Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2008, Article ID 341684, 12 pages
http://dx.doi.org/10.1155/2008/341684
Research Article

Reordering for Improved Constrained Reconstruction from Undersampled k-Space Data

1Laboratory for Structural NMR Imaging, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
2Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT 84112, USA
3Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, UT 84108, USA
4Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, USA

Received 8 April 2008; Revised 29 June 2008; Accepted 2 October 2008

Academic Editor: Habib Zaidi

Copyright © 2008 Ganesh Adluru and Edward V. R. DiBella. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [26 citations]

The following is the list of published articles that have cited the current article.

  • Pooria Zamani, and Hamid Soltanian-Zadeh, “Compressive sensing cardiac cine MRI using invertible non-linear transform,” 2014 22nd Iranian Conference on Electrical Engineering (ICEE), pp. 1903–1906, . View at Publisher · View at Google Scholar
  • Sung-Min Gho, Chunlei Liu, and Dong-Hyun Kim, “Application of Low-pass & High-pass reconstruction for improving the performance of the POCS based algorithm,” 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1–3, . View at Publisher · View at Google Scholar
  • Anil K. Attili, Andreas Schuster, Eike Nagel, Johan H. C. Reiber, and Rob J. van der Geest, “Quantification in cardiac MRI: advances in image acquisition and processing,” International Journal of Cardiovascular Imaging, vol. 26, pp. 27–40, 2010. View at Publisher · View at Google Scholar
  • F D Santiago, R T Nakamura, D Kaplan, and E C de Faria, “Protective modulation of carotid atherosclerosis in hyperalphalipoproteinemic individuals.,” The international journal of cardiovascular imaging, vol. 26, no. 1, pp. 27–34, 2010. View at Publisher · View at Google Scholar
  • Leonardo Ramirez, Claudia Prieto, Carlos Sing-Long, Sergio Uribe, Philip Batchelor, Cristian Tejos, and Pablo Irarrazaval, “TRIO a Technique for Reconstruction Using Intensity Order: Application to Undersampled MRI,” IEEE Transactions on Medical Imaging, vol. 30, no. 8, pp. 1566–1576, 2011. View at Publisher · View at Google Scholar
  • Jason Mendes, Dennis L. Parker, Jordan Hulet, Gerald S. Treiman, and Seong-Eun Kim, “CINE turbo spin echo imaging,” Magnetic Resonance in Medicine, vol. 66, no. 5, pp. 1286–1292, 2011. View at Publisher · View at Google Scholar
  • Saiprasad Ravishankar, and Yoram Bresler, “MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning,” IEEE Transactions on Medical Imaging, vol. 30, no. 5, pp. 1028–1041, 2011. View at Publisher · View at Google Scholar
  • M. Usman, C. Prieto, T. Schaeffter, and P. G. Batchelor, “k-t group sparse: A method for accelerating dynamic MRI,” Magnetic Resonance in Medicine, vol. 66, no. 4, pp. 1163–1176, 2011. View at Publisher · View at Google Scholar
  • Bing Wu, Rick P. Millane, Richard Watts, and Philip J. Bones, “Prior Estimate-Based Compressed Sensing in Parallel MRI,” Magnetic Resonance in Medicine, vol. 65, no. 1, pp. 83–95, 2011. View at Publisher · View at Google Scholar
  • Rachid Deriche, Sylvain Merlet, Jian Cheng, and Aurobrata Ghosh, “Spherical Polar Fourier EAP and ODF reconstruction via compressed sensing in diffusion MRI,” Proceedings - International Symposium on Biomedical Imaging, pp. 365–371, 2011. View at Publisher · View at Google Scholar
  • Li Feng, Monvadi B. Srichai, Ruth P. Lim, Alexis Harrison, Wilson King, Ganesh Adluru, Edward V. R. Dibella, Daniel K. Sodickson, Ricardo Otazo, and Daniel Kim, “Highly accelerated real-time cardiac cine MRI using k-t SPARSE-SENSE,” Magnetic Resonance in Medicine, 2012. View at Publisher · View at Google Scholar
