Preoperative Virtual Reduction Planning Algorithm of Fractured Pelvis Based on Adaptive Templates

Area of Science:

• Orthopedic Surgery
• Medical Imaging
• Computational Anatomy

Background:

• Minimally invasive pelvic fracture treatment demands precise preoperative planning.
• Current planning relies heavily on surgeon experience, lacking objective tools.
• Complex pelvic anatomy presents challenges for accurate fracture reduction.

Purpose of the Study:

• To develop a virtual reduction algorithm for pelvic fractures using statistical shape models (SSM).
• To enhance the accuracy and applicability of preoperative planning for pelvic fracture surgery.
• To provide surgeons with a reliable tool for virtual surgical planning.

Main Methods:

• Construction of adaptive pelvic statistical shape models (SSM) considering sexual dimorphism.
• Development of an optimization algorithm for iterative fragment pose adjustment and template matching.
• Validation using simulated fractures and clinical data.

Main Results:

• SSM-based reduction achieved high precision in simulated (2.20±1.09 mm, 3.16±1.26°) and clinical (2.78±0.95 mm, 3.10±0.53°) cases.
• The method demonstrated superior accuracy compared to mean shape models.
• Wider applicability than symmetry-based methods was observed.

Conclusions:

• Pelvic digital models derived from SSM exhibit strong generalization capabilities.
• The SSM-based virtual reduction algorithm effectively reconstructs target fracture positions for preoperative planning.
• This method offers a powerful, precise, and broadly applicable tool for surgeons.