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Volume rendering
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<h2 id="scope">Scope</h2> <p>Volume rendering is distinguished from thin slice <a href="/facts/Tomography/HGqzMl2s">tomography</a> presentations, and is also generally distinguished from projections of 3D models, including <a href="/facts/Maximum_intensity_projection/uT3Yvwb6">maximum intensity projection</a>.<a class="footnote-ref" id="fnref:1" href="#fn:1"><sup>1</sup></a> Still, technically, all volume renderings become projections when viewed on a <a href="/facts/Display_device/yX1Mypk0">2-dimensional display</a>, making the distinction between projections and volume renderings a bit vague. Nevertheless, the epitomes of volume rendering models feature a mix of for example coloring<a class="footnote-ref" id="fnref:2" href="#fn:2"><sup>2</sup></a> and shading<a class="footnote-ref" id="fnref:3" href="#fn:3"><sup>3</sup></a> in order to create realistic and/or observable representations. </p> <h2 id="direct-volume-rendering">Direct volume rendering</h2> <p>A direct volume renderer<a class="footnote-ref" id="fnref:4" href="#fn:4"><sup>4</sup></a><a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a> requires every sample value to be mapped to opacity and a color. This is done with a "<a href="/facts/Transfer_function/slDsh1lH">transfer function</a>" which can be a simple ramp, a <a href="/facts/Piecewise_linear_function/6bKlCbEa">piecewise linear function</a> or an arbitrary table. Once converted to an <a href="/facts/RGBA_color_model/gSIu9laE">RGBA color model</a> (for red, green, blue, alpha) value, the composed RGBA result is projected on the corresponding pixel of the frame buffer. The way this is done depends on the rendering technique. </p><p>A combination of these techniques is possible. For instance, a shear warp implementation could use texturing hardware to draw the aligned slices in the <a href="/facts/Off-screen_buffer/kr8I4mm3">off-screen buffer</a>. </p> <h3>Volume ray casting</h3> <p class="note">Main article: <a href="/facts/Volume_ray_casting/bnCjkUUR">Volume ray casting</a></p> <p>The technique of volume ray casting can be derived directly from the <a href="/facts/Rendering_equation/jQHVyQ18">rendering equation</a>. It provides results of very high quality, usually considered to provide the best image quality. Volume ray casting is classified as image based volume rendering technique, as the computation emanates from the output image, not the input volume data as is the case with object based techniques. In this technique, a ray is generated for each desired image pixel. Using a simple camera model, the ray starts at the center of projection of the camera (usually the eye point) and passes through the image pixel on the imaginary image plane floating in between the camera and the volume to be rendered. The ray is clipped by the boundaries of the volume in order to save time. Then the ray is sampled at regular or adaptive intervals throughout the volume. The data is interpolated at each sample point, the transfer function applied to form an RGBA sample, the sample is composited onto the accumulated RGBA of the ray, and the process repeated until the ray exits the volume. The RGBA color is converted to an RGB color and deposited in the corresponding image pixel. The process is repeated for every pixel on the screen to form the completed image. </p> <h3>Splatting</h3> <p class="note">Main article: <a href="/facts/Gaussian_splatting/bcjLger2">Gaussian splatting</a></p> <p>This is a technique which trades quality for speed. Here, every volume element is <a href="/facts/Texture_splatting/TvmQmGSp">splatted</a>, as Lee Westover said, like a snow ball, on to the viewing surface in back to front order. These splats are rendered as disks whose properties (color and transparency) vary diametrically in normal (<a href="/facts/Gaussian_distribution/UapjjPyQ">Gaussian</a>) manner. Flat disks and those with other kinds of property distribution are also used depending on the application.