Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?
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...
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| Published in | Magnetic resonance in medicine Vol. 68; no. 6; pp. 1983 - 1993 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc., A Wiley Company
01.12.2012
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0740-3194 1522-2594 1522-2594 |
| DOI | 10.1002/mrm.24187 |
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| Abstract | 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. |
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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] 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. 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.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. |
| Author | Wielopolski, Piotr Bernsen, Monique Kotek, Gyula Meijering, Erik Houston, Gavin van der Weerd, Louise Plenge, Esben Niessen, Wiro J. Poot, Dirk H. J. |
| Author_xml | – sequence: 1 givenname: Esben surname: Plenge fullname: Plenge, Esben email: e.plenge@erasmusmc.nl organization: Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC - University Medical Center, Rotterdam, Rotterdam, The Netherlands – sequence: 2 givenname: Dirk H. J. surname: Poot fullname: Poot, Dirk H. J. organization: Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC - University Medical Center, Rotterdam, Rotterdam, The Netherlands – sequence: 3 givenname: Monique surname: Bernsen fullname: Bernsen, Monique organization: Department of Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands – sequence: 4 givenname: Gyula surname: Kotek fullname: Kotek, Gyula organization: Department of Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands – sequence: 5 givenname: Gavin surname: Houston fullname: Houston, Gavin organization: Department of Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands – sequence: 6 givenname: Piotr surname: Wielopolski fullname: Wielopolski, Piotr organization: Department of Radiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands – sequence: 7 givenname: Louise surname: van der Weerd fullname: van der Weerd, Louise organization: Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 8 givenname: Wiro J. surname: Niessen fullname: Niessen, Wiro J. organization: Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC - University Medical Center, Rotterdam, Rotterdam, The Netherlands – sequence: 9 givenname: Erik surname: Meijering fullname: Meijering, Erik organization: Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC - University Medical Center, Rotterdam, Rotterdam, The Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22298247$$D View this record in MEDLINE/PubMed |
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| CODEN | MRMEEN |
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| Cites_doi | 10.1002/mrm.10203 10.1109/TMI.2010.2051680 10.1002/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L 10.1002/mrm.10237 10.1364/JOSAA.6.001715 10.1093/comjnl/bxm075 10.1002/nbm.1042 10.1002/mrm.21391 10.1155/2007/43953 10.1109/MSP.2003.1203207 10.1109/TPAMI.2004.1261081 10.1016/j.acra.2006.05.003 10.1109/TMI.2008.2007348 10.1016/1049-9652(91)90045-L 10.1007/s00330-003-1992-7 10.1002/ima.20016 10.1016/0022-5193(70)90109-8 10.1109/83.650118 10.1007/978-3-540-79490-5_8 10.1109/TIP.2006.871079 10.1002/mrm.1910340618 10.1016/0022-5193(73)90145-8 10.1002/1522-2594(200101)45:1<29::AID-MRM1005>3.0.CO;2-Z 10.1016/S0730-725X(02)00511-8 |
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| References | Pipe JG. Motion correction with PROPELLER MRI: Application to head motion and free-breathing cardiac imaging. Magn Reson Med 1999; 42: 963-969. Kaczmarz S. Angenäherte Auflösung von Systemen linearer Gleichungen. Bull Acad Pol Sci Lett A 1937; 355-357. 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. Park S, Park M, Kang M. Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 2003; 20: 21-36. Lin Z, Shum HY. Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans Pattern Anal Mach Intell 2004; 26: 83-97. Irani M, Peleg S. Improving resolution by image registration. CVGIP: Graph Models Image Process 1991; 53: 231-239. Pruessmann K. Encoding and reconstruction in parallel MRI. NMR Biomed 2006; 19: 288-299. 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. 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. 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. Greenspan H, Oz G, Kiryati N, Peled S. MRI inter-slice reconstruction using super-resolution. Magn Reson Imaging 2002; 20: 437-446. 