医学图像大体数据快速体绘制算法研究

为了在保证绘制质量的前提下有效提高大的医学体数据的绘制速度,提出了一种快速的体绘制算法。该算法将大的体数据分割成等大小的数据块,然后通过对每一数据块进行空白数据块的空间跳跃、提前数据块截止和提前光线截止的可见性测试来加快体绘制的速度,最后使用体绘制预积分来提高体绘制的图像质量。实验结果表明,对大的体数据,可以在不损失图像质量的前提下,实现快速的绘制。     

交互式动态体绘制及其加速算法

体绘制三维成象法是一门新兴的3D采样数据场可视化技术，在医学成象和科学可视化领域有着极为广泛的应用，但由于3D数据量大，其使用往往受到巨大计算开销的限制，因此很多研究人员致力于静态体绘制加速算法的研究，并解决医学图象三维可视化中三维体数据显示速度与成象质量问题，因而提出了一种交互式动态体绘制算法，即从任意的视点距离和视线方向进行动态编制，并在分析其算法复杂度的基础上，提出一种新的加速算法，同时使得动态体绘制过程几乎达到实时的效果，经验证，这种算法比标准算法快4～5倍。     

基于PC的医学图像体绘制方法研究与仿真

体绘制技术是一种能够准确反映体数据内部信息的可视化技术。本文主要介绍了一种改进的体绘制算法，即首先对三维医学图像施加模糊增强，然后对增强后的图像进行模糊阈值分割，从而可以清晰地对三维数据场进行分类，即边界区与非边界区。对边界区与非边界区分别进行绘制。在PC机上进行仿真的结果表明，此方法既能提高绘制的速度，又能保证绘制质量。     

基于相邻层间相似性和空体素跳跃的体绘制加速算法研究

Splatting是经典的基于物序的直接体绘制方法,运算数据量的多少制约着算法绘制图像的速度。为了进一步提升绘制速度,采用基于相邻层间相似性和空体素跳跃相结合的方法进行加速,在读取数据过程中对图片中的三维纹理数据进行筛选,并使用足迹表对筛选后的三维纹理数据进行二维投影,利用相邻层间相似性计算每一个点的灰度值,并根据灰度值将数据分类,算出对成像没有影响的空体素,跳过其绘制过程从而加速算法。实验结果显示,该算法能够在保证绘制图像质量的基础上,在一定程度上解决和改善Splatting算法数据的空间相关性和运算效率的问题。     

建立在图像识别基础上的体绘制加速方法

提出了一种新算法——IRVR（Image Recognition Volume Rendering）,该算法能大幅降低冗余数据,从而提升体绘制速度。IRVR算法首先利用交叉熵阈值分割法从三维数据集中将物体像素和背景像素识别出来,然后将迭代光线追踪方法和物体检测采样策略结合起来对原始三维数据集进行采样。接着运用快速迭代法对分类数据集进行采样,从而定位视线与原始数据集的交点。IRVR算法还应用了准确正规采样方法（例如,三线性插值、样条插值等）在体绘制过程中对原始数据集进行插值。经过实验得出的结论证明IRVR算法既能提高体绘制的速度,又可以保证体绘制图像的质量。     

基于PC系统平台实现快速体绘制技术

体绘制技术可将难于理解的三维数据场转化成直观的图像，作为一种最具前景的科学计算可视化技术在医学影像方面有着极强的实用意义。由于体绘制算法需要强大的计算能力，在通常的PC系统上难以实现满足交互式应用的绘制速度，因此阻碍了其普及。研究PC系统上的快速体绘制技术，无疑具有重要的意义．本文使用Shear-warp的体绘制算法，并利用Intel公司的SSE2扩展指令对整个绘制过程进行加速，实现了基于中高档桌面PC系统平台的快速体绘制应用。     

在流水线结构上的基于Shear-Wrap的并行体数据绘制算法

在以前的基于目标空间划分的并行体数据绘制算法中，局部绘制和图象融合是两个串行的过程，在节点机的局部绘制阶段几乎没有数据通讯，但在数据融合阶段数据通讯量非常大，出现总线争用甚至通讯阻塞，而且在这个阶段有非常大的同步开销。本文利用流水线结构，让局部体数据绘制和图象融合并行执行，很好地解决了上述缺点。并在一个基于微机的流水线结构上实现了一个新的基于目标空间划分的并行体数据绘制算法。     

