 

  11. 3D Slicer [Internet]. https:// www.slicer.org/. Accessed 10 Aug 2022

  12. Das S, Panda A, Deen NG, Kuipers JAM (2018) A sharp-interface immersed boundary method to simulate convective and conjugate heat transfer through highly complex periodic porous structures. Chem Eng Sci 191:1–18. https://doi.org/10.1016/j.ces.2018.04.061

    Article  Google Scholar 

  13. Pennes HH (1948) Analysis of tissue and arterial blood temperatures in the resting human forearm. J Appl Physiol 1:93–122. https://doi.org/10.1152/jappl.1948.1.2.93

    Article  Google Scholar 

  14. Versteeg HK, Malalasekera W (2007) An introduction to computational fluid dynamics: the finite volume method. Pearson Education, India. ISBN 8131720489

  15. Cooper GM (2000) The Development and Causes of Cancer In: The cell: a molecular approach 2nd edn. Sinauer Associates, Sunderland (MA). https://www.ncbi.nlm.nih.gov/books/NBK9963/

  16. Byrd BK, Krishnaswamy V, Gui J, Rooney T, Zuurbier R, Rosenkranz K, Paulsen K, Barth RJ (2020) The shape of breast cancer. Breast Cancer Res Treat 183:403–410. https://doi.org/10.1007/s10549-020-05780-6

    Article  Google Scholar 

  17. Bassett LW, Lee-Felker S (2018) Breast imaging screening and diagnosis. In: Bland K, Copeland E, Klimberg V, Gradishar W (eds) The breast, 5th edn. Elsevier, Amsterdam, pp 337–361. https://doi.org/10.1016/B978-0-323-35955-9.00026-X

    Chapter  Google Scholar 

  18. Kumar M, Roy S (2016) A sharp interface immersed boundary method for moving geometries with mass conservation and smooth pressure variation. Comput fluids 137:15–35. https://doi.org/10.1016/j.compfluid.2016.07.008

    Article  MathSciNet  Google Scholar 

  19. Kumar M, Roy S, Ali M (2016) An efficient immersed boundary algorithm for simulation of flows in curved and moving geometries. Comput Fluids 129:159–178. https://doi.org/10.1016/j.compfluid.2016.02.009

    Article  MathSciNet  Google Scholar 

  20. Gilmanov A, Sotiropoulos F (2005) A hybrid Cartesian/immersed boundary method for simulating flows with 3D, geometrically complex, moving bodies. J Comput Phys 207:457–492. https://doi.org/10.1016/j.jcp.2005.01.020

    Article  Google Scholar 

  21. Mark A, Rundqvist R, Edelvik F (2011) Comparison between different immersed boundary conditions for simulation of complex fluid flows. Fluid Dyn Mater Process 7:241–258. https://doi.org/10.3970/fdmp.2011.007.241

    Article  Google Scholar 

  22. Mark A, Van Wachem BGM (2008) Derivation and validation of a novel implicit second-order accurate immersed boundary method. J Comput Phys 227:6660–6680. https://doi.org/10.1016/j.jcp.2008.03.031

    Article  MathSciNet  Google Scholar 

  23. Municchi F, Radl S (2017) Consistent closures for Euler–Lagrange models of bi-disperse gas-particle suspensions derived from particle-resolved direct numerical simulations. Int J Heat Mass Transf 111:171–190. https://doi.org/10.1016/j.ijheatmasstransfer.2017.03.122

    Article  Google Scholar 

  24. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057. https://doi.org/10.1007/s10278-013-9622-7

    Article  Google Scholar 

  25. Doshi JB (2010) Differential equations for scientists and engineers. Narosa Publishing House, India

    Google Scholar 

  26. Kumar N, Singh S, Doshi JB (2013) Nodal integral method using quadrilateral elements for transport equations: part 1—convection-diffusion equation. Numer Heat Transfer Part B Fundam 64:1–21. https://doi.org/10.1080/10407790.2013.784125

    Article  Google Scholar 

  27. Salloum M, Ma R, Zhu L (2009) Enhancement in treatment planning for magnetic nanoparticle hyperthermia: optimization of the heat absorption pattern. Int J Hyperthermia 25:309–321. https://doi.org/10.1080/02656730902803118

    Article  Google Scholar 

  28. Lin CT, Liu KC (2009) Estimation for the heating effect of magnetic nanoparticles in perfused tissues. Int Commun Heat Mass Transf 36:241–244. https://doi.org/10.1016/j.icheatmasstransfer.2008.11.006

    Article  Google Scholar 

  29. Golneshan AA, Lahonian M (2011) The effect of magnetic nanoparticle dispersion on temperature distribution in a spherical tissue in magnetic fluid hyperthermia using the lattice Boltzmann method. Int J Hyperthermia 27:266–274. https://doi.org/10.3109/02656736.2010.519370

    Article  Google Scholar 

  30. Attaluri A, Ma R, Qiu Y, Li W, Zhu L (2011) Nanoparticle distribution and temperature elevations in prostatic tumours in mice during magnetic nanoparticle hyperthermia. Int J Hyperthermia 27:491–502. https://doi.org/10.3109/02656736.2011.584856

    Article  Google Scholar 

  31. He X, McGee S, Coad J, Schmidlin F, Iaizzo P, Swanlund DJ, Kluge S, Rudie E, Bischof J (2004) Investigation of the thermal and tissue injury behaviour in microwave thermal therapy using a porcine kidney model. Int J Hyperthermia 20:567–593. https://doi.org/10.1080/0265673042000209770

    Article  Google Scholar 

  32. Pearce JA (2009) Relationship between Arrhenius models of thermal damage and the CEM 43 thermal dose. Energy-Based Treat Tissue Assess 7181:718104. https://doi.org/10.1117/12.807999