<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a><a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a> </p> <h3>Shear warp</h3> <p>The shear warp approach to volume rendering was developed by Cameron and Undrill, popularized by Philippe Lacroute and <a href="/facts/Marc_Levoy/JJtNgNgr">Marc Levoy</a>.<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a> In this technique, the <a href="/facts/Viewing_transformation/XnF55m31">viewing transformation</a> is transformed such that the nearest face of the volume becomes axis aligned with an off-screen image <a href="/facts/Data_buffer/tVy4j2Dc">data buffer</a> with a fixed scale of voxels to pixels. The volume is then rendered into this buffer using the far more favorable memory alignment and fixed scaling and blending factors. Once all slices of the volume have been rendered, the buffer is then warped into the desired orientation and scaled in the displayed image. </p><p>This technique is relatively fast in software at the cost of less accurate sampling and potentially worse image quality compared to ray casting. There is memory overhead for storing multiple copies of the volume, for the ability to have near axis aligned volumes. This overhead can be mitigated using <a href="/facts/Run_length_encoding/3k6x9afM">run length encoding</a>. </p> <h3>Texture-based volume rendering</h3> <p>Many 3D graphics systems use <a href="/facts/Texture_mapping/9gDTsuXN">texture mapping</a> to apply images, or textures, to geometric objects. Commodity PC <a href="/facts/Graphics_cards/bcKfPyFt">graphics cards</a> are fast at texturing and can efficiently render slices of a 3D volume, with real time interaction capabilities. <a href="/facts/Workstation/h5tK4hTt">Workstation</a> <a href="/facts/GPU/PTK1RQVp">GPUs</a> are even faster, and are the basis for much of the production volume visualization used in <a href="/facts/Medical_imaging/pL6JyJEp">medical imaging</a>, oil and gas, and other markets (2007). In earlier years, dedicated 3D texture mapping systems were used on graphics systems such as <a href="/facts/Silicon_Graphics/DKvkrfLX">Silicon Graphics</a> <a href="/facts/InfiniteReality/B0Fd9B4c">InfiniteReality</a>, <a href="/facts/Hewlett-Packard/abuNSYNu">HP</a> Visualize FX <a href="/facts/Graphics_accelerator/PTK1RQVp">graphics accelerator</a>, and others. This technique was first described by <a href="/facts/Bill_Hibbard/7oIAFsrG">Bill Hibbard</a> and Dave Santek.<a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a> </p><p>These slices can either be aligned with the volume and rendered at an angle to the viewer, or aligned with the viewing plane and sampled from unaligned slices through the volume. Graphics hardware support for 3D textures is needed for the second technique. </p><p>Volume aligned texturing produces images of reasonable quality, though there is often a noticeable transition when the volume is rotated. </p> <h2 id="hardware-accelerated-volume-rendering">Hardware-accelerated volume rendering</h2> <p>Due to the extremely parallel nature of direct volume rendering, special purpose volume rendering hardware was a rich research topic before <a href="/facts/GPU/PTK1RQVp">GPU</a> volume rendering became fast enough. The most widely cited technology was the VolumePro real-time ray-casting system, developed by <a href="/facts/Hanspeter_Pfister/RTocxHKj">Hanspeter Pfister</a> and scientists at <a href="/facts/Mitsubishi_Electric_Research_Laboratories/YkLHE2JP">Mitsubishi Electric Research Laboratories</a>,<a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a> which used high memory bandwidth and brute force to render using the ray casting algorithm. The technology was transferred to TeraRecon, Inc. and two generations of ASICs were produced and sold. The VP1000<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a> was released in 2002 and the VP2000<a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a> in 2007. </p><p><a href="/facts/Pixel_shader/qfhkKFwA">Pixel shaders</a> are able to read and write randomly from video memory and perform some basic mathematical and logical calculations. These <a