基于稀疏矩阵的并行体绘制算法

直接体绘制是三维数据可视化的重要方法。在实际应用中体数据规模庞大,如何降低计算工作量以获得更高的绘制速度是一个亟待解决的问题。文章针对该问题提出了一种运行于集群系统之上的基于稀疏矩阵的并行Splatting体绘制算法,该算法利用稀疏矩阵对体数据结构进行优化,通过实验获得了令人满意的结果。     

加速透视投影体绘制技术研究

针对可视化中透视体绘制计算量大、耗时较长的不利因素,算法将三维体数据集按照三种主要的观察方向(X,Y,Z)抽取出切片数据,对切片数据进行错切操作,设立一个与切片平行的中间图像平面;在绘制中间图像时使用图形硬件所提供的纹理混和功能;最终图像经过中间图像的变形而得到,从而使绘制速度得到了较大提高。     

光线投射算法中重采样的设计和实现

体绘制技术在医学成像和科学可视化领域有着极为广泛的应用，但由于其巨大的计算开销，限制了其实时动态体绘制的应用，因此许多研究人员致力于静态体绘制加速算法的研究，为了提高体绘制速度。分析了三维规则数据场重采样的原理。光线投射算法中对3D数据场重采样的实现方法；根据具体重建对象，提出了在3D数据场重采样中采用球形包围盒的方法，给出了人体头部和眼球的三维可视化结果，实验表明：这种算法能有效地减少重采样的计算量，并使求交计算更加简单。     

Direct volume rendering of unstructured tetrahedral meshes using CUDA and OpenMP

Direct volume visualization is an important method in many areas, including computational fluid dynamics and medicine. Achieving interactive rates for direct volume rendering of large unstructured volumetric grids is a challenging problem, but parallelizing direct volume rendering algorithms can help achieve this goal. Using Compute Unified Device Architecture (CUDA), we propose a GPU-based volume rendering algorithm that itself is based on a cell projection-based ray-casting algorithm designed for CPU implementations. We also propose a multicore parallelized version of the cell-projection algorithm using OpenMP. In both algorithms, we favor image quality over rendering speed. Our algorithm has a low memory footprint, allowing us to render large datasets. Our algorithm supports progressive rendering. We compared the GPU implementation with the serial and multicore implementations. We observed significant speed-ups that, together with progressive rendering, enables reaching interactive rates for large datasets.     

Progressive volume rendering of large unstructured grids

We describe a new progressive technique that allows real-time rendering of extremely large tetrahedral meshes. Our approach uses a client-server architecture to incrementally stream portions of the mesh from a server to a client which refines the quality of the approximate rendering until it converges to a full quality rendering. The results of previous steps are re-used in each subsequent refinement, thus leading to an efficient rendering. Our novel approach keeps very little geometry on the client and works by refining a set of rendered images at each step. Our interactive representation of the dataset is efficient, light-weight, and high quality. We present a framework for the exploration of large datasets stored on a remote server with a thin client that is capable of rendering and managing full quality volume visualizations.     

Real-time visualization of large volume datasets on standard PC hardware

In medical area, interactive three-dimensional volume visualization of large volume datasets is a challenging task. One of the major challenges in graphics processing unit (GPU)-based volume rendering algorithms is the limited size of texture memory imposed by current GPU architecture. We attempt to overcome this limitation by rendering only visible parts of large CT datasets. In this paper, we present an efficient, high-quality volume rendering algorithm using GPUs for rendering large CT datasets at interactive frame rates on standard PC hardware. We subdivide the volume dataset into uniform sized blocks and take advantage of combinations of early ray termination, empty-space skipping and visibility culling to accelerate the whole rendering process and render visible parts of volume data. We have implemented our volume rendering algorithm for a large volume data of 512 x 304 x 1878 dimensions (visible female), and achieved real-time performance (i.e., 3-4 frames per second) on a Pentium 4 2.4GHz PC equipped with NVIDIA Geforce 6600 graphics card ( 256 MB video memory). This method can be used as a 3D visualization tool of large CT datasets for doctors or radiologists.     