  • J. M. Wild, S. Kozerke, P. G. Batchelor, T. Schaeffter, C. Prieto, and M. Usman, “Group sparse reconstruction using intensity-based clustering,” Magnetic Resonance in Medicine, vol. 69, no. 4, pp. 1169–1179, 2012. View at Publisher · View at Google Scholar
  • Bahareh Vafadar, and Philip J. Bones, “MR images from fewer data,” Image Reconstruction From Incomplete Data Vii, vol. 8500, 2012. View at Publisher · View at Google Scholar
  • Sylvain. L Merlet, and Rachid Deriche, “Continuous diffusion signal, EAP and ODF estimation via Compressive Sensing in diffusion MRI,” Medical Image Analysis, vol. 17, no. 5, pp. 556–572, 2013. View at Publisher · View at Google Scholar
  • Xiaobo Qu, Yingkun Hou, Fan Lam, Di Guo, Jianhui Zhong, and Zhong Chen, “Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator,” Medical Image Analysis, 2013. View at Publisher · View at Google Scholar
  • W. Koning, C. A. T. van den Berg, J. J. M. Zwanenburg, E. A. J. Langenhuizen, A. J. Raaijmakers, A. Andreychenko, P. R. Luijten, D. W. J. Klomp, and J. J. Bluemink, “High-Resolution MRI of the Carotid Arteries Using a Leaky Waveguide Transmitter and a High-Density Receive Array at 7 T,” Magnetic Resonance In Medicine, vol. 69, no. 4, pp. 1186–1193, 2013. View at Publisher · View at Google Scholar
  • Xiaobo Qu, Yingkun Hou, Fan Lam, Di Guo, and Zhong Chen, “Magnetic resonance image reconstruction using similarities learnt from multi-modal images,” 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings, pp. 264–268, 2013. View at Publisher · View at Google Scholar
  • Maria Rosaria Russo, “Adaptive Arnoldi-Tikhonov regularization for image restoration,” Numerical Algorithms, vol. 65, no. 4, pp. 745–757, 2014. View at Publisher · View at Google Scholar
  • Ganesh Adluru, Yaniv Gur, Liyong Chen, David Feinberg, Jeffrey Anderson, and Edward V. R. DiBella, “MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering,” Medical Physics, vol. 42, no. 8, pp. 4734–4744, 2015. View at Publisher · View at Google Scholar
  • James A. Rioux, Steven D. Beyea, and Chris V. Bowen, “3D single point imaging with compressed sensing provides high temporal resolution R 2* mapping for in vivo preclinical applications,” Magnetic Resonance Materials in Physics, Biology and Medicine, 2016. View at Publisher · View at Google Scholar
  • Johannes F. M. Schmidt, Claudio Santelli, and Sebastian Kozerke, “MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods,” Plos One, vol. 11, no. 4, 2016. View at Publisher · View at Google Scholar
  • Srikant Kamesh Iyer, Tolga Tasdizen, Nathan Burgon, Eugene Kholmovski, Nassir Marrouche, Ganesh Adluru, and Edward DiBella, “Compressed sensing for rapid late gadolinium enhanced imaging of the left atrium: A preliminary study,” Magnetic Resonance Imaging, 2016. View at Publisher · View at Google Scholar
  • Li Feng, Leon Axel, Hersh Chandarana, Kai Tobias Block, Daniel K. Sodickson, and Ricardo Otazo, “XD-GRASP: Golden-Angle Radial MRI with Reconstruction of Extra Motion-State Dimensions Using Compressed Sensing,” Magnetic Resonance In Medicine, vol. 75, no. 2, pp. 775–788, 2016. View at Publisher · View at Google Scholar
  • Shujun Liu, Jianxin Cao, Hongqing Liu, Xichuan Zhou, Kui Zhang, and Zhengzhou Li, “MRI reconstruction via enhanced group sparsity and nonconvex regularization,” Neurocomputing, 2017. View at Publisher · View at Google Scholar
  • Saiprasad Ravishankar, Brian E. Moore, Raj Rao Nadakuditi, and Jeffrey A. Fessler, “Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging,” IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1116–1128, 2017. View at Publisher · View at Google Scholar
  • Medya Siadat, Nasser Aghazadeh, and Ozan Öktem, “Reordering for improving global Arnoldi–Tikhonov method in image restoration problems,” Signal, Image and Video Processing, 2017. View at Publisher · View at Google Scholar