Magnetic Resonance in Medicine

Virtual Issue: Data Sampling and Image Reconstruction

Methods for sampling MRI data and for transforming that data to a usable image continue to be proposed and refined, and the primary journal for publishing this work remains MRM. This retrospective table of contents (TOC) series highlights a few such papers, and coincides with the "ISMRM Workshop on Data Sampling and Image Reconstruction" to be held in Sedona, Arizona from February 3 – 6, 2012 (http://www.ismrm.org/workshops/Data13/)

At the core of any application in MRI are the methods with which data are sampled and used to create an image. Because these methods are loosely correlated with the type of application, technologies discussed here may be specific to one area (e.g., fMRI), but more often have broad impact in their utility. This field continues to flourish as it challenges such notions as the sampling theorem, of linear encoding gradients, and of conventional Cartesian sampling approaches, while it enjoys the benefits ever-increasing computational power. Despite the huge promise offered in this area, many of the new approaches continue to face challenges of robustness, repeatability, predictability, and practicality compared to the conventional sampling and reconstruction methods which have been historically used in MRI. Thus beyond invention, the completion, testing and translation of new ideas into something useful adds to the body of work. The papers below represent a sparse and somewhat random sampling of the more recent work in this area published in Magn Reson Med, along with a few classic references, (with many important works left out).

Classic Papers:
1. Fast spiral coronary artery imaging
CH. Meyer, BS. Hu, DG. Nishimura, A. Macovski
1992

2. SENSE: Advances in sensitivity encoding with arbitrary k-space trajectories
KP. Pruessmann, M. Weiger, P. Börnert, P. Boesiger
2001

3. Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA)
MA. Griswold, PM. Jakob, RM. Heidemann, M. Nittka, V. Jellus, J. Wang, B Kiefer, A. Haase
2002

4. k-t BLAST and k-t SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations
J Tsao, P Boesiger, KP Pruessmann
2003

5. Sparse MRI: The application of compressed sensing for rapid MR imaging
M. Lustig, D. Donoho, JM Pauly
2007

2011 Vol 66
6. The Effect of Reconstruction and Acquisition Parameters for GRAPPA-Based Parallel Imaging on the Image Quality
S. Bauer, M. Markl, M. Honal, and B. A. Jung

7. Low-dimensional-Structure Self-Learning and Thresholding: Regularization Beyond Compressed Sensing for MRI Reconstruction
Mehmet Akc ̧akaya, Tamer A. Basha, Beth Goddu, Lois A. Goepfert, Kraig V. Kissinger, Vahid Tarokh, Warren J. Manning, and Reza Nezafat

8. On the Undersampling Strategies to Accelerate Time-Resolved 3D Imaging Using k-t-GRAPPA
Bernd Jung, Aurelien F. Stalder, Simon Bauer, and Michael Markl

9. Sparse-CAPR: Highly Accelerated 4D CE-MRA With Parallel Imaging and Nonconvex Compressive Sensing
Joshua D. Trzasko, Clifton R. Haider, Eric A. Borisch, Norbert G. Campeau, James F. Glockner, Stephen J. Riederer, and Armando Manduca

10. Spiral Phyllotaxis: The Natural Way to Construct a 3D Radial Trajectory in MRI
Davide Piccini, Arne Littmann, Sonia Nielles-Vallespin, and Michael O. Zenge

11. Generalized GRAPPA Operators for Wider Spiral Bands: Rapid Self-Calibrated Parallel Reconstruction for Variable Density Spiral MRI
Wei Lin, Peter Bornert, Feng Huang, George R. Duensing, and Arne Reykowski

12. A New Design and Rationale for 3D Orthogonally Oversampled k-Space Trajectories
James G. Pipe, Nicholas R. Zwart, Eric A. Aboussouan, Ryan K. Robison, Ajit Devaraj, and Kenneth O. Johnson

2012: Volume 67
13. Parallel Imaging with Nonlinear Reconstruction Using Variational Penalties
Florian Knoll, Christian Clason, Kristian Bredies, Martin Uecker, and Rudolf Stollberger

14. A Hybrid Method for More Efficient Channel-by- Channel Reconstruction with Many Channels
Feng Huang, Wei Lin, George R. Duensing, and Arne Reykowski

