Structural similarity analysis of midfacial fractures: a feasibility study

Romke Rozema*, Herbert T Kruitbosch, Baucke van Minnen, Bart Dorgelo, Joep Kraeima, Peter M A van Ooijen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
10 Downloads (Pure)

Abstract

The structural similarity index metric is used to measure the similarity between two images. The aim here was to study the feasibility of this metric to measure the structural similarity and fracture characteristics of midfacial fractures in computed tomography (CT) datasets following radiation dose reduction, iterative reconstruction (IR) and deep learning reconstruction. Zygomaticomaxillary fractures were inflicted on four human cadaver specimen and scanned with standard and low dose CT protocols. Datasets were reconstructed using varying strengths of IR and the subsequently applying the PixelShine™ deep learning algorithm as post processing. Individual small and non-dislocated fractures were selected for the data analysis. After attenuating the osseous anatomy of interest, registration was performed to superimpose the datasets and subsequently to measure by structural image quality. Changes to the fracture characteristics were measured by comparing each fracture to the mirrored contralateral anatomy. Twelve fracture locations were included in the data analysis. The most structural image quality changes occurred with radiation dose reduction (0.980036±0.011904), whilst the effects of IR strength (0.995399±0.001059) and the deep learning algorithm (0.999996±0.000002) were small. Radiation dose reduction and IR strength tended to affect the fracture characteristics. Both the structural image quality and fracture characteristics were not affected by the use of the deep learning algorithm. In conclusion, evidence is provided for the feasibility of using the structural similarity index metric for the analysis of structural image quality and fracture characteristics.

Original languageEnglish
Pages (from-to)1571-1578
Number of pages8
JournalQuantitative imaging in medicine and surgery
Volume12
Issue number2
Early online date17-Aug-2021
DOIs
Publication statusPublished - Feb-2022

Keywords

  • Maxillofacial trauma
  • structural similarity index
  • fracture visualization
  • computed tomography (CT)
  • low dose
  • advanced model based iterative reconstruction
  • deep learning
  • COMPUTED-TOMOGRAPHY
  • ITERATIVE RECONSTRUCTION

Cite this