Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures

Smelyanskiy, Mikhail; Holmes, David W.; Chhugani, Jatin; Larson, Alan G.; Carmean, Doug; Hanson, Dennis P.; Dubey, Pradeep; Augustine, Kurt E.; Kim, D.; Kyker, A.; Lee, V.W.; Nguyen, Anthony D.; Seiler, Larry; Robb, Richard A.

doi:10.1109/tvcg.2009.164

Cited by 70 publications

(35 citation statements)

References 26 publications

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“…The quality of volume rendering has always been of central interest to the community, and relying on visual inspection is a common practice. Meissner et al [26] evaluate volume rendering techniques using the human visual system as a reference while, more recently, Smelyanskiy et al [40] present a domain expert guided comparison scheme. While those approaches are valuable, the need for a more systematic evaluation is discussed in several papers [11], [15], [16], [18].…”

Section: Related Workmentioning

confidence: 99%

“…In visualization, expert analysis and error quantification are, to the best of our knowledge, the only two verification tools previously employed for verification of volume rendering techniques [26], [28], [40]. Whereas it is easy to envision situations where an expert may fail to predict a code mistake, it is more difficult to see when error quantification fails.…”

Section: Verificationmentioning

confidence: 99%

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Verifying Volume Rendering Using Discretization Error Analysis

Etiene

Jonsson

Ropinski

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Abstract-We propose an approach for verification of volume rendering correctness based on an analysis of the volume rendering integral, the basis of most DVR algorithms. With respect to the most common discretization of this continuous model (Riemann summation), we make assumptions about the impact of parameter changes on the rendered results and derive convergence curves describing the expected behavior. Specifically, we progressively refine the number of samples along the ray, the grid size, and the pixel size, and evaluate how the errors observed during refinement compare against the expected approximation errors. We derive the theoretical foundations of our verification approach, explain how to realize it in practice and discuss its limitations. We also report the errors identified by our approach when applied to two publicly-available volume rendering packages.

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Section: Related Workmentioning

confidence: 99%

Section: Verificationmentioning

confidence: 99%

Verifying Volume Rendering Using Discretization Error Analysis

Etiene

Jonsson

Ropinski

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…Given the fact, that ray-casting can be considered as the volume rendering technique producing the highest image quality [37], it is remarkable that only a few techniques for interactive advanced volume illumination have focused on this paradigm. Rezk-Salama [27] has proposed a fast Monte-Carlo-based algorithm which incorporates shadowing and scattering effects into a GPU-based volume ray-caster.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, the preprocessing techniques are not applicable to large volumetric data sets since the precomputed illumination volume would consume too much extra graphics memory. While this is not a drawback of the slice-based approaches, they have the downside that they are tightly bound to the slice-based rendering paradigm, which is known to result in inferior image quality as compared to ray-casting based techniques [37]. To achieve high quality results with slice-based rendering, a rather large number of slices is necessary which directly impacts rendering performance.…”

Section: Introductionmentioning

confidence: 99%

Image Plane Sweep Volume Illumination

Sundén

Ynnerman

Ropinski

2011

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. A CT scan of a penguin (512 × 512 × 1146 voxels) rendered using image plane sweep volume illumination. Local and global illumination effects are integrated, yielding in high quality visualizations of opaque as well as transparent materials.Abstract-In recent years, many volumetric illumination models have been proposed, which have the potential to simulate advanced lighting effects and thus support improved image comprehension. Although volume ray-casting is widely accepted as the volume rendering technique which achieves the highest image quality, so far no volumetric illumination algorithm has been designed to be directly incorporated into the ray-casting process. In this paper we propose image plane sweep volume illumination (IPSVI), which allows the integration of advanced illumination effects into a GPU-based volume ray-caster by exploiting the plane sweep paradigm. Thus, we are able to reduce the problem complexity and achieve interactive frame rates, while supporting scattering as well as shadowing. Since all illumination computations are performed directly within a single rendering pass, IPSVI does not require any preprocessing nor does it need to store intermediate results within an illumination volume. It therefore has a significantly lower memory footprint than other techniques. This makes IPSVI directly applicable to large data sets. Furthermore, the integration into a GPU-based ray-caster allows for high image quality as well as improved rendering performance by exploiting early ray termination. This paper discusses the theory behind IPSVI, describes its implementation, demonstrates its visual results and provides performance measurements.

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“…In [1], comparison of performance on CPU, FPGA and GPU is done using some image processing applications. And in [4], performance analysis of CPU and GPU is performed on some medical image volume rendering application. In this paper, we compare the performance of GPU and CPU (single-core and multi-core) using a NLP application.…”

Section: Introductionmentioning

confidence: 99%

Generating Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications

Gupta¹,

Babu²

2011

IJACSA

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Abstract-In Natural Language Processing (NLP) applications, the main time-consuming process is string matching due to the large size of lexicon. In string matching processes, data dependence is minimal and hence it is ideal for parallelization. A dedicated system with memory interleaving and parallel processing techniques for string matching can reduce this burden of host CPU, thereby making the system more suitable for real-time applications. Now it is possible to apply parallelism using multi-cores on CPU, though they need to be used explicitly to achieve high performance. Recent GPUs hold a large number of cores, and have a potential for high performance in many general purpose applications. Programming tools for multi-cores on CPU and a large number of cores on GPU have been formulated, but it is still difficult to achieve high performance on these platforms. In this paper, we compare the performance of single-core, multi-core CPU and GPU using such a Natural Language Processing application.

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Mapping High-Fidelity Volume Rendering for Medical Imaging to CPU, GPU and Many-Core Architectures

Cited by 70 publications

References 26 publications

Verifying Volume Rendering Using Discretization Error Analysis

Verifying Volume Rendering Using Discretization Error Analysis

Image Plane Sweep Volume Illumination

Generating Performance Analysis of GPU compared to Single-core and Multi-core CPU for Natural Language Applications

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