    Article  Google Scholar 

  33. Schutt DJ, Haemmerich D (2008) Effects of variation in perfusion rates and of perfusion models in computational models of radio frequency tumor ablation. Med Phys 35:3462–3470. https://doi.org/10.1118/1.2948388

    Article  Google Scholar 

  34. Soetaert F, Dupré L, Ivkov R, Crevecoeur G (2015) Computational evaluation of amplitude modulation for enhanced magnetic nanoparticle hyperthermia. Biomed Eng 60:491–504. https://doi.org/10.1515/bmt-2015-0046

    Article  Google Scholar 

  35. Vaupel P, Kallinowski F, Okunieff P (1989) Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors. A Review, Cancer Res 49:6449–6465

    Google Scholar 

  36. Singh G, Kumar N, Avti P (2020) Computational evaluation of effectiveness for intratumoral injection strategies in magnetic nanoparticle assisted thermotherapy. Int J Heat Mass Transfer 148:119129. https://doi.org/10.1016/j.ijheatmasstransfer.2019.119129

    Article  Google Scholar 

  37. Singh A, Kumar N (2022) Parameterizing the effects of tumor shape in magnetic nanoparticle thermotherapy through a computational approach. J Heat Transfer 144:1–12. https://doi.org/10.1115/1.4052967

    Article  Google Scholar 

  38. Salloum M, Ma RH, Weeks D, Zhu L (2008) Controlling nanoparticle delivery in magnetic nanoparticle hyperthermia for cancer treatment: experimental study in agarose gel. Int J Hyperthermia 24:337–345. https://doi.org/10.1080/02656730801907937

    Article  Google Scholar 

  39. Siauve N, Nicolas L, Vollaire C, Marchal C (2004) Optimization of the sources in local hyperthermia using a combined finite element-genetic algorithm method. Int J Hyperthermia 20:815–833. https://doi.org/10.1080/02656730410001711664

    Article  Google Scholar 

  40. Nain S, Kumar N, Avti PK (2022) Computational investigation of the tumor position and ambient conditions on magnetic nanoparticle thermo-therapy. Therm Sci Eng Prog 34:101396. https://doi.org/10.1016/j.tsep.2022.101396

    Article  Google Scholar 

  41. Nain S, Kumar N, Chudasama B, Avti PK (2023) The SLP estimation of the nanoparticle systems using size-dependent magnetic properties for the magnetic hyperthermia therapy. J Magn Magn Mater 565:170219. https://doi.org/10.1016/j.jmmm.2022.170219

    Article  Google Scholar 

Download references

Acknowledgements

The research is performed in partial fulfillment of the requirements for PhD degree by Amritpal Singh from Thapar Institute of Engineering and Technology (TIET) Patiala.

Funding

This research received no specific grant from the public, commercial, or not-for-profit funding agencies.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Neeraj Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors also declare that they do not have any financial interests/personal relationships, which may be considered as potential competing interests.

Ethical approval

Ethical approval does not apply to this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

\(\left({A}_{p}-{\chi }_{e}{\alpha }_{e} {A}_{e}- {\chi }_{w}{\alpha }_{w} {A}_{w} -{\chi }_{n}{\alpha }_{n} {A}_{n}- {\chi }_{s}{\alpha }_{s} {A}_{s}\right){T}_{p}^{t+1}= (\left(1-{\chi }_{e}\right){A}_{e}+ {\chi }_{w}{\beta }_{w} {A}_{w}){T}_{e}^{t+1}+(\left(1-{\chi }_{w}\right){A}_{w}{+{\chi }_{e}{\beta }_{e} {A}_{e}) T}_{w}^{t+1}+ (\left(1-{\chi }_{n}\right){A}_{e}+ {\chi }_{s}{\beta }_{s} {A}_{s}){T}_{n}^{t+1}+((1-{\chi }_{s}){A}_{s}{+{\chi }_{n}{\beta }_{n} {A}_{n}) T}_{s}^{t+1}+ {B}_{p}+{\chi }_{e}{\gamma }_{e}{A}_{e}+ {\chi }_{w}{\gamma }_{w}{A}_{w}+{\chi }_{n}{\gamma }_{n}{A}_{n}+ {\chi }_{s}{\gamma }_{s}{A}_{s}\) A.1

For \({p}_{1}\) IB node (as shown in Fig. 1).

For east and north nodes: \({\chi }_{e}=1\) and \({\chi }_{n}\) \(=\) 1.

For west and south nodes: \({\chi }_{w}=0\) and \({\chi }_{s}\) \(=\) 0.

\(\left({A}_{p}-{\alpha }_{e} {A}_{e}- {\alpha }_{n} {A}_{n}\right){T}_{p}^{t+1}= ({A}_{w}{+{\beta }_{e} {A}_{e}) T}_{w}^{t+1}+({A}_{s}{+{\beta }_{n} {A}_{n}) T}_{s}^{t+1}+{B}_{p}+{\gamma }_{e}{A}_{e}+{\gamma }_{n}{A}_{n}\) A.2

For regular node, the \(\chi\) value in all direction is zero (‘p’ node, shown in Fig. 1).

\({A}_{p}{T}_{p}^{t+1}= {A}_{e}{T}_{e}^{t+1}+{A}_{w}{ T}_{w}^{t+1}+ {A}_{n}{T}_{n}^{t+1}+{A}_{s}{ T}_{s}^{t+1}+ {B}_{p}\) A.3

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, A., Kumar, N. A coupled finite-volume immersed boundary method for the simulation of bioheat transfer in 3D complex tumor. Engineering with Computers 39, 3743–3758 (2023). https://doi.org/10.1007/s00366-023-01797-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-023-01797-9

Keywords

Navigation