Thesis Document

📄 Master's Thesis

Investigation of Hip Joint Space Mapping Using Statistical Shape Modelling and 3D Imaging

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🔬 Research Overview

In this project, I investigated the sensitivity of Joint Space Mapping (JSM) to hip positioning and developed a semi-automated angulation protocol using CT data. Key findings include:

  • Test-retest precision of ~0.3mm in joint space measurements
  • No significant effect of hip rotation on Joint Space Width (JSW) measurements
  • Identification of manual processing as a key source of precision error
  • Development of statistical approaches for automated segmentation

statistical shape modelling Using statistical shape models to build canonical hip joint for semi-automated 3D modelling of patient hips.

🛠️ Technical Contributions

  1. Statistical Shape Modelling: Constructed canonical models for automated 3D hip joint reconstruction from CT images.

  2. C++ Angulation Protocol: Developed a novel coordinate system-based protocol for precise hip joint rotation measurement.

  3. Stradview Enhancement: Extended Stradview capabilities for improved joint space mapping analysis.

  4. Statistical Analysis: Employed statistical parametric mapping for comprehensive spatial data analysis.

📚 Reference

Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome

Turmezei, T.D., Treece, G.M., Gee, A.H. et al. Scientific Reports 10, 4127 (2020)

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