Developing an Analogue Residual Limb for Comparative DVC Analysis of Transtibial Prosthetic Socket Designs.

DVC amputation digital volume correlation micro-CT prosthetic

Journal

Materials (Basel, Switzerland)
ISSN: 1996-1944
Titre abrégé: Materials (Basel)
Pays: Switzerland
ID NLM: 101555929

Informations de publication

Date de publication:
07 Sep 2020
Historique:
received: 31 07 2020
revised: 01 09 2020
accepted: 02 09 2020
entrez: 10 9 2020
pubmed: 11 9 2020
medline: 11 9 2020
Statut: epublish

Résumé

Personalised prosthetic sockets are fabricated by expert clinicians in a skill- and experience-based process, with research providing tools to support evidence-based practice. We propose that digital volume correlation (DVC) may offer a deeper understanding of load transfer from prosthetic sockets into the residual limb, and tissue injury risk. This study's aim was to develop a transtibial amputated limb analogue for volumetric strain estimation using DVC, evaluating its ability to distinguish between socket designs. A soft tissue analogue material was developed, comprising silicone elastomer and sand particles as fiducial markers for image correlation. The material was cast to form an analogue residual limb informed by an MRI scan of a person with transtibial amputation, for whom two polymer check sockets were produced by an expert prosthetist. The model was micro-CT scanned according to (i) an unloaded noise study protocol and (ii) a case study comparison between the two socket designs, loaded to represent two-legged stance. The scans were reconstructed to give 108 µm voxels. The DVC noise study indicated a 64 vx subvolume and 50% overlap, giving better than 0.32% strain sensitivity, and ~3.5 mm spatial resolution of strain. Strain fields induced by the loaded sockets indicated tensile, compressive and shear strain magnitudes in the order of 10%, with a high signal:noise ratio enabling distinction between the two socket designs. DVC may not be applicable for socket design in the clinical setting, but does offer critical 3D strain information from which existing in vitro and in silico tools can be compared and validated to support the design and manufacture of prosthetic sockets, and enhance the biomechanical understanding of the load transfer between the limb and the prosthesis.

Identifiants

pubmed: 32906701
pii: ma13183955
doi: 10.3390/ma13183955
pmc: PMC7557588
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Engineering and Physical Sciences Research Council
ID : EP/M508147/1
Organisme : Engineering and Physical Sciences Research Council
ID : EP/N02723X/1
Organisme : Engineering and Physical Sciences Research Council
ID : EP/N02723X/1
Organisme : Royal Academy of Engineering
ID : RF/130

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Auteurs

Kathryn Rankin (K)

Bioengineering Science Research Group, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK.
µ-VIS X-Ray Imaging Centre, University of Southampton, Southampton SO17 1BJ, UK.

Joshua Steer (J)

Bioengineering Science Research Group, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK.

Joshua Paton (J)

Bioengineering Science Research Group, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK.

Mark Mavrogordato (M)

µ-VIS X-Ray Imaging Centre, University of Southampton, Southampton SO17 1BJ, UK.

Alexander Marter (A)

Bioengineering Science Research Group, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK.

Peter Worsley (P)

Skin Health Research Group, School of Health Sciences, University of Southampton, Southampton SO16 6YD, UK.
Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK.

Martin Browne (M)

Bioengineering Science Research Group, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK.
Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK.

Alexander Dickinson (A)

Bioengineering Science Research Group, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK.
Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK.

Classifications MeSH