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, we exploit a multi-disciplinary strategy based on synchrotron scans in combination with micro-mechanical tests. This allows to offer a fracture mechanics-based approach for quantifying the critical stress intensity factor 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 as a fundamental aspect for fracture prevention.
Il drammatico aumento delle fratture ossee da fragilità e l'onere economico e sanitario ad esse correlato rendono necessario un nuovo approccio per prevenire fratture catastrofiche. Studi recenti affrontano la questione da una prospettiva multiscala, elevando i fenomeni alla microscala come la chiave per una prevenzione efficace e precoce. Tuttavia, sorgono diverse limitazioni, in particolare per una valutazione quantitativa dell'effetto delle micro-porosità lacunari sulle fasi di inizio e propagazione delle fratture. Inoltre, la necessità di immagini ad alta risoluzione impone dispendiose fasi di post-elaborazione. In questo contesto, si sfrutta una strategia multidisciplinare basata su scansioni al sincrotrone in combinazione con test micro-meccanici. Ciò consente di offrire un approccio basato sulla meccanica della frattura per quantificare il fattore di intensificazione degli sforzi nelle ossa umane trabecolari in condizioni fisio-patologiche. A questo si affianca un quadro morfologico e densitometrico per cogliere le differenze della rete lacunare in presenza di alterazioni patologiche. Per ovviare alle attuali fasi di segmentazione manuale/semi-automatica, lunghe e computazionalmente onerose, si è implementata una rete neurale convoluzionale che rileva l'inizio e la propagazione dei danni alla microscala. I risultati evidenziano l'intima interazione tra i fenomeni tenacizzanti ed infragilenti alla microscala come aspetto fondamentale per la prevenzione delle fratture.
Image-guided computational and experimental analysis of fractured patients (GAP)
Buccino, Federica
2021/2022
Abstract
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, we exploit a multi-disciplinary strategy based on synchrotron scans in combination with micro-mechanical tests. This allows to offer a fracture mechanics-based approach for quantifying the critical stress intensity factor 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 as a fundamental aspect for fracture prevention.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/190575