CAVASS: A Computer-Assisted Visualization and Analysis Software System

The Medical Image Processing Group at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open-source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available and open source, and it is integrated with toolkits such as Insight Toolkit and Visualization Toolkit. CAVASS runs on Windows, Unix, Linux, and Mac but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive clusters of work stations for more time-consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3-dimensional and higher-dimensional medical imagery, so support for digital imaging and communication in medicine data and the efficient implementation of algorithms is given paramount importance

Implementation of a sort-last volume rendering using 3D textures

La tesi consiste in una estensione della libreria grafica Aura (licenza gpl, sviluppata dalla Vrije Universiteit di Amsterdam) aggiungendo i componenti necessari ad effettuare un volume rendering distribuito secondo il paradigma sort-last ed usando le texture 3D. Il programma è stato testato su un cluster di 9 PC

Accelerating a 3D finite-difference wave propagation code using GPU graphics cards

International audienceWe accelerate a three-dimensional finite-difference in the time domain (FDTD) wave propagation code by a factor between about 20 and 60 compared to a serial implementation using Graphics Processing Unit (GPU) computing on NVIDIA graphics cards with the CUDA programming language. We describe the implementation of the code in CUDA to simulate the propagation of seismic waves in a heterogeneous elastic medium. We also implement Convolution Perfectly Matched Layers (CPMLs) on the graphics cards to efficiently absorb outgoing waves on the fictitious edges of the grid. We show that the code that runs on a graphics card gives the expected results by comparing our results to those obtained by running the same simulation on a classical processor core. The methodology that we present can be used for Maxwell's equations as well because their form is similar to that of the seismic wave equation written in velocity vector and stress tensor

New Algorithmic Techniques for Large Scale Volumetric Data Visualization on Parallel Architectures

Volume visualization is widely used as an effective approach for the visual exploration, computational analysis, and manipulation of volumetric datasets. Due to the dramatic advances in imaging instruments and computing technologies, such datasets are now appearing at a very fast rate with increasingly larger sizes in many engineering, science and medical applications. Isosurface and direct volume rendering(DVR) are two of the most widely used techniques to render such datasets. This dissertation introduces novel techniques for rendering isosurfaces and volumes, and extends these techniques to multiprocessor architectures. We first focus on cluster-based techniques for isosurface extraction and rendering using polygonal approximation. We present a new simple indexing scheme and data layout approach, which enable scalable and efficient isosurface generation. This algorithm is the first known parallel algorithm to achieve provable load balancing on multiprocessor systems. We also develop an algorithm to generate isosurfaces using ray-casting on multi-core processors. Our method is based on a hybrid strategy that begins with an object order traversal of the data followed by ray-casting on ordered sets of an adaptive number of subcubes, one set for each small group of pixels on the image. We develop a multithreaded implementation, which uses new dynamic load balancing techniques that start with an image partitioning for the initial stage and then perform dynamic allocation of groups of ray-casting tasks among the different threads. The strategy ensures almost equal loads among the cores while maintaining spatial data locality. This scheme is extended to perform direct volume rendering and is shown to achieve similar improvements in terms of overall performance, load balancing, and scalability. We conduct a large number of tests for all our algorithms on the University of Maryland Visualization Cluster and on the 8-core Clovertown platform using a wide variety of datasets such as Richtmyer-Meshkov Instability dataset (7.5GB for each time step) and Visible Human dataset (~1GB). We obtain results that consistently validate the efficiency and the scalability of our algorithms. In particular, the overall performance of our hybrid ray-casting scheme achieves an interactive rendering rate on high resolution (1024x1024) screens for all the datasets tested

On the Real-Time Performance, Robustness and Accuracy of Medical Image Non-Rigid Registration

