Fetching the paper…
Reading the bibliography…
The advanced magnetic resonance (MR) image reconstructions such as the compressed sensing and subspace-based imaging are considered as large-scale, iterative, optimization problems.
D. K. Sodickson and W. J. Manning, “Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays,” Magnetic resonance in medicine , vol. 38, no. 4, pp. 591–603, 1997
1997
Earlier work this paper cites.
K. P. Pruessmann, M. Weiger, M. B. Scheidegger, and P. Boesiger, “SENSE: sensitivity encoding for fast MRI,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 42, no. 5, pp. 952–962, 1999
1999
Earlier work this paper cites.
J. A. Fessler and B. P. Sutton, “Nonuniform fast Fourier transforms using min-max interpolation,” IEEE Transactions on Signal Processing , vol. 51, no. 2, pp. 560–574, 2003
2003
Earlier work this paper cites.
K. P. Pruessmann, “Encoding and reconstruction in parallel MRI,” NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In vivo , vol. 19, no. 3, pp. 288–299, 2006
2006
Earlier work this paper cites.
E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on information theory , vol. 52, no. 2, pp. 489–509, 2006
2006
Earlier work this paper cites.
D. L. Donoho, “Compressed sensing,” IEEE Transactions on information theory , vol. 52, no. 4, pp. 1289–1306, 2006
2006
Earlier work this paper cites.
E. J. Candès and T. Tao, “Near-optimal signal recovery from random projections: Universal encoding strategies?” IEEE transactions on information theory , vol. 52, no. 12, pp. 5406–5425, 2006
2006
Earlier work this paper cites.
M. Lustig, D. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine , vol. 58, no. 6, pp. 1182–1195, 2007
2007
Earlier work this paper cites.
Z.-P. Liang, “Spatiotemporal imaging with partially separable functions,” p. 988–991, 2007
2007
Earlier work this paper cites.
S. S. Stone, H. Yi, J. P. Haldar, W.-m. W. Hwu, B. P. Sutton, and Z.-p. Liang, “How GPUs can improve the quality of magnetic resonance imaging,” in In The First Workshop on General Purpose Processing on Graphics Processing Units , 2007
2007
Earlier work this paper cites.
T. S. Sörensen, T. Schaeffter, K. Ø. Noe, and M. S. Hansen, “Accelerating the nonequispaced fast fourier transform on commodity graphics hardware,” IEEE Transactions on Medical Imaging , vol. 27, no. 4, pp. 538–547, April 2008. [Online]. Available: https://doi.org/10.1109/TMI.2007.909834
2007
Earlier work this paper cites.
S. S. Stone, J. P. Haldar, S. C. Tsao, W.-m. W. Hwu, B. P. Sutton, and Z.-P. Liang, “Accelerating advanced mri reconstructions on GPUs,” Journal of Parallel and Distributed Computing , vol. 68, no. 10, pp. 1307 – 1318, 2008, general-Purpose Processing using Graphics Processing Units. [Online]. Available: https://doi.org/https://doi.org/10.1016/j.jpdc.2008.05.013
2008
Earlier work this paper cites.
M. S. Hansen, D. Atkinson, and T. S. Sörensen, “Cartesian sense and k-t sense reconstruction using commodity graphics hardware,” Magnetic Resonance in Medicine , vol. 59, no. 3, pp. 463–468, 2008. [Online]. Available: https://doi.org/10.1002/mrm.21523
2008
Earlier work this paper cites.
Z. Yang and M. Jacob, “Efficient NUFFT algorithm for non-cartesian MRI reconstruction,” in IEEE International Symposium on Biomedical Imaging , June 2009, pp. 117–120. [Online]. Available: https://doi.org/10.1109/ISBI.2009.5192997
2009
Earlier work this paper cites.
Y. Zhuo, X.-L. Wu, J. P. Haldar, W.-m. W. Hwu, Z.-P. Liang, and B. P. Sutton, “Accelerating iterative field-compensated MR image reconstruction on GPUs,” in 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2010, pp. 820–823
2010
Cited alongside, same era.
S. Ramani and J. A. Fessler, “Parallel MR image reconstruction using augmented lagrangian methods,” IEEE Transactions on Medical Imaging , vol. 30, no. 3, pp. 694–706, 2010
2010
Cited alongside, same era.
G. Pratx and L. Xing, “GPU computing in medical physics: a review,” Medical Physics , vol. 38, no. 5, pp. 2685–2697, 2011. [Online]. Available: https://doi.org/10.1118/1.3578605
2011
Cited alongside, same era.
Y. Zhuo, X.-L. Wu, J. P. Haldar, T. Marin, W.-m. W. Hwu, Z.-P. Liang, and B. P. Sutton, Using GPUs to Accelerate Advanced MRI Reconstruction with Field Inhomogeneity Compensation , ser. Applications of GPU Computing Series. Morgan Kaufmann, 2011, pp. 709–722
2011
Cited alongside, same era.
A. Cerjanic, J. L. Holtrop, G. C. Ngo, B. Leback, G. Arnold, M. Van Moer, G. LaBelle, J. A. Fessler, and B. P. Sutton, “Powergrid: A open source library for accelerated iterative magnetic resonance image reconstruction,” in Proc. Intl. Soc. Mag. Res. Med , vol. 525, 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
P. Després and X. Jia, “A review of GPU-based medical image reconstruction,” Physica Medica: European Journal of Medical Physics , vol. 42, pp. 76–92, Oct 2017. [Online]. Available: https://doi.org/10.1016/j.ejmp.2017.07.024
2017
Later among the works it cites.