Transform coding for hardware-accelerated volume rendering

Hardware-accelerated volume rendering using the GPU is now the standard approach for real-time volume rendering, although limited graphics memory can present a problem when rendering large volume data sets. Volumetric compression in which the decompression is coupled to rendering has been shown to be an effective solution to this problem; however, most existing techniques were developed in the context of software volume rendering, and all but the simplest approaches are prohibitive in a real-time hardware-accelerated volume rendering context. In this paper we present a novel block-based transform coding scheme designed specifically with real-time volume rendering in mind, such that the decompression is fast without sacrificing compression quality. This is made possible by consolidating the inverse transform with dequantization in such a way as to allow most of the reprojection to be precomputed. Furthermore, we take advantage of the freedom afforded by off-line compression in order to optimize the encoding as much as possible while hiding this complexity from the decoder. In this context we develop a new block classification scheme which allows us to preserve perceptually important features in the compression. The result of this work is an asymmetric transform coding scheme that allows very large volumes to be compressed and then decompressed in real-time while rendering on the GPU.     

Incremental Volume Rendering Using Hierarchical Compression

We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear-Warp Factorisation method which does not require specialised graphics hardware. However the original Shear-Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear-Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. Results from tests using typical volume datasets will be presented which demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements.     

Particle-based multiple irregular volume rendering on CUDA

In this paper, we describe an improved particle-based volume rendering (PBVR) technique for previewing a large irregular volume dataset using the CUDA architecture. This technique allows for opaque and emissive particles to render translucent volumes without visibility sorting. Our GPU acceleration of PBVR provides the multi-volume rendering feature while remaining compatible with both regular and irregular volumes. We also reduce the memory cost required for storing all sub-pixel values by proposing a pixel repetition technique for a large sub-pixel level. By adjusting the repetition level, we achieved a very smooth level of detail (LOD) control for trading quality for speed. Our work demonstrates a full-detail rendering rate from 5 to 10 fps for irregular volume data with mega-scale cell numbers on an NVIDIA GeForce 8800GTS.     

Template-based rendering of run-length- encoded volumes

Template-based volume rendering is a technique to accelerate volume ray casting. It does not trade off image quality for rendering speed. However, it still falls short of interactive manipulation of volume data, mainly owing to the ray-by-ray volume access pattern and the long ray path in the transparent regions. In this paper we present an object-order template-based volume rendering method that uses run-length encoding to enable skipping highly transparent regions. We present three algorithms, one for each principal axis direction. By combining the advantages of object-order volume traversal and run-length encoded volumes, the algorithms achieve high quality rendering in a much shorter time than the original template-based volume rendering. © 1998 John Wiley & Sons, Ltd.     

基于打包索引纹理的大规模数据体绘制算法

针对大规模数据体绘制效率低下的问题,提出一种算法：对体数据进行纹理分块打包,移除空数据块,并创建数据块的索引数据,绘制时通过索引访问打包后的纹理实现大规模数据完全载入显存,同时在索引中标记空数据及高密度数据块的位置,绘制前生成其有效的立方体数据表达,结合早期光线终止与空域跳过等加速技术,有效地实现了大规模的体数据的实时绘制,同时保证了结果图像的质量。     

Interactive volume rendering of large fields

We first present the volume-rendering pipeline and the most typical of the existing methods for each pipeline stage. The complexity of each stage in terms of computing time is analyzed for each method. Then the demands and the scope of interactive volume rendering are briefly summarized. Based on this analysis we examine alternate solutions to optimize each pipeline stage in order to allow interactive visualization while maintaining the image quality. The proposed method maximizes interactive manipulation possibilities and minimizes runtimes by sampling at the Nyquist rate and by flexibly trading off quality for performance at any pipeline level. Our approach is suitable for rendering large, scalar, discrete volume fields such as semitransparent clouds (or X-rays) on the fly.