The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Accelerating advanced MRI reconstructions on GPUs
Open accessAbstract
Computational acceleration on graphics processing units (GPUs) can make advanced magnetic resonance imaging (MRI) reconstruction algorithms attractive in clinical settings, thereby improving the quality of MR images across a broad spectrum of applications. This paper describes the acceleration of such an algorithm on NVIDIA's Quadro FX 5600. The reconstruction of a 3D image with 128(3) voxels achieves up to 180 GFLOPS and requires just over one minute on the Quadro, while reconstruction on a quad-core CPU is twenty-one times slower. Furthermore, relative to the true image, the error exhibited by the advanced reconstruction is only 12%, while conventional reconstruction techniques incur error of 42%.
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“Aynı yıl yayınlanan Stone ve diğerlerinin [14] çalışması ise görüntü işlemede CUDA kullanılmasının ilk örneğidir.”
From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [14]
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Cites
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Self-citationAdvances in sensitivity encoding with arbitrary k ‐space trajectories
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Research works printed but not linked: 1
- S. Stone, H. Yi, J. Haldar, W. Hwu, B. Sutton, Z. Liang, How GPUs can improve the quality of magnetic resonance imaging, in: First Workshop on General Purpose Processing on Graphics Processing Units, GPGPU, 2007
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Gai, J., Obeid, N., Holtrop, J. L., Wu, X.-L., Lam, F., Fu, M., Haldar, J. P., Hwu, W.-M. W., Liang, Z.-P., & Sutton, B. P. (2013) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2013.01.001 · Cited by 43
Self-citationParallel CT image reconstruction based on GPUs
Flores, L. A., Vidal, V., Mayo, P., Rodenas, F., & Verdú, G. (2014) · Radiation Physics and Chemistry · doi:10.1016/j.radphyschem.2013.03.011 · Cited by 28
CT Image Reconstruction Based on GPUs
Flores, L. A., Vidal, V., Mayo, P., Rodenas, F., & Verdú, G. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.308 · Cited by 16
A CUDA-based reverse gridding algorithm for MR reconstruction
Yang, J., Feng, C., & Zhao, D. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.038 · Cited by 16
CUDA accelerated method for motion correction in MR PROPELLER imaging
Feng, C., Yang, J., Zhao, D., & Liu, J. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.04.013 · Cited by 8
GPU 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
Metadata
- Authors
- Stone, S. S., Haldar, J. P., Tsao, S. C., Hwu, W.-M. W., Sutton, B. P., & Liang, Z.-P.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Stone, S.S.
- Venue
- Journal of Parallel and Distributed Computing
- Publisher
- Elsevier BV
- Published
- 2008-10
- Volume
- 68
- Issue
- 10
- Pages
- 1307-1318
- ISSN
- 0743-7315 (ISSN-L)
- PMC
- PMC3142623
- OpenAlex
- W2036343507
- Cited by
- 249
- References
- 43
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 5 · 2022: 2 · 2021: 8 · 2020: 9 · 2019: 11 · 2018: 17 · 2017: 17 · 2016: 15 · 2015: 17 · 2014: 22 · 2013: 24 · 2012: 25
- FWCI
- 17.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Parallel and Distributed Computing (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI 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
- MRI reconstruction, GPU acceleration, graphics processing units, parallel computing, 3D image reconstruction, computational acceleration, reconstruction error, CPU vs GPU performance comparison
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