A geometric network model of intrinsic grey-matter connectivity of the human brain

Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuro- science is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct ‘shortcuts’ through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections

Computational Fluid Dynamics with Embedded Cut Cells on Graphics Hardware

The advent of general purpose computing on graphics cards has led to significant software speedup in many fields. Designing code for GPUs, however, requires careful consideration of the underlying hardware. This thesis explores the implementation of fluid dynamics simulations featuring embedded cut cells using the CUDA programming platform. We demonstrate efficient generation and handling of geometric surface data in rectilinear computational grids. This is added to a split Euler solver to define piecewise linear cut cells describing solid surfaces in fluid flows. To reduce the memory footprint of embedded boundaries, we present a system of compressed data structures. The software is extended to run on multiple graphics cards and shows good scaling. Simulating embedded boundaries requires a description of object surfaces. We implement a fast and robust narrow band signed distance field generator for graphics cards based on the Characteristic/Scan Conversion algorithm for stereolithography files. The thesis presents an augmented approach to handle commonly occurring complex configurations and we show that the method is correct for all closed surfaces. We discuss efficient feature construction and work scheduling and demonstrate high-speed distance generation for complex geometries. At the core of our simulation implementation is a split Euler solver for high-speed flow. We present a one-dimensional method that achieves coalesced memory access and uses shared memory caching to best harness the potential of GPU hardware. Multidimensional simulations use a framework of data transposes to align data with sweep dimensions to maintain optimal memory access. Analysis of the solver shows that compute resources are used efficiently. The solver is extended to include cut cells describing solid boundaries in the domain. We present a compression and mapping method to reduce the memory footprint of the surface information. The cut cell solver is validated with different flow regimes and we simulate shock wave interaction with complex geometries to demonstrate the stability of the implementation. We conclude with multi-card parallelisation and analyse existing literature on domain segmentation and GPU communication. We present a system of domain splitting and message passing with overlapping compute and communication streams. A comparison of naïve and GPU-aware Open MPI shows the benefits of using CUDA specific library calls. The complete software pipeline demonstrates good scaling for up to thirty-two cards on a GPU cluster

A Deep Learning based Fast Signed Distance Map Generation

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency

Efficient discretization of signed distance fields

A Signed distance field (SDF) is an implicit function that returns the distance to the surface of a volume given a point in the space. The sign of the field indicates if the point is inside or outside the volume. These fields are usually used to accelerate computer graphics algorithms in different areas, such as rendering or collision detection. There are many well-defined primitives and operators to model objects using these functions. For example, SDFs allow applying smooth boolean operations between primitives. Applying these operators to triangles meshes can require complex algorithms susceptible to precision problems. Even though SDFs allow modelling objects, they currently are not a used format, and not many modelling tools use it. Most of the time, we want to calculate this field from triangle meshes. If the mesh is two-manifold, the easiest way to calculate the signed distance from a point is by searching for the minimum distance at all the mesh triangles. This strategy requires iterating all the triangles for each query to the signed distance field. There are methods based on different strategies that accelerate this nearest triangle search. If the user does not require getting exact distances to the object, other strategies exist that discretize the space in some fixed sample points. Then, the queries to arbitrary points are calculated using an interpolation of the precalculated discretization. This project presents a new approach based on an octree-like subdivision to accelerate the computation of these signed distance fields queries from a triangle mesh. The main idea is to construct an octree structure in which each leaf will contain only the nearest triangles for all the points in that region. Therefore, when the user wants to calculate the distance from an arbitrary point in the space, it will only compare the triangles influencing that region. Moreover, we present a method to calculate approximated distances based on the discretization approach mentioned before. We designed and developed an octree discretization strategy and explored different interpolation techniques. The distance computation of this discretization is accelerated by the strategy developed in the project

Convolution filtering of continuous signed distance fields for polygonal meshes

Signed distance fields obtained from polygonal meshes are commonly used in various applications. However, they can have C1 discontinuities causing creases to appear when applying operations such as blending or metamorphosis. The focus of this work is to efficiently evaluate the signed distance function and to apply a smoothing filter to it while preserving the shape of the initial mesh. The resulting function is smooth almost everywhere, while preserving the exact shape of the polygonal mesh. Due to its low complexity, the proposed filtering technique remains fast compared to its main alternatives providing C1-continuous distance field approximation. Several applications are presented such as blending, metamorphosis and heterogeneous modelling with polygonal meshes

Implementació en CUDA del Distance Field

El Distance Field en 3D és una representació on, per a un conjunt discret de punts dins d'un volum, es coneix la distància des de cada punt al punt més proper de la superfície de qualsevol dels objectes del domini. Es proposa una implementació per a calcular el Distance Field en 3D basada en l'arquitectura CUDA. En el plantejament proposat, es calcula la distància Euclídea per a cada punt i per totes les cares. El codi està preparat per a calcular el signed distance field però en la implementació final no s'ha inclòs. El signe es determina utilitzant el mètode angleweighted pseudonormals. També es proposa una segona implementació que permet obtenir un rendiment més satisfactori en temps a costa d'obtenir el DF només a les regions properes a la superfície. Finalment es compara el rendiment d'una implementació i l'altra i com varia el rendiment en cada una d'elles a mida que es van aplicant optimitzacions

Screwing assembly oriented interactive model segmentation in HMD VR environment

© 2019 John Wiley & Sons, Ltd. Although different approaches of segmenting and assembling geometric models for 3D printing have been proposed, it is difficult to find any research studies, which investigate model segmentation and assembly in head-mounted display (HMD) virtual reality (VR) environments for 3D printing. In this work, we propose a novel and interactive segmentation method for screwing assembly in the environments to tackle this problem. Our approach divides a large model into semantic parts with a screwing interface for repeated tight assembly. Specifically, after a user places the cutting interface, our algorithm computes the bounding box of the current part automatically for subsequent multicomponent semantic Boolean segmentations. Afterwards, the bolt is positioned with an improved K3M image thinning algorithm and is used for merging paired components with union and subtraction Boolean operations respectively. Moreover, we introduce a swept Boolean-based rotation collision detection and location method to guarantee a collision-free screwing assembly. Experiments show that our approach provides a new interactive multicomponent semantic segmentation tool that supports not only repeated installation and disassembly but also tight and aligned assembly

A Deep Learning based Fast Signed Distance Map Generation

International audienceSigned distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency