Uncluttered Single-Image Visualization of Vascular Structures Using GPU and Integer Programming

Abstract

Direct projection of three-dimensional branching structures, such as networks of cables, blood vessels, or neurons onto a 2D image creates the illusion of intersecting structural parts and creates challenges for understanding and communication. We present a method for visualizing such structures, and demonstrate its utility in visualizing the abdominal aorta and its branches, whose tomographic images might be obtained by computed tomography or magnetic resonance angiography, in a single two-dimensional stylistic image, without overlaps among branches. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. Adopting the integer linear programming-based formulation for the protein structure prediction problem, we tested the proposed technique using 30 visualizations produced from five patient scans with representative anatomical variants in the abdominal aortic vessel tree. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. Comparison of the results with the other optimization technique previously reported elsewhere suggests that, in most aspects, the quality of the visualization is comparable to that of the previous one, with a significant gain in the computation time of the algorithm.

Vessel Maps: A Survey of Map‐Like Visualizations of the Cardiovascular System

“…They can be seen as a treelike system of tubes that quickly becomes complex if multiple branches are considered, resulting in intricate 3D structures. Won et al [WRN06, WRRN09, WJR * 13] propose an uncluttered single‐image visualization of the abdominal aortic tree. They optimize the 3D geometry of a binary tree of depth n in a 2D layout to resolve overlaps.…”

“…To optimize this function, Won et al [WRRN09] first used simulated annealing [KGV83] and later proposed another approach that uses a technique inspired by the protein side‐chain placement problem [LW75, WJR * 13]. Jeon et al [JWY13] also extended the procedure to parallel computing architecture.…”

Visualisation of Control Software for Cyber-Physical Systems

“…An entire area of Computer Science is dedicated to Visualization, that is, to using (often parallel) algorithms to visualize scientific data (see [55] for a survey). Such algorithms are tailored to visualize data coming from given application domains, such as Biology (see, e.g., [56,57]), Medicine (see, e.g., [58][59][60][61]), Mathematics (see, e.g., [62]), and more specifically, for example, in weather forecasting (see, e.g., [63]), in cellular screen visual analysis (see, e.g., [64]) and in taxi trajectories [65]. Such works typically start from a huge amount of available data to be visualized, whilst here our starting point is an OBDD representing a controller.…”

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

“…El registro de imágenes, es una técnica para definir la relación geométrica entre cada punto de dos imágenes, esto es de gran ayuda en las cirugías ayudadas por computadora, pero es indispensable que el tiempo de procesamiento de un valor pequeño, por lo que Kei Ikeda y Fumihiko Ino proponen un método eficiente para acelerar el registro no rígido de información mutua con CUDA (Ikeda, Ino and Hagihara, 2014).Otro tipo de imágenes que es necesario analizar son las visualizaciones tridimensionales de vasos cerebrales, que sirven para diagnosticar enfermedades, sin embargo, cuando se analizan varios vasos cerebrales es difícil decidir el orden de profundidad de manera clara; como solución en (Luo, 2013), (Won et al, 2013), se plantea la combinación de colores a distancia y la mejora de la profundidad estereoscópica se combinan con la reproducción de volúmenes pre integrados, basada en CUDA y su función avanzada de transferencia para una mejor percepción de la profundidad. En (Idzenga et al, 2014) se mencionan las ventajas de utilizar una GPU en la imagenología de ultrasonidos de dos dimensiones, mientras que en (Xanthis et al, 2014), se presenta un simulador (228 veces más rápido que la implementación serial basada en CPU) de imagenología de resonancia magnética (MRI), con ayuda de un ambiente basado en GPU.…”