Ultrasonic Interferometric Procedure for Quantifying the Bone-Implant Interface

Sensors (Basel). 2023 Jun 26;23(13):5942. doi: 10.3390/s23135942.

Abstract

The loosening of an artificial joint is a frequent and critical complication in orthopedics and trauma surgery. Due to a lack of accuracy, conventional diagnostic methods such as projection radiography cannot reliably diagnose loosening in its early stages or detect whether it is associated with the formation of a biofilm at the bone-implant interface. In this work, we present a non-invasive ultrasound-based interferometric measurement procedure for quantifying the thickness of the layer between bone and prosthesis as a correlate to loosening. In principle, it also allows for the material characterization of the interface. A well-known analytical model for the superposition of sound waves reflected in a three-layer system was combined with a new method in data processing to be suitable for medical application at the bone-implant interface. By non-linear fitting of the theoretical prediction of the model to the actual shape of the reflected sound waves in the frequency domain, the thickness of the interlayer can be determined and predictions about its physical properties are possible. With respect to determining the layer's thickness, the presented approach was successfully applied to idealized test systems and a bone-implant system in the range of approx. 200 µm to 2 mm. After further optimization and adaptation, as well as further experimental tests, the procedure offers great potential to significantly improve the diagnosis of prosthesis loosening at an early stage and may also be applicable to detecting the formation of a biofilm.

Keywords: film thickness; interferometric measurement; material characterization; non-invasive technique; prosthesis loosening; quantitative ultrasound; thin layers; ultrasonic reflection.

MeSH terms

  • Artificial Limbs*
  • Bone-Implant Interface*
  • Prosthesis Implantation
  • Sound
  • Ultrasonics

Grants and funding

This research was funded by Technologieallianz Oberfranken (TAO).