href="/facts/Single_instruction,_multiple_data/gQHWQSpo">SIMD</a> processors were used to perform general calculations such as rendering polygons and signal processing. In recent <a href="/facts/GPU/PTK1RQVp">GPU</a> generations, the pixel shaders now are able to function as <a href="/facts/Multiple_instruction,_multiple_data/MA3orFRh">MIMD</a> processors (now able to independently branch) utilizing up to 1 GB of texture memory with floating point formats. With such power, virtually any algorithm with steps that can be performed in parallel, such as <a href="/facts/Volume_ray_casting/bnCjkUUR">volume ray casting</a> or <a href="/facts/Tomographic_reconstruction/rk84hL6s">tomographic reconstruction</a>, can be performed with tremendous acceleration. The programmable <a href="/facts/Pixel_shaders/qfhkKFwA">pixel shaders</a> can be used to simulate variations in the characteristics of lighting, shadow, <a href="/facts/Reflection_(computer_graphics)/ThoxJKIy">reflection</a>, emissive color and so forth. Such simulations can be written using high level <a href="/facts/Shading_language/DyFraJM7">shading languages</a>. </p> <h2 id="optimization-techniques">Optimization techniques</h2> <p>The primary goal of optimization is to skip as much of the volume as possible. A typical medical data set can be 1 GB in size. To render that at 30 frame/s requires an extremely fast memory bus. Skipping voxels means less information needs to be processed. </p> <h3>Empty space skipping</h3> <p>Often, a volume rendering system will have a system for identifying regions of the volume containing no visible material. This information can be used to avoid rendering these transparent regions.<a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a> </p> <h3>Early ray termination</h3> <p>This is a technique used when the volume is rendered in front to back order. For a ray through a pixel, once sufficient dense material has been encountered, further samples will make no significant contribution to the pixel and so may be neglected. </p> <h3>Octree and BSP space subdivision</h3> <p>The use of hierarchical structures such as <a href="/facts/Octree/hNT74wvT">octree</a> and <a href="/facts/Binary_space_partitioning/sn9MhTSB">BSP</a>-tree could be very helpful for both compression of volume data and speed optimization of volumetric ray casting process. </p> <h3>Volume segmentation</h3> <p><a href="/facts/Image_segmentation/jofAhbxa">Image segmentation</a> is a manual or automatic procedure that can be used to section out large portions of the volume that one considers uninteresting before rendering, the amount of calculations that have to be made by ray casting or texture blending can be significantly reduced. This reduction can be as much as from O(n) to O(log n) for n sequentially indexed voxels. Volume segmentation also has significant performance benefits for other <a href="/facts/Ray_tracing_(graphics)/n6qpap2q">ray tracing</a> algorithms. Volume segmentation can subsequently be used to highlight or expose<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a> structures of interest. </p> <h3>Multiple and adaptive resolution representation</h3> <p>By representing less interesting regions of the volume in a coarser resolution, the data input overhead can be reduced. On closer observation, the data in these regions can be populated either by reading from memory or disk, or by <a href="/facts/Interpolation/8PyetX4f">interpolation</a>. The coarser resolution volume is resampled to a smaller size in the same way as a 2D mipmap image is created from the original. These smaller volume are also used by themselves while rotating the volume to a new orientation. </p> <h3>Pre-integrated volume rendering</h3> <p>Pre-integrated volume rendering<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a> is a method that can reduce sampling artifacts by pre-computing much of the required data. It is especially useful in hardware-accelerated applications<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a><a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a> because it improves quality