Real-Time High-Quality Image to Mesh Conversion for Finite Element Simulations

Technological Advances in Medical Imaging have enabled the acquisition of images accurately describing biological tissues. Finite Element (FE) methods on these images provide the means to simulate biological phenomena such as brain shift registration, respiratory organ motion, blood flow pressure in vessels, etc. FE methods require the domain of tissues be discretized by simpler geometric elements, such as triangles in two dimensions, tetrahedra in three, and pentatopes in four. This exact discretization is called a mesh . The accuracy and speed of FE methods depend on the quality and fidelity of the mesh used to describe the biological object. Elements with bad quality introduce numerical errors and slower solver convergence. Also, analysis based on poor fidelity meshes do not yield accurate results specially near the surface. In this dissertation, we present the theory and the implementation of both a sequential and a parallel Delaunay meshing technique for 3D and ---for the first time--- 4D space-time domains. Our method provably guarantees that the mesh is a faithful representation of the multi-tissue domain in topological and geometric sense. Moreover, we show that our method generates graded elements of bounded radius-edge and aspect ratio, which renders our technique suitable for Finite Element analysis. A notable feature of our implementation is speed and scalability. The single-threaded performance of our 3D code is faster than the state of the art open source meshing tools. Experimental evaluation shows a more than 82% weak scaling efficiency for up to 144 cores, reaching a rate of more than 14.3 million elements per second. This is the first 3D parallel Delaunay refinement method to achieve such a performance, on either distributed or shared-memory architectures. Lastly, this dissertation is the first to develop and examine the sequential and parallel high-quality and fidelity meshing of general space-time 4D multi-tissue domains

Toward a real time multi-tissue Adaptive Physics-Based Non-Rigid Registration framework for brain tumor resection

This paper presents an adaptive non-rigid registration method for aligning pre-operative MRI with intra-operative MRI (iMRI) to compensate for brain deformation during brain tumor resection. This method extends a successful existing Physics-Based Non-Rigid Registration (PBNRR) technique implemented in ITKv4.5. The new method relies on a parallel adaptive heterogeneous biomechanical Finite Element (FE) model for tissue/tumor removal depicted in the iMRI. In contrast the existing PBNRR in ITK relies on homogeneous static FE model designed for brain shift only (i.e., it is not designed to handle brain tumor resection). As a result, the new method (1) accurately captures the intra-operative deformations associated with the tissue removal due to tumor resection and (2) reduces the end-to-end execution time to within the time constraints imposed by the neurosurgical procedure. The evaluation of the new method is based on 14 clinical cases with: (i) brain shift only (seven cases), (ii) partial tumor resection (two cases), and (iii) complete tumor resection (five cases). The new adaptive method can reduce the alignment error up to seven and five times compared to a rigid and ITK\u27s PBNRR registration methods, respectively. On average, the alignment error of the new method is reduced by 9.23 and 5.63 mm compared to the alignment error from the rigid and PBNRR method implemented in ITK. Moreover, the total execution time for all the case studies is about 1 min or less in a Linux Dell workstation with 12 Intel Xeon 3.47 GHz CPU cores and 96 GB of RAM

Fully Generalized Two-Dimensional Constrained Delaunay Mesh Refinement

Traditional refinement algorithms insert a Steiner point from a few possible choices at each step. Our algorithm, on the contrary, defines regions from where a Steiner point can be selected and thus inserts a Steiner point among an infinite number of choices. Our algorithm significantly extends existing generalized algorithms by increasing the number and the size of these regions. The lower bound for newly created angles can be arbitrarily close to $30^{\circ}$. Both termination and good grading are guaranteed. It is the first Delaunay refinement algorithm with a $30^{\circ}$ angle bound and with grading guarantees. Experimental evaluation of our algorithm corroborates the theory

A unifying operating platform for 5G end-to-end and multi-layer orchestration

Heterogeneity of current software solutions for 5G is heading for complex and costly situations, with high fragmentation, which in turn creates uncertainty and the risk of delaying 5G innovations. This context motivated the definition of a novel Operating Platform for 5G (5G-OP), a unifying reference functional framework supporting end-to-end and multi-layer orchestration. 5G-OP aims at integrated management, control and orchestration of computing, storage, memory, networking core and edge resources up to the end-user devices and terminals (e.g., robots and smart vehicles). 5G-OP is an overarching architecture, with agnostic interfaces and well-defined abstractions, offering the seamless integration of current and future infrastructure control and orchestration solutions (e.g., OpenDaylight, ONOS, OpenStack, Apache Mesos, OpenSource MANO, Docker, LXC, etc.) The paper provides also the description of a prototype that can be seen as a simplified version of a 5G-OP, whose feasibility has been demonstrated in Focus Group IMT2020 of ITU-T

Designing the 5G network infrastructure: a flexible and reconfigurable architecture based on context and content information

Abstract 5G networks will have to offer extremely high volumes of content, compared to those of today’s. Moreover, they will have to support heterogeneous traffics, including machine-to-machine, generated by a massive volume of Internet-of-Things devices. Traffic demands will be variable in time and space. In this work, we argue that all this can be achieved in a cost-effective way if the network is flexible and reconfigurable. We present the Flex5Gware network architecture, designed to meet the above requirements. Moreover, we discuss the links between flexibility and reconfigurability, on the one side, and context awareness and content awareness, on the other; we show how two of the building blocks of the Flex5Gware architecture, namely on-the-fly MAC reconfiguration and dynamic multi-cell coordinated resource scheduling, can leverage context and content information to make reconfiguration decisions. Our evaluation, conducted by both prototyping and simulation, show that this brings significant benefits in terms of improved coexistence of heterogeneous access networks and reduced latency. We terminate our paper by discussing how the Flex5Gware architecture aims at influencing future system standardization and leveraging the benefits of some key 5G networking enablers described in the paper