Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?
Corresponding Author
Esben Plenge
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Departments of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, P. O. Box 2040, 3000 CA Rotterdam, The Netherlands===Search for more papers by this authorDirk H. J. Poot
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorMonique Bernsen
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorGyula Kotek
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorGavin Houston
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorPiotr Wielopolski
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorLouise van der Weerd
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
Search for more papers by this authorWiro J. Niessen
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Quantitative Imaging Group, Department of Imaging Science & Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
Search for more papers by this authorErik Meijering
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorCorresponding Author
Esben Plenge
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Departments of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, P. O. Box 2040, 3000 CA Rotterdam, The Netherlands===Search for more papers by this authorDirk H. J. Poot
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorMonique Bernsen
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorGyula Kotek
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorGavin Houston
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorPiotr Wielopolski
Department of Radiology, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorLouise van der Weerd
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
Search for more papers by this authorWiro J. Niessen
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Quantitative Imaging Group, Department of Imaging Science & Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
Search for more papers by this authorErik Meijering
Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center, Rotterdam, Rotterdam, The Netherlands
Department of Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC – University Medical Center Rotterdam, Rotterdam, The Netherlands
Search for more papers by this authorAbstract
Improving the resolution in magnetic resonance imaging comes at the cost of either lower signal-to-noise ratio, longer acquisition time or both. This study investigates whether so-called super-resolution reconstruction methods can increase the resolution in the slice selection direction and, as such, are a viable alternative to direct high-resolution acquisition in terms of the signal-to-noise ratio and acquisition time trade-offs. The performance of six super-resolution reconstruction methods and direct high-resolution acquisitions was compared with respect to these trade-offs. The methods are based on iterative back-projection, algebraic reconstruction, and regularized least squares. The algorithms were applied to low-resolution data sets within which the images were rotated relative to each other. Quantitative experiments involved a computational phantom and a physical phantom containing structures of known dimensions. To visually validate the quantitative evaluations, qualitative experiments were performed, in which images of three different subjects (a phantom, an ex vivo rat knee, and a postmortem mouse) were acquired with different magnetic resonance imaging scanners. The results show that super-resolution reconstruction can indeed improve the resolution, signal-to-noise ratio and acquisition time trade-offs compared with direct high-resolution acquisition. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.
REFERENCES
- 1 Haacke EM, Brown RW, Thomson MR, Venkatesan R. Magnetic resonance imaging. Physical principles and sequence design. New York: Wiley; 1999.
- 2 Heidemann RM, Özsarlak O, Parizel PM, Michiels J, Kiefer B, Jellus V, Müller M, Breuer F, Blaimer M, Griswold MA, Jakob PM. A brief review of parallel magnetic resonance imaging. Eur Radiol 2003; 13: 2323–2337.
- 3 Pruessmann K. Encoding and reconstruction in parallel MRI. NMR Biomed 2006; 19: 288–299.
- 4
Pipe JG.
Motion correction with PROPELLER MRI: Application to head motion and free-breathing cardiac imaging.
Magn Reson Med
1999;
42:
963–969.
10.1002/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L CAS PubMed Web of Science® Google Scholar
- 5 Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58: 1182–1195.
- 6 Tsai RY, Huang TS. Multi-frame image restoration and registration. In: Advances in computer vision and image processing, Vol. 1. Greenwich, CT, USA: JAI Press; 1984. pp 317–339.
- 7 Borman S, Stevenson RL. Spatial resolution enhancement of low-resolution image sequences: a comprehensive review with directions for future research. Technical report, Department of Electrical Engineering, University of Notre Dame, Notre Dame, Indiana, USA, 1998.
- 8 Elad M, Feuer A. Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images. IEEE Trans Image Process 1997; 6: 1646–1658.
- 9 Park S, Park M, Kang M. Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 2003; 20: 21–36.
- 10 Fiat D. Method of enhancing an MRI signal. U S Patent 6,294,914 ( 2001).
- 11 Peled S, Yeshurun Y. Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magn Reson Med 2001; 45: 29—35.
- 12 Greenspan H, Oz G, Kiryati N, Peled S. MRI inter-slice reconstruction using super-resolution. Magn Reson Imaging 2002; 20: 437–446.
- 13 Peeters RR, Kornprobst P, Nikolova M, Sunaert S, Vieville T, Malandain G, Deriche R, Faugeras O, Ng M, Hecke PV. The use of superresolution techniques to reduce slice thickness in functional MRI. Int J Imag Syst Tech 2004; 14: 131–138.
- 14
Zhang X, Lam EY, Wu EX, Wong KK.
Application of Tikhonov regularization to super-resolution reconstruction of brain MRI images. In:
Medical Imaging and Informatics.
Berlin, Heidelberg:
Springer-Verlag;
2008. pp
51–56.
10.1007/978-3-540-79490-5_8 Google Scholar
- 15 Rousseau F, Glenn OA, Iordanova B, Rodriguez-Carranza C, Vigneron DB, Barkovich JA, Studholme C. Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad Radiol 2006; 13: 1072–1081.
- 16 Rousseau F, Kim K, C S, Koob M, Dietemann JL. On super-resolution for fetal brain MRI. In: Proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II. Berlin, Heidelberg: Springer-Verlag; 2010, p. 355–362.
- 17 Gholipour A, Estroff J, Warfield S. Robust super-resolution volume reconstruction from slice acquisitions: Application to fetal brain MRI. IEEE Trans Med Imaging 2010; 29: 1739–1758.
- 18 Robinson D, Milanfar P. Statistical performance analysis of super-resolution. IEEE Trans Image Process 2006; 15: 1413–1428.
- 19 Lin Z, Shum HY. Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans Pattern Anal Mach Intell 2004; 26: 83–97.
- 20 Shilling RZ, Robbie TQ, Bailloeul T, Mewes K, Mersereau RM, Brummer ME. A super-resolution framework for 3-D high-resolution and high-contrast imaging using 2-D multislice MRI. IEEE Trans Med Imaging 2009; 28: 633–644.
- 21 Poot DHJ, Van Meir V, Sijbers J. General and Efficient Super-Resolution Method for Multi-Slice MRI. In: Proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I. Berlin, Heidelberg: Springer-Verlag; 2010. pp 615–622.
- 22 Greenspan H. Super-resolution in medical imaging. Comput J 2009; 52: 43–63.
- 23 Scheffler K. Superresolution in MRI? Magn Reson Med 2002; 48: 408.
- 24 Peled S, Yeshurun Y. Superresolution in MRI — Perhaps sometimes. Magn Reson Med 2002; 48: 409–409.
- 25 Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med 1995; 34: 910–914.
- 26 Irani M, Peleg S. Improving resolution by image registration. CVGIP: Graph Models Image Process 1991; 53: 231–239.
- 27 Zomet A, Rav-Acha A, Peleg S. Robust Super-Resolution. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2001, Kauai, HI. pp 645–650.
- 28 Kaczmarz S. Angenäherte Auflösung von Systemen linearer Gleichungen. Bull Acad Pol Sci Lett A 1937; 355–357.
- 29 Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am A 1989; 6: 1715–1726.
- 30 Gordon R, Bender R, Herman GT. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J Theor Biol 1970; 29: 471–481.
- 31 Herman GT, Lent A, Rowland SW. ART: Mathematics and applications. A report on the mathematical foundations and on the applicability to real data of the algebraic reconstruction techniques. J Theor Biol 1973; 42: 1–32.
- 32 van Eekeren AWM, Schutte K, Oudegeest OR, van Vliet LJ. Performance evaluation of super-resolution reconstruction methods on real-world data. EURASIP J Adv Signal Process 2007; 2007: 1–12.