without a large performance impact. Unlike most other optimizations, this does not skip voxels. Rather it reduces the number of samples needed to accurately display a region of voxels. The idea is to render the intervals between the samples instead of the samples themselves. This technique captures rapidly changing material, for example the transition from muscle to bone with much less computation. </p> <h3>Image-based meshing</h3> <p><a href="/facts/Image-based_meshing/W2zuG9CM">Image-based meshing</a> is the automated process of creating computer models from 3D image data (such as <a href="/facts/MRI/Vh9yG9qq">MRI</a>, <a href="/facts/X-ray_computed_tomography/NusPJ2S8">CT</a>, <a href="/facts/Industrial_CT_scanning/TnvAI9GL">Industrial CT</a> or <a href="/facts/Microtomography/1KwzUjoK">microtomography</a>) for computational analysis and design, e.g. CAD, CFD, and FEA. </p> <h3>Temporal reuse of voxels</h3> <p>For a complete display view, only one voxel per pixel (the front one) is required to be shown (although more can be used for smoothing the image), if animation is needed, the front voxels to be shown can be cached and their location relative to the camera can be recalculated as it moves. Where display voxels become too far apart to cover all the pixels, new front voxels can be found by ray casting or similar, and where two voxels are in one pixel, the front one can be kept. </p> <h2 id="list-of-related-software">List of related software</h2> Open source <ul><li><a href="/facts/3D_Slicer/b9YfcRKg">3D Slicer</a> – a software package for scientific visualization and image analysis</li> <li><a href="/facts/ClearVolume/sq8chQQN">ClearVolume</a> – a GPU ray-casting based, live 3D visualization library designed for high-end volumetric light sheet microscopes.</li> <li><a href="/facts/ParaView/YCvNbd0l">ParaView</a> – a cross-platform, large data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. ParaView is built on VTK (below).</li> <li><a href="/facts/Studierfenster/V0GznbgG">Studierfenster (StudierFenster)</a> – a free, non-commercial Open Science client/server-based Medical Imaging Processing (MIP) online framework.</li> <li><a href="/facts/Vaa3D/2nWrS4Tv">Vaa3D</a> – a 3D, 4D and 5D volume rendering and image analysis platform for gigabytes and terabytes of large images (based on OpenGL) especially in the microscopy image field. Also cross-platform with Mac, Windows, and Linux versions. Include a comprehensive plugin interface and 100 plugins for image analysis. Also render multiple types of surface objects.</li> <li><a href="/facts/VisIt/XUppj0yN">VisIt</a> – a cross-platform interactive parallel visualization and graphical analysis tool for viewing scientific data.</li> <li><a href="/facts/Volume_cartography/7kmjkYaM">Volume cartography</a> – an open source software used in recovering the <a href="/facts/En-Gedi_Scroll/2aVX1YuL">En-Gedi Scroll</a>.</li> <li><a href="/facts/Voreen/udhLagDI">Voreen</a> – a cross-platform rapid application development framework for the interactive visualization and analysis of multi-modal volumetric data sets. It provides GPU-based volume rendering and data analysis techniques</li> <li><a href="/facts/VTK/SXTm1vTl">VTK</a> – a general-purpose C++ toolkit for data processing, visualization, 3D interaction, computational geometry, with Python and Java bindings. Also, VTK.js provides a JavaScript implementation.</li></ul> Commercial <ul><li><a href="/facts/Amira_(Software)/CXKNoc5g">Amira</a> – a 3D visualization and analysis software for scientists and researchers (in life sciences and biomedical)</li> <li><a href="/facts/Imaris/AbDoINB9">Imaris</a> – a scientific software module that delivers all the necessary functionality for data management, visualization, analysis, segmentation and interpretation of 3D and 4D microscopy datasets</li> <li><a href="/facts/MeVisLab/B1DYGP8G">MeVisLab</a> – cross-platform software for medical image processing and visualization (based on OpenGL and Open Inventor)</li> <li><a href="/facts/Open_Inventor/Y31WVo5E">Open Inventor</a> – a high-level 3D API for 3D graphics software development (C++, .NET, Java)</li> <li><a href="/facts/ScanIP/PaQauxHw">ScanIP</a> – an image processing and <a href="/facts/Image-based_meshing/W2zuG9CM">image-based meshing</a> platform that can render scan data (MRI, CT, Micro-CT...) in 3D directly after import.