GPU-Accelerated Visualization of Scattered Point Data

Falch, Thomas L.; Fløystad, Jostein Bø; Breiby, Dag W.; Elster, Anne C.

doi:10.1109/access.2013.2281080

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…While OD theory is an established, thorough and complete theory of Figure 1 shows two-dimensional reconstructions of the diffraction pattern. The main features are consistent with the observations of Jagner et al [2] and Falch et al [11] The major difference is in the diffuse streaks. The streaks of the current pattern have no explicit intensity maxima whereas Jagner et al could assign maxima to either the orthorhombic or monoclinic unit cell.…”

Section: Introductionsupporting

confidence: 91%

Order-Disorder in Diaquobis(salicylato)copper(II) Revisited

Lutz¹,

Batenburg²

2018

Croat. Chem. Acta

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We re-investigated the crystal structure of diaquobis(salicylato)copper(II), CuSal, which is an OD material with disordered layer stacking. The diffraction pattern consists of sharp Bragg spots and diffuse streaks. From the Bragg reflections we determined the ordered part of the structure, which can either be described by an orthorhombic or a twinned monoclinic lattice. The main motif of the structure consists of one-dimensional coordination polymers that are connected by hydrogen bonds into two-dimensional layers. The layer stacking is disordered and from simulations with a Markovian growth models we could fully explain the diffuse streaks of the current structure when the layers are stacked randomly.

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Section: Introductionsupporting

confidence: 91%

Order-Disorder in Diaquobis(salicylato)copper(II) Revisited

Lutz¹,

Batenburg²

2018

Croat. Chem. Acta

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“…In order to facilitate the rotation, the patient can comfortably remain seated or stand on an automatic spinning platform. There are other interesting research works that combine X-ray imaging and GPU processing, like the one discussed in [38].…”

Section: A Rotating Patient One Sensor and Dense Surface Mappingsmentioning

confidence: 99%

Densitometric Radiographic Imaging With Contour Sensors

2019

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We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient's volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient's volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (X). In order to derive this map of lengths (L), the camera equations of the X-ray system and the contour sensor are determined. The patient's surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived L is applied to X as a physical operation (subtraction), obtaining soft tissue-(D S) or bone-enhanced (D B) figures. In the D S type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding X). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed X instances. In the D B type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination. INDEX TERMS Conventional X-ray imaging, contour data, densitometric images, dynamic range, depth information.

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“…One way is to reduce the sampling number directly, like empty-space skipping [18], early ray termination [19]and multiresolution algorithm [20,21], but the texture organization is complicated in multiresolution method, and the multidimensional transfer function design is hard [22], in addition, boundary fusion between different resolutions needs extra processing [23]. Another way is value estimation, for example, Zhang et al [24] presents a strategy using cubic B-spline method to estimate sampling values from neighboring samples with rendering speed 17 to 34 fps, and T. L. Falch et al [25] directly use scattered point data to estimate all the sampling values of volume dataset. Although workload reduction can realize speed-up, but in some cases, insufficient sampling rates may degrade image quality.…”

Section: Introductionmentioning

confidence: 99%

Improving Efficiency for CUDA-Based Volume Rendering by Combining Segmentation and Modified Sampling Strategies

Zhou¹,

Du²,

Zhao³

et al. 2016

International Journal of Simulation: Systems, Science &Amp; Technology

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The objective of this paper is to present a speed-up method to improve the rendering speed of ray casting at the same time obtaining high-quality images. Ray casting is the most commonly used volume rendering algorithm, and suitable for parallel processing. In order to improve the efficiency of parallel processing, the latest platform-Compute Unified Device Architecture (CUDA) is used. The speed-up method uses improved workload allocation and sampling strategies according to CUDA features. To implement this method, the optimal number of segments of each ray is dynamically selected based on the change of the corresponding visual angle, and each segment is processed by a distinct thread processor. In addition, for each segment, we apply different sampling quantity and density according to the distance weight. Rendering speed results show that our method achieves an average 70% improvement in terms of speed, and even 145% increase in some special cases, compared to conventional ray casting on Graphics Processing Unit (GPU). Speed-up ratio shows that this method can effectively improve the factors that influence efficiency of rendering. Excellent rendering performance makes this method contribute to real-time 3-D reconstruction.

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GPU-Accelerated Visualization of Scattered Point Data

Cited by 6 publications

References 44 publications

Order-Disorder in Diaquobis(salicylato)copper(II) Revisited

Order-Disorder in Diaquobis(salicylato)copper(II) Revisited

Densitometric Radiographic Imaging With Contour Sensors

Improving Efficiency for CUDA-Based Volume Rendering by Combining Segmentation and Modified Sampling Strategies

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