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. Scheffler K. Superresolution in MRI? Magn Reson Med 2002; 48: 408. Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am A 1989; 6: 1715-1726. Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med 1995; 34: 910-914. Haacke EM, Brown RW, Thomson MR, Venkatesan R. Magnetic resonance imaging. Physical principles and sequence design. New York: Wiley; 1999. 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. 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. 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. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58: 1182-1195. Robinson D, Milanfar P. Statistical performance analysis of super-resolution. IEEE Trans Image Process 2006; 15: 1413-1428. Greenspan H. Super-resolution in medical imaging. Comput J 2009; 52: 43-63. Peled S, Yeshurun Y. Superresolution in MRI - Perhaps sometimes. Magn Reson Med 2002; 48: 409-409. 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. 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. 2006; 13 2010 2004; 26 1995; 34 1991; 53 1989; 6 2006; 15 2003; 13 1998 2008 2006; 19 1999; 42 1997; 6 2001; 45 1937 2007; 58 1999 2009; 28 2002; 48 2009; 52 1973; 42 2001 2002; 20 2010; 29 1984; 1 2007; 2007 2004; 14 1970; 29 2003; 20 e_1_2_7_5_2 e_1_2_7_4_2 e_1_2_7_3_2 Kaczmarz S (e_1_2_7_29_2) 1937 e_1_2_7_9_2 e_1_2_7_8_2 e_1_2_7_6_2 Tsai RY (e_1_2_7_7_2) 1984 e_1_2_7_19_2 e_1_2_7_18_2 Haacke EM (e_1_2_7_2_2) 1999 e_1_2_7_16_2 e_1_2_7_15_2 e_1_2_7_14_2 e_1_2_7_13_2 e_1_2_7_12_2 e_1_2_7_11_2 e_1_2_7_10_2 e_1_2_7_26_2 e_1_2_7_27_2 Zomet A (e_1_2_7_28_2) 2001 Rousseau F (e_1_2_7_17_2) 2010 e_1_2_7_25_2 e_1_2_7_24_2 e_1_2_7_30_2 e_1_2_7_23_2 e_1_2_7_31_2 Poot DHJ (e_1_2_7_22_2) 2010 e_1_2_7_32_2 e_1_2_7_21_2 e_1_2_7_33_2 e_1_2_7_20_2 |
| References_xml | – reference: Kaczmarz S. Angenäherte Auflösung von Systemen linearer Gleichungen. Bull Acad Pol Sci Lett A 1937; 355-357. – reference: Greenspan H, Oz G, Kiryati N, Peled S. MRI inter-slice reconstruction using super-resolution. Magn Reson Imaging 2002; 20: 437-446. – reference: 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. – reference: Pruessmann K. Encoding and reconstruction in parallel MRI. NMR Biomed 2006; 19: 288-299. – reference: 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. – reference: 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. – reference: Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am A 1989; 6: 1715-1726. – reference: Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58: 1182-1195. – reference: Pipe JG. Motion correction with PROPELLER MRI: Application to head motion and free-breathing cardiac imaging. Magn Reson Med 1999; 42: 963-969. – reference: Park S, Park M, Kang M. Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 2003; 20: 21-36. – reference: 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. – reference: Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med 1995; 34: 910-914. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: 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. – reference: Greenspan H. Super-resolution in medical imaging. Comput J 2009; 52: 43-63. – reference: Robinson D, Milanfar P. Statistical performance analysis of super-resolution. IEEE Trans Image Process 2006; 15: 1413-1428. – reference: Haacke EM, Brown RW, Thomson MR, Venkatesan R. Magnetic resonance imaging. Physical principles and sequence design. New York: Wiley; 1999. – reference: Irani M, Peleg S. Improving resolution by image registration. CVGIP: Graph Models Image Process 1991; 53: 231-239. – reference: Peled S, Yeshurun Y. Superresolution in MRI - Perhaps sometimes. Magn Reson Med 2002; 48: 409-409. – reference: 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. – reference: Lin Z, Shum HY. Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans Pattern Anal Mach Intell 2004; 26: 83-97. – reference: Scheffler K. Superresolution in MRI? Magn Reson Med 2002; 48: 408. – start-page: 615 year: 2010 end-page: 622 – volume: 29 start-page: 471 year: 1970 end-page: 481 article-title: Algebraic reconstruction techniques (ART) for three‐dimensional electron microscopy and X‐ray photography publication-title: J Theor Biol – volume: 42 start-page: 963 year: 1999 end-page: 969 article-title: Motion correction with PROPELLER MRI: Application to head motion and free‐breathing cardiac imaging publication-title: Magn Reson Med – volume: 13 start-page: 2323 year: 2003 end-page: 2337 article-title: A brief review of parallel magnetic resonance