</li></ul> <ul><li><a href="/facts/Tomviz/1IgtvtaK">tomviz</a> – a 3D visualization platform for scientists and researchers that can utilize Python scripts for advanced 3D data processing.</li> <li><a href="/facts/VoluMedic/KwQCGnuo">VoluMedic</a> – a volume slicing and rendering software</li></ul> <h2 id="see-also">See also</h2> Wikimedia Commons has media related to Volume rendering. <ul><li>Cinematic rendering, an alternative visualization technique</li> <li><a href="/facts/Isosurface/kvMHSemB">Isosurface</a>, a surface that represents points of a constant value (e.g. pressure, temperature, velocity, density) within a volume of space</li> <li><a href="/facts/Flow_visualization/j4WuQdFC">Flow visualization</a>, a technique for the visualization of <a href="/facts/Vector_field/ccFNuPPp">vector fields</a></li> <li><a href="/facts/Volume_mesh/4Y171FIK">Volume mesh</a>, a polygonal representation of the interior volume of an object</li></ul> <h2 id="further-reading">Further reading</h2> <ul><li>M. Ikits, J. Kniss, A. Lefohn and C. Hansen: <a href="https://developer.nvidia.com/gpugems/gpugems/part-vi-beyond-triangles/chapter-39-volume-rendering-techniques"><i>Volume Rendering Techniques</i></a>. In: <i>GPU Gems</i>, Chapter 39 (online-version in the developer zone of Nvidia).</li> <li><a href="http://www.byclb.com/TR/Tutorials/volume_rendering/Index.aspx">Volume Rendering</a>, Volume Rendering Basics Tutorial by Ph.D. Ömer Cengiz ÇELEBİ</li> <li>Barthold Lichtenbelt, Randy Crane, Shaz Naqvi, <i>Introduction to Volume Rendering</i> (Hewlett-Packard Professional Books), Hewlett-Packard Company 1998.</li> <li>Peng H., Ruan, Z, Long, F, Simpson, JH, Myers, EW: <i>V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets.</i> Nature Biotechnology, 2010 <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fnbt.1612">10.1038/nbt.1612</a> <a href="http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.1612.html">Volume Rendering of large high-dimensional image data</a>.</li> <li>Daniel Weiskopf (2006). <i>GPU-Based Interactive Visualization Techniques</i>. Springer Science & Business Media. <a href="/facts/ISBN_(identifier)/15AdSPa9">ISBN</a> 978-3-540-33263-3.</li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Fishman, Elliot K.; Ney, Derek R.; Heath, David G.; Corl, Frank M.; Horton, Karen M.; Johnson, Pamela T. (2006). "Volume Rendering versus Maximum Intensity Projection in CT Angiography: What Works Best, When, and Why". RadioGraphics. 26 (3): 905–922. doi:10.1148/rg.263055186. ISSN 0271-5333. PMID 16702462. <a href="/wiki/Elliot_K._Fishman" target="_blank">/wiki/Elliot_K._Fishman</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Silverstein, Jonathan C.; Parsad, Nigel M.; Tsirline, Victor (2008). "Automatic perceptual color map generation for realistic volume visualization". Journal of Biomedical Informatics. 41 (6): 927–935. doi:10.1016/j.jbi.2008.02.008. ISSN 1532-0464. PMC 2651027. PMID 18430609. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2651027" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2651027</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Page 185 in Leif Kobbelt (2006). Vision, Modeling, and Visualization 2006: Proceedings, November 22-24. IOS Press. ISBN 9783898380812. <a href="9783898380812" target="_blank">9783898380812</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>Marc Levoy, "Display of Surfaces from Volume Data", IEEE CG&A, May 1988. Archive of Paper <a href="/wiki/IEEE" target="_blank">/wiki/IEEE</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Drebin, Robert A.; Carpenter, Loren; Hanrahan, Pat (1988). "Volume rendering". ACM SIGGRAPH Computer Graphics. 22 (4): 65–74. doi:10.1145/378456.378484. Drebin, Robert A.; Carpenter, Loren; Hanrahan, Pat (1988). 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