Finite element modeling of the cement-bone interface beneath the tibial tray of a total knee arthroplasty: Study of the stress shielding effect (modellazione agli elementi finiti dell'interfaccia cemento-osso sotto il piatto tibiale di protesi totale di ginocchio: Analisi dello stress shielding)

The goal of this thesis was the study of the cement-bone interface in the tibial component of a cemented total knee prosthesis. One of the things you can see in specimens after in vivo service is that resorption of bone occurs in the interdigitated region between bone and cement. A stress shielding effect was investigated as a cause to explain bone resorption. Stress shielding occurs when bone is loaded less than physiological and therefore it starts remodeling according to the new loading conditions. µCT images were used to obtain 3D models of the bone and cement structure and a Finite Element Analysis was used to simulate different kind of loads. Resorption was also simulated by performing erosion operations in the interdigitated bone region. Finally, 4 models were simulated: bone (trabecular), bone with cement, and two models of bone with cement after progressive erosions of the bone. Compression, tension and shear test were simulated for each model in displacement-control until 2% of strain. The results show how the principal strain and Von Mises stress decrease after adding the cement on the structure and after the erosion operations. These results show that a stress shielding effect does occur and rises after resorption starts.

Automatic detection of the inferior alveolar nerve in CT scans with CenterNet

The inferior alveolar nerve (IAN) lies within the mandibular canal, named inferior alveolar canal in literature. The detection of this nerve is important during maxillofacial surgeries or for creating dental implants. The poor quality of cone-beam computed tomography (CBCT) and computed tomography (CT) scans and/or bone gaps within the mandible increase the difficulty of this task, posing a challenge to human experts who are going to manually detect it and resulting in a time-consuming task.Therefore this thesis investigates two methods to automatically detect the IAN: a non-data driven technique and a deep-learning method. The latter tracks the IAN position at each frame leveraging detections obtained with the deep neural network CenterNet, fined-tuned for our task, and temporal and spatial information.