imaging publication-title: Eur Radiol – volume: 6 start-page: 1715 year: 1989 end-page: 1726 article-title: High‐resolution image recovery from image‐plane arrays, using convex projections publication-title: J Opt Soc Am A – year: 2001 – volume: 20 start-page: 437 year: 2002 end-page: 446 article-title: MRI inter‐slice reconstruction using super‐resolution publication-title: Magn Reson Imaging – volume: 14 start-page: 131 year: 2004 end-page: 138 article-title: The use of superresolution techniques to reduce slice thickness in functional MRI publication-title: Int J Imag Syst Tech – start-page: 355 year: 2010 end-page: 362 – volume: 1 start-page: 317 year: 1984 end-page: 339 – volume: 26 start-page: 83 year: 2004 end-page: 97 article-title: Fundamental limits of reconstruction‐based superresolution algorithms under local translation publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 48 start-page: 408 year: 2002 article-title: Superresolution in MRI? publication-title: Magn Reson Med – volume: 52 start-page: 43 year: 2009 end-page: 63 article-title: Super‐resolution in medical imaging publication-title: Comput J – volume: 19 start-page: 288 year: 2006 end-page: 299 article-title: Encoding and reconstruction in parallel MRI publication-title: NMR Biomed – year: 1998 – volume: 20 start-page: 21 year: 2003 end-page: 36 article-title: Super‐resolution image reconstruction: a technical overview publication-title: IEEE Signal Process Mag – volume: 58 start-page: 1182 year: 2007 end-page: 1195 article-title: Sparse MRI: the application of compressed sensing for rapid MR imaging publication-title: Magn Reson Med – volume: 15 start-page: 1413 year: 2006 end-page: 1428 article-title: Statistical performance analysis of super‐resolution publication-title: IEEE Trans Image Process – start-page: 355 year: 1937 end-page: 357 article-title: Angenäherte Auflösung von Systemen linearer Gleichungen publication-title: Bull Acad Pol Sci Lett A – volume: 45 start-page: 29 year: 2001 end-page: 35 article-title: Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging publication-title: Magn Reson Med – start-page: 51 year: 2008 end-page: 56 – volume: 2007 start-page: 1 year: 2007 end-page: 12 article-title: Performance evaluation of super‐resolution reconstruction methods on real‐world data publication-title: EURASIP J Adv Signal Process – volume: 28 start-page: 633 year: 2009 end-page: 644 article-title: A super‐resolution framework for 3‐D high‐resolution and high‐contrast imaging using 2‐D multislice MRI publication-title: IEEE Trans Med Imaging – volume: 6 start-page: 1646 year: 1997 end-page: 1658 article-title: Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images publication-title: IEEE Trans Image Process – volume: 34 start-page: 910 year: 1995 end-page: 914 article-title: The Rician distribution of noisy MRI data publication-title: Magn Reson Med – volume: 48 start-page: 409 year: 2002 end-page: 409 article-title: Superresolution in MRI — Perhaps sometimes publication-title: Magn Reson Med – volume: 13 start-page: 1072 year: 2006 end-page: 1081 article-title: Registration‐based approach for reconstruction of high‐resolution in utero fetal MR brain images publication-title: Acad Radiol – volume: 29 start-page: 1739 year: 2010 end-page: 1758 article-title: Robust super‐resolution volume reconstruction from slice acquisitions: Application to fetal brain MRI publication-title: IEEE Trans Med Imaging – year: 1999 – volume: 53 start-page: 231 year: 1991 end-page: 239 article-title: Improving resolution by image registration publication-title: CVGIP: Graph Models Image Process – start-page: 645 year: 2001 end-page: 650 – volume: 42 start-page: 1 year: 1973 end-page: 32 article-title: ART: Mathematics and applications. 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| Snippet | 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... 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... |
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| SubjectTerms | Algorithms Image Enhancement - instrumentation Image Enhancement - methods Image Interpretation, Computer-Assisted - instrumentation Image Interpretation, Computer-Assisted - methods image quality magnetic resonance imaging Magnetic Resonance Imaging - instrumentation Magnetic Resonance Imaging - methods Phantoms, Imaging reconstruction Reproducibility of Results Sensitivity and Specificity Signal-To-Noise Ratio super-resolution |
| Title | Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? |
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