Three critical issues about medical image non-rigid registration are performance, robustness and accuracy. A registration method, which is capable of responding timely with an accurate alignment, robust against the variation of the image intensity and the missing data, is desirable for its clinical use. This work addresses all three of these issues. Unacceptable execution time of Non-rigid registration (NRR) often presents a major obstacle to its routine clinical use. We present a hybrid data partitioning method to parallelize a NRR method on a cooperative architecture, which enables us to get closer to the goal: accelerating using architecture rather than designing a parallel algorithm from scratch. to further accelerate the performance for the GPU part, a GPU optimization tool is provided to automatically optimize GPU execution configuration.;Missing data and variation of the intensity are two severe challenges for the robustness of the registration method. A novel point-based NRR method is presented to resolve mapping function (deformation field) with the point correspondence missing. The novelty of this method lies in incorporating a finite element biomechanical model into an Expectation and Maximization (EM) framework to resolve the correspondence and mapping function simultaneously. This method is extended to deal with the deformation induced by tumor resection, which imposes another challenge, i.e. incomplete intra-operative MRI. The registration is formulated as a three variable (Correspondence, Deformation Field, and Resection Region) functional minimization problem and resolved by a Nested Expectation and Maximization framework. The experimental results show the effectiveness of this method in correcting the deformation in the vicinity of the tumor. to deal with the variation of the intensity, two different methods are developed depending on the specific application. For the mono-modality registration on delayed enhanced cardiac MRI and cine MRI, a hybrid registration method is designed by unifying both intensity- and feature point-based metrics into one cost function. The experiment on the moving propagation of suspicious myocardial infarction shows effectiveness of this hybrid method. For the multi-modality registration on MRI and CT, a Mutual Information (MI)-based NRR is developed by modeling the underlying deformation as a Free-Form Deformation (FFD). MI is sensitive to the variation of the intensity due to equidistant bins. We overcome this disadvantage by designing a Top-to-Down K-means clustering method to naturally group similar intensities into one bin. The experiment shows this method can increase the accuracy of the MI-based registration.;In image registration, a finite element biomechanical model is usually employed to simulate the underlying movement of the soft tissue. We develop a multi-tissue mesh generation method to build a heterogeneous biomechanical model to realistically simulate the underlying movement of the brain. We focus on the following four critical mesh properties: tissue-dependent resolution, fidelity to tissue boundaries, smoothness of mesh surfaces, and element quality. Each mesh property can be controlled on a tissue level. The experiments on comparing the homogeneous model with the heterogeneous model demonstrate the effectiveness of the heterogeneous model in improving the registration accuracy

Interactive High Performance Volume Rendering

This thesis is about Direct Volume Rendering on high performance computing systems. As direct rendering methods do not create a lower-dimensional geometric representation, the whole scientific dataset must be kept in memory. Thus, this family of algorithms has a tremendous resource demand. Direct Volume Rendering algorithms in general are well suited to be implemented for dedicated graphics hardware. Nevertheless, high performance computing systems often do not provide resources for hardware accelerated rendering, so that the visualization algorithm must be implemented for the available general-purpose hardware. Ever growing datasets that imply copying large amounts of data from the compute system to the workstation of the scientist, and the need to review intermediate simulation results, make porting Direct Volume Rendering to high performance computing systems highly relevant. The contribution of this thesis is twofold. As part of the first contribution, after devising a software architecture for general implementations of Direct Volume Rendering on highly parallel platforms, parallelization issues and implementation details for various modern architectures are discussed. The contribution results in a highly parallel implementation that tackles several platforms. The second contribution is concerned with the display phase of the “Distributed Volume Rendering Pipeline”. Rendering on a high performance computing system typically implies displaying the rendered result at a remote location. This thesis presents a remote rendering technique that is capable of hiding latency and can thus be used in an interactive environment

Parallel Rendering and Large Data Visualization

We are living in the big data age: An ever increasing amount of data is being produced through data acquisition and computer simulations. While large scale analysis and simulations have received significant attention for cloud and high-performance computing, software to efficiently visualise large data sets is struggling to keep up. Visualization has proven to be an efficient tool for understanding data, in particular visual analysis is a powerful tool to gain intuitive insight into the spatial structure and relations of 3D data sets. Large-scale visualization setups are becoming ever more affordable, and high-resolution tiled display walls are in reach even for small institutions. Virtual reality has arrived in the consumer space, making it accessible to a large audience. This thesis addresses these developments by advancing the field of parallel rendering. We formalise the design of system software for large data visualization through parallel rendering, provide a reference implementation of a parallel rendering framework, introduce novel algorithms to accelerate the rendering of large amounts of data, and validate this research and development with new applications for large data visualization. Applications built using our framework enable domain scientists and large data engineers to better extract meaning from their data, making it feasible to explore more data and enabling the use of high-fidelity visualization installations to see more detail of the data.Comment: PhD thesi

HARDWARE-ACCELERATED AUTOMATIC 3D NONRIGID IMAGE REGISTRATION

Software implementations of 3D nonrigid image registration, an essential tool in medical applications like radiotherapies and image-guided surgeries, run excessively slow on traditional computers. These algorithms can be accelerated using hardware methods by exploiting parallelism at different levels in the algorithm. We present here, an implementation of a free-form deformation-based algorithm on a field programmable gate array (FPGA) with a customized, parallel and pipelined architecture. We overcome the performance bottlenecks and gain speedups of up to 40x over traditional computers while achieving accuracies comparable to software implementations. In this work, we also present a method to optimize the deformation field using a gradient descent-based optimization scheme and solve the problem of mesh folding, commonly encountered during registration using free-form deformations, using a set of linear constraints. Finally, we present the use of novel dataflow modeling tools to automatically map registration algorithms to hardware like FPGAs while allowing for dynamic reconfiguration