Multiscale reconstruction for MR fingerprinting
Purpose To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