C.-H. Chang, X. Yu, and J. X. Ji, “Compressed sensing MRI reconstruction from 3d multichannel data using GPUs,” Magnetic Resonance in Medicine , vol. 78, no. 6, pp. 2265–2274, 2017. [Online]. Available: https://doi.org/10.1002/mrm.26636
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine learning , vol. 3, no. 1, pp. 1–122, 2011
2011
Cited alongside, same era.
X.-L. Wu, J. Gai, F. Lam, M. Fu, J. P. Haldar, Y. Zhuo, Z.-P. Liang, W.-M. W. Hwu, and B. P. Sutton, “Impatient MRI: Illinois Massively Parallel Acceleration Toolkit for image reconstruction with enhanced throughput in MRI,” in IEEE International Symposium on Biomedical Imaging . IEEE, 2011, pp. 69–72, exported from refbase (https://dell-desktop:81/refbase/show.php?record=2870), last updated on Tue, 07 Apr 2020 14:32:34 -0400. [Online]. Available: https://doi.org/10.1109/ISBI.2011.5872356
2011
Cited alongside, same era.
M. Murphy, M. Alley, J. Demmel, K. Keutzer, S. Vasanawala, and M. Lustig, “Fast ℓ 1 \ell_{1} -spirit compressed sensing parallel imaging mri: Scalable parallel implementation and clinically feasible runtime,” IEEE Transactions on Medical Imaging , vol. 31, no. 6, pp. 1250–1262, June 2012. [Online]. Available: https://doi.org/10.1109/TMI.2012.2188039
2012
Cited alongside, same era.
D. S. Smith, J. C. Gore, T. E. Yankeelov, and E. B. Welch, “Real-time compressive sensing mri reconstruction using GPU computing and split bregman methods,” International journal of biomedical imaging , vol. 2012, pp. 864 827–864 827, 2012, 22481908[pmid]. [Online]. Available: https://doi.org/10.1155/2012/864827
2012
Cited alongside, same era.
S. Schaetz and M. Uecker, “A multi-GPU programming library for real-time applications,” in Algorithms and Architectures for Parallel Processing , Y. Xiang, I. Stojmenovic, B. O. Apduhan, G. Wang, K. Nakano, and A. Zomaya, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 114–128
2012
Cited alongside, same era.
M. Freiberger, F. Knoll, K. Bredies, H. Scharfetter, and R. Stollberger, “The agile library for biomedical image reconstruction using GPU acceleration,” Computing in Science Engineering , vol. 15, no. 1, pp. 34–44, Jan 2013. [Online]. Available: https://doi.org/10.1109/MCSE.2012.40
2012
Cited alongside, same era.
A. Eklund, P. Dufort, D. Forsberg, and S. M. LaConte, “Medical image processing on the GPU - past, present and future,” Medical Image Analysis , vol. 17, no. 8, pp. 1073 – 1094, 2013. [Online]. Available: https://doi.org/https://doi.org/10.1016/j.media.2013.05.008
2013
Cited alongside, same era.
S. Nam, M. Akçakaya, T. Basha, C. Stehning, W. J. Manning, V. Tarokh, and R. Nezafat, “Compressed sensing reconstruction for whole-heart imaging with 3d radial trajectories: a graphics processing unit implementation,” Magnetic Resonance in Medicine , vol. 69, no. 1, pp. 91–102, 2013. [Online]. Available: https://doi.org/10.1002/mrm.24234
2013
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers et al. , “In-datacenter performance analysis of a tensor processing unit,” in 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) . IEEE, 2017, pp. 1–12
2017
Later among the works it cites.
N. Jouppi. (2017) Quantifying the performance of the TPU, our first machine learning chip. [Online]. Available: https://cloud.google.com/blog/products/gcp/quantifying-the-performance-of-the-tpu-our-first-machine-learning-chip
2017
Later among the works it cites.
H. Wang, H. Peng, Y. Chang, and D. Liang, “A survey of GPU-based acceleration techniques in mri reconstructions,” Quantitative Imaging in Medicine and Surgery , vol. 8, no. 2, 2018. [Online]. Available: http://qims.amegroups.com/article/view/18832
2018
Later among the works it cites.
M. Uecker and J. Tamir, “mrirecon/bart: version 0.5.00,” Aug. 2019. [Online]. Available: https://doi.org/10.5281/zenodo.3376744
2019
Later among the works it cites.
K. Yang, Y.-F. Chen, G. Roumpos, C. Colby, and J. Anderson, “High performance Monte Carlo simulation of Ising model on TPU clusters,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis , ser. SC ’19. ACM, 2019, pp. 83:1–83:15. [Online]. Available: http://doi.acm.org/10.1145/3295500.3356149
2019
Later among the works it cites.
2019
Later among the works it cites.
F. Ong and M. Lustig, “SigPy: A Python Package for High Performance Iterative Reconstruction,” in Proc. ISMRM , 2019
2019
Later among the works it cites.