15. Parallel Traveling-Wave MRI: A Feasibility Study
Yong Pang, Daniel B. Vigneron, and Xiaoliang Zhang

16. Sparsity and Low-Contrast Object Detectability
Joshua D. Trzasko, Zhonghao Bao, Armando Manduca, Kiaran P. McGee, and Matt A. Bernstein

17. Improving GRAPPA Using Cross-Sampled Autocalibration Data
Haifeng Wang, Dong Liang, Kevin F. King, Gajanan Nagarsekar, Yuchou Chang, and Leslie Ying

18. Improved Least Squares MR Image Reconstruction Using Estimates of k-Space Data Consistency
Kevin M. Johnson, Walter F. Block, Scott. B. Reeder, and Alexey Samsonov

19. Localization by Nonlinear Phase Preparation and k-Space Trajectory Design
Walter R.T. Witschey, Chris A. Cocosco, Daniel Gallichan, Gerrit Schultz, Hans Weber, Anna Welz, Ju ̈ rgen Hennig, and Maxim Zaitsev

20. Adaptive Self-Calibrating Iterative GRAPPA Reconstruction
Suhyung Park and Jaeseok Park

2012: Volume 68:
21. Application of the Fractional Fourier Transform to Image Reconstruction in MRI
Vicente Parot, Carlos Sing-Long, Carlos Lizama, Cristian Tejos, Sergio Uribe, and Pablo Irarrazaval

22. K-t ISD: Dynamic Cardiac MR Imaging Using Compressed Sensing with Iterative Support Detection
Dong Liang, Edward V. R. DiBella, Rong-Rong Chen, and Leslie Ying

23. Simple Method for MR Gradient System Characterization and K-Space Trajectory Estimation
Nii Okai Addy, Holden H. Wu, and Dwight G. Nishimura

24. Single Shot Concentric Shells Trajectories for Ultra Fast fMRI
Benjamin Zahneisen, Thimo Hugger, Kuan J. Lee, Pierre LeVan, Marco Reisert, Hsu-Lei Lee, Jakob Assla ̈ nder, Maxim Zaitsev, and Jurgen Hennig

25. Nonlinear GRAPPA: A Kernel Approach to Parallel MRI Reconstruction
Yuchou Chang, Dong Liang, and Leslie Ying

26. k-t Sparse GROWL: Sequential Combination of Partially Parallel Imaging and Compressed Sensing in k-t Space Using Flexible Virtual Coil
Feng Huang, Wei Lin, George R. Duensing, and Arne Reykowski

27. Null Space Imaging: Nonlinear Magnetic Encoding Fields Designed Complementary to Receiver Coil Sensitivities for Improved Acceleration in Parallel Imaging
Leo K. Tam, Jason P. Stockmann, Gigi Galiana, and R. Todd Constable

28. Accelerated MR Imaging Using Compressive Sensing with No Free Parameters
Kedar Khare, Christopher J. Hardy, Kevin F. King, Patrick A. Turski, and Luca Marinelli

Early View
29. Location constrained approximate message passing for compressed sensing MRI
Kyunghyun Sung, Bruce L. Daniel and Brian A. Hargreaves

30. Distributed spirals: A new class of three-dimensional k-space trajectories
Dallas C. Turley and James G. Pipe

31. Single shot trajectory design for region-specific imaging using linear and nonlinear magnetic encoding fields
Kelvin J. Layton, Daniel Gallichan, Frederik Testud, Chris A. Cocosco, Anna M. Welz, Christoph Barmet, Klaas P. Pruessmann, Jürgen Hennig and Maxim Zaitsev

32. Highly accelerated real-time cardiac cine MRI using k–t SPARSE-SENSE
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

33. Gadgetron: An open source framework for medical image reconstruction
Michael Schacht Hansen and Thomas Sangild Sørensen

34. Improved parallel MR imaging using a coefficient penalized regularization for GRAPPA reconstruction
Wentao Liu, Xin Tang, Yajun Ma and Jia-Hong Gao

35. Coil compression for accelerated imaging with Cartesian sampling
Tao Zhang, John M. Pauly, Shreyas S. Vasanawala and Michael Lustig