Page 413 - Advances in Biomechanics and Tissue Regeneration

REFERENCES 411

FIG. 21.5 (A) Gruen zones and (B) intersection of FEM and RPIM solutions.

distal stem, since the X-ray image does not include this region. FEM and RPIM’s solutions were also compared. In
Fig. 21.5B the black-and-white map indicates the spatial intersection of the two solutions. White nodes mean that both
solutions predicted bone, whereas black nodes reflect contradictory results or nonbone zones predicted by both
methods. With this qualitative analysis, it is possible to depict that FEM and RPIM produce similar results.

21.4 CONCLUSIONS

The mechanical model adopted in this work produced a trabecular distribution similar to the X-ray image, proving
the efficiency of the remodeling algorithm. Moreover the success of the approach was independent of the numerical
method used. However, unlike FEM, RPIM’s solution was able to predict the compressive trabecular group and pro-
duce more accurate and smoother apparent density distribution maps. As future work a three-dimensional (3-D)
model should be used to simulate/predict the 3-D structural interaction between femur and implant. Nonetheless,
using this remodeling algorithm to predict bone remodeling after implant insertion is a promising approach with
an important impact on implants’ evaluation and success of THA.

Acknowledgments
¸
The authors truly acknowledge the funding provided by Minist erio da Ci^ encia, Tecnologia e Ensino Superior, Fundacão para a Ci^ encia e a Tecnologia
(Portugal), under Grants SFRH/BD/133105/2017 and by project funding MIT-EXPL/ISF/0084/2017. Additionally the authors gratefully acknowl-
edge the funding of Project NORTE-01-0145-FEDER-000022—SciTech—Science and Technology for Competitive and Sustainable Industries, cofi-
nanced by Programa Operacional Regional do Norte (NORTE2020), through Fundo Europeu de Desenvolvimento Regional (FEDER).

References
[1] S. Affatato, Perspectives in Total Hip Arthroplasty: Advances in Biomaterials and Their Tribological Interactions, Woodhead Publishing,
Oxford, UK, 2014.
[2] A.D. Woolf, B. Pfleger, Burden of major musculoskeletal conditions, Bull. World Health Organ. 81 (9) (2003) 646–656, https://doi.org/10.1371/
journal.pone.0090633.
[3] S. Kurtz, K. Ong, E. Lau, F. Mowat, M. Halpern, Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to
2030, J. Bone Jt. Surg. 89 (4) (2007) 780, https://doi.org/10.2106/JBJS.F.00222.
[4] R.L. Barrack, Economics of revision total hip arthroplasty, Clin. Orthop. Relat. Res. (319) (1995) 209–214, https://doi.org/10.1016/J.
CUOR.2006.02.007.
[5] C.K. Ledford, T.S. Watters, S.S. Wellman, D.E. Attarian, M.P. Bolognesi, Risk versus reward: total joint arthroplasty outcomes after various solid
organ transplantations, J. Arthroplast. 29 (8) (2014) 1548–1552, https://doi.org/10.1016/j.arth.2014.03.027.
[6] M.R. Abdul Kadir, Computational Biomechanics of the Hip Joint, Springer Berlin Heidelberg, Berlin, Heidelberg, 2014.
[7] J. Belinha, R.M.N. Jorge, L.M.J.S. Dinis, A meshless microscale bone tissue trabecular remodelling analysis considering a new anisotropic bone
tissue material law, Comput. Methods Biomech. Biomed. Engin. 16 (11) (2013) 1170–1184, https://doi.org/10.1080/10255842.2012.654783.
[8] M.M.A. Peyroteo, J. Belinha, S. Vinga, L.M.J.S. Dinis, R.M. Natal Jorge, Mechanical bone remodelling: comparative study of distinct numerical
approaches, Eng. Anal. Bound. Elem. (2018), https://doi.org/10.1016/J.ENGANABOUND.2018.01.011.

II. MECHANOBIOLOGY AND TISSUE REGENERATION