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preface

With rapid developments in the field of 3D data acquisition and advanced visualization performed on modern workstations with interactive frame rates, volume data will become increasingly important.

Volume data sets can be captured by techniques such as MRI, CT, PET, USCT or echolocation, or generated by physical simulations (fluid dynamics or particle systems). Volume Data Rendering: The core breakthrough of version 8 is its high-performance volume rendering extension, which is able to interact with other 3D objects in the graph library. This data information has been widely used for ×× detection, aneurysm visualization and treatment planning. The data is useful for non-destructive material testing using computed tomography or ultrasound.

In addition, huge 3D data sets derived from seismic studies can also be visualized through our volume rendering engine. More powerful and flexible functionality for both finance and science: New features for finance and science applications include multiple legends with new auto-positioning options and exclusion of inactive trading periods from date ranges. More powerful heat maps and spectrograms improve the efficiency of advanced scientific applications.

Volume Data Overview

Volume data consists of voxels. A voxel is a basic volume element, which can also be understood as a point or a small area with arrangement and color in three-dimensional space, which is why it is possible to keep up to six scalar parameters.

Usually voxels belong to a fixed grid, so volume data can be stored as tables. In this case, the run can be thought of as a multidimensional array, and the volume data can be thought of as a locally stored *.csv file. More commonly, the dataset is divided into several slices and each slice is stored as a bitmap image, while complex compression algorithms can be applied to the image to reduce the model size.

Volume data visualization

Visualized volume data includes four main algorithms. The following will discuss the characteristics of various algorithms and techniques and the existing problems.

The most straightforward solution is based on the slice approach, which means giving each volume data slice a separate visualization opportunity for scrolling interaction.

The advantage of this technique lies in simple operation and less complex calculation. The downside is that the visualizer needs to imagine reconstructing the entire object structure. Therefore, slice-based methods are not the best choice for analyzing extremely complex and ambiguous structures. However, this method is well suited to visualizing the interior of known objects, such as the internal structure of the human body. This is also why this method is widely used in the medical industry. It is most commonly used for MRI and CT. It is worth noting that general CT and MRI studies have relatively low resolution in one dimension, which leads to some difficulties in utilizing datasets with more advanced technologies.

Other technical simulations

This method is very suitable for experts familiar with certain technology visual analysis applications. For example, in the development of new technologies for the medical and seismic industries, experts can smoothly transition from old technology solutions to modern technologies. This approach is not often adopted for the following reasons: First, it requires the use of very detailed volumetric data sets, while other essential information may be lost or corrupted by mimicking another technique. As a result, the popularity of visualization will diminish as new technologies are integrated into expert workflows. Second, this type of visualization development takes a lot of time to get close to visualizing the initial image, after which some images will be discarded for use. Another problem is that it takes someone with some technical experience to interpret the results correctly.

volume rendering

3D rendering refers to visualizing 3D objects with 2D images. The most commonly used 3D rendering is realistic image visualization based on polygonal mesh surfaces. This technology is widely used because modern graphics architectures speed up application operations.

indirect volume rendering

Indirect Volume Rendering There are several tools available for polygon mesh models. This method consists of two stages, the first stage is to extract isosurfaces from the dataset based on a specific threshold, and there are several algorithms that can perform this task (the most popular is Marching Cubes ). Sometimes isosurface extraction can be improved by developing special algorithms based on specific features of a particular dataset. Then visualize the polygonal surface model with a 3D graphics engine or other tool, such as the mesh model of LightningChart, which is very suitable for this method.

The main advantage of this method is based on exploiting the advantages of old technology. It contains all the typical features of 3D object visualization, such as rotation, use of different numbers of light sources, interaction with other 3D objects, etc. Therefore, it makes complex 3D structure analysis simpler. It is particularly useful for visual detection of important details in unknown datasets. Performance optimization of the 3D rendering engine allows any modern computer to visualize data. In addition, the technique allows developers to use more sophisticated noise reduction algorithms.

The visualization program of the first stage of this method has certain defects. Because converting data from polygon volume datasets results in the loss of internally unwanted data. Isoplanar extraction algorithms can require complex calculations, i.e. preprocessing takes a lot of time, which is often why it is not possible to interactively change the threshold for surface extraction.

direct volume rendering

Direct volume rendering does not require preprocessing. Observing the data directly from the original dataset provides the algorithm with the opportunity to dynamically modify transfer functions and thresholds. And there are methods that allow visualization of the internal structure of a dataset in a translucent way.

Direct volume rendering is currently the most powerful method for visualizing data. Visualizations have all the advantages of a polygonal mesh model and can be easily bound in the same scene. In addition, you can cut a portion of the model to see structures hidden by the object's surface.

The high configuration hardware requirements are the downside of this approach, but with the constant optimization of modern graphics cards, visualizations can be run on even the cheapest hardware. Another drawback is the high development cost of the volume rendering engine.

There are several different techniques for implementing direct volume rendering. The most common is render in your own way with tools created for GPU acceleration of polygon mesh models. Texture-based volume rendering and volume ray casting are the most successful direct volume rendering methods.

Texture-based volume rendering uses a series of planes to construct objects. The dataset is projected onto a plane as a texture. The final shape consists of alpha particles in the blending plane. Volume Ray Casting uses cubes as placeholder nodes in the volume model. The model itself is projected onto both sides of the cube by a ray casting algorithm that uses rays to accumulate data and synthesize it with specific equations called Ray Functions.

Ray functionality is a truly fascinating feature of ray casting algorithms. It allows setting how rays perform data sampling and pixel color calculations. Different ray functions can extract different features from the data. Let's discuss the following three examples of ray functions:

The cumulative feature attempts to collect and combine as much data as possible, giving viewers the opportunity to explore the internal structure of an object. A visualization using this technique looks like a translucent gel. Advances in Volume Data Visualization

The Maximum Intensity function displays only the brightest values sampled by the ray. The visual effect is similar to that of X-ray images. This feature allows you to obtain additional information about the internal structure of an object. Advances in Volume Data Visualization

The isosurface rendering model surface looks like a polygon model rendering. The final result is very similar to that of indirect volume rendering.

conclusion

Hardware development prepares the ground for increased interest in different data acquisition technologies. Improvements in consumer computer performance will have a positive impact on the popularity of advanced volume visualization techniques such as direct and indirect volume rendering.

LightningChart has an excellent tool for fragment and indirect volume rendering based visualization of volume data. In addition, LightningChart's direct volume rendering engine provides many advanced features for data visualization.

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