Advanced Materials and Smart Structures
Supervisor: Prof. Laura Vergani
Tutor: Prof. Gaetano Cascini
University of Origin: Politecnico di Milano - Biomedical Engineering
The dramatic increase in fragility fractures and the related health and economic burden rise the urge of a cutting-edge attitude to anticipate catastrophic fracture propagation in human bones.
Recent studies address the issue from a multi-scale perspective, elevating the micro-scale phenomena as the key for detecting early damage occurrence. However, several limitations arise specifically for defining a quantitative framework to assess the contribution of lacunar micro-pores to fracture initiation and propagation. Moreover, the need for high resolution imaging imposes time demanding post-processing phases.
In this context, GAP exploits synchrotron scans in combination with micro-mechanical tests, to offer a fracture mechanics-based approach for quantifying the critical stress intensification in healthy and diseased trabecular human bones. This is paired with a morphological and densitometric framework for capturing lacunar network differences in presence of pathological alterations To address the current time-consuming and computationally expensive manual/semi-automatic segmenting steps, we implement convolutional neural network to detect the initiation and propagation of micro-scale damages. The results highlight the intimate cross talks between toughening and weakening phenomena at micro-scale, paving the way for novel preventive strategies and patient-specific treatments.
See Figure 2.
See Figure 2.
- Mapping local mechanical properties of human healthy and osteoporotic femoral heads (Fig. 3)
- Clarifying the role of lacunae in damage initiation and progression (Fig. 4)
- Localizing bone failure bands via validated numerical models (Fig. 5)