The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs
Open accessAbstract
Expectation Maximization (EM) and the Simultaneous Iterative Reconstruction Technique (SIRT) are two iterative computed tomography reconstruction algorithms often used when the data contain a high amount of statistical noise, have been acquired from a limited angular range, or have a limited number of views. A popular mechanism to increase the rate of convergence of these types of algorithms has been to perform the correctional updates within subsets of the projection data. This has given rise to the method of Ordered Subsets EM (OS-EM) and the Simultaneous Algebraic Reconstruction Technique (SART). Commodity graphics hardware (GPUs) has shown great promise to combat the high computational demands incurred by iterative reconstruction algorithms. However, we find that the special architecture and programming model of GPUs add extra constraints on the real-time performance of ordered subsets algorithms, counteracting the speedup benefits of smaller subsets observed on CPUs. This gives rise to new relationships governing the optimal number of subsets as well as relaxation factor settings for obtaining the smallest wall-clock time for reconstruction-a factor that is likely application-dependent. In this paper we study the generalization of SIRT into Ordered Subsets SIRT and show that this allows one to optimize the computational performance of GPU-accelerated iterative algebraic reconstruction methods.
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Xu ve diğerleri [26] 2010 yılında 3B elektron tomografi iteratif geriçatma algoritmasını CUDA platformunu kullanarak hızlandırmışlardır.
“Xu ve diğerleri [26] 2010 yılında 3B elektron tomografi iteratif geriçatma algoritmasını CUDA platformunu kullanarak hızlandırmışlardır.”
From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [26]
Section 2.2 (page unavailable) explicitly says the reported results were obtained with GLSL shaders, not CUDA. The Conclusions (page unavailable) say the authors planned to investigate CUDA in future work. The paper studies GPU-accelerated iterative CT reconstruction, but the cited claim incorrectly describes it as a CUDA acceleration of 3D electron tomography. (LLM, conf=0.93)
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Experimental — accuracy not yet measuredPalenstijn ve diğerleri [28] 2011 yılında Xu ve diğerlerinin [26] çalışmasını iyileştirmiş ve thread bloklarının ve belleğin daha verimli kullanılması sayesinde 10 kata varan hızlanma elde etmiştir.
“Çalışmada thread bloklarının ve belleğin daha verimli kullanılması sayesinde 10 kata varan hızlanma elde edilmiştir.”
From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [26]
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Citations
Cites
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Self-citationUnmatched projector/backprojector pairs in an iterative reconstruction algorithm
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Self-citationA Survey of General-Purpose Computation on Graphics Hardware
Owens, J. D., Luebke, D. P., Govindaraju, N. K., et al. (2005) · eScholarship (California Digital Library) · doi:10.2312/egst.20051043 · Cited by 149
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Self-citationMR image reconstruction using the GPU
Schiwietz, T., Chang, T., Speier, P., & Westermann, R. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.652223 · Cited by 61
On Block-Iterative Entropy Maximization
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Xu, W., Xu, F., Jones, M., Keszthelyi, B., Sedat, J., Agard, D., & Mueller, K. (2010) · Journal of Structural Biology · doi:10.1016/j.jsb.2010.03.018 · Cited by 71
Self-citationA distributed multi-GPU system for high speed electron microscopic tomographic reconstruction
Zheng, S. Q., Branlund, E., Kesthelyi, B., Braunfeld, M. B., Cheng, Y., Sedat, J. W., & Agard, D. A. (2011) · Ultramicroscopy · doi:10.1016/j.ultramic.2011.03.015 · Cited by 13
Self-citationGPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi
AYDIN, S., SAMET, R., & BAY, Ö. F. (2020) · Politeknik Dergisi · doi:10.2339/politeknik.563767 · Cited by 3
“compensation using graphics processing units (GPUs)”, Journal of Structural Biology, 171: 142–153, (2010) [27] Xu F.”
Method
Metadata
- Authors
- Xu, F., Xu, W., Jones, M., Keszthelyi, B., Sedat, J., Agard, D., & Mueller, K.
- Affiliations
- Stony Brook University (US)
- Howard Hughes Medical Institute (US)
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2010-06
- Volume
- 98
- Issue
- 3
- Pages
- 261-270
- ISSN
- 0169-2607 (ISSN-L)
- PMC
- PMC2871068
- OpenAlex
- W2103616940
- Cited by
- 63
- References
- 18
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 1 · 2022: 2 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 3 · 2016: 7 · 2015: 4 · 2014: 5 · 2013: 8 · 2012: 4
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- Howard Hughes Medical Institute
- National Institutes of Health (5r21eb004099-02)
- National Institute of General Medical Sciences (GM 31627, R01GM031627)
- National Institute of Biomedical Imaging and Bioengineering (R21 EB004099, R21 EB004099-01)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- ordered subsets, iterative reconstruction, GPU acceleration, computed tomography, SIRT, SART, expectation-maximization algorithm, relaxation parameter, real-time performance, parallel computing
- MeSH terms
- Algorithms, Computer Graphics, Humans, Image Processing, Computer-Assisted, Software
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