Equivariant Spherical CNN for Data Efficient and High-Performance Medical Image Processing

This work highlights the significance of equivariant networks as efficient and high-performance approaches for tomography applications. Our study builds upon the limitations of Convolutional Neural Networks (CNNs), which have shown promise in post-processing various medical imaging systems. However, the efficiency of conventional CNNs heavily relies on an undiminished and proper training set. To tackle this issue, in this study, we introduce an equivariant network, aiming to reduce CNN's dependency on specific training sets. We evaluate the efficacy of equivariant CNNs on spherical signals for tomographic medical imaging problems. Our results demonstrate superior quality and computational efficiency of spherical CNNs (SCNNs) in denoising and reconstructing benchmark problems. Furthermore, we propose a novel approach to employ SCNNs as a complement to conventional image reconstruction tools, enhancing the outcomes while reducing reliance on the training set. Across all cases, we observe a significant decrease in computational costs while maintaining the same or higher quality of image processing using SCNNs compared to CNNs. Additionally, we explore the potential of this network for broader tomography applications, particularly those requiring omnidirectional representation

Advances in Craniofacial Surgery

Calvaria development initiates by growth from primary ossification centers meeting each other to form suture sites. The term craniosynostosis describes premature fusion of one or more of the calvarial sutures. Deformities are usually observable during the first few months of the newborn’s life. The premature fusion of sutures could produce intracranial pressure elevation and consequently lead to abnormal neurocognitive = neurologic development. Patients with craniosynostosis require surgical plans containing multiple surgical staging. In the following chapter, we present our experience in surgical treatment of children with various craniosynostosis syndromes

The performance of a cable-stayed bridge pylon under close-range blast loads

Recent bridge collapses have raised an awareness of, and a concern for, the safety and robustness of bridges subjected to blast loading scenarios. The incident pressure generated by the explosion can cause severe structural damage and a loss of critical structural members, resulting in partial collapse of the bridge. Previously, most relevant research effort has been devoted to understanding the response of buildings under blast loading and to develop guidelines to increase the resistance of such structures, while relatively little research attention has been focused on bridge structures. Recent advancements in numerical methods have enabled the viable and cost-effective simulation of complicated blast scenarios, and hence these methods provide a useful reference for safeguarding design and assessment of critical infrastructure. To reduce the computational costs, previous studies on long span bridges under blast loads typically take advantage of sub-structuring techniques, in which only part of the structure is modelled. However, such oversimplifications can lead to erroneous results. Accordingly, this study is an attempt to simulate the dynamic response of an entire cable-stayed bridge subjected to blast loading based on best practice techniques obtained from the literature. The response of a steel bridge, designed according to the minimum requirements of the Australian Standard AS5100, is investigated when subjected to blast loads ranging from small to large explosions at different positions above the deck using numerical simulations. In addition, the potential effects of blast loads on different structural components (i.e. the deck and pylons) are discussed and possible blast mitigation strategies such as the application of FRP and optimization of the geometry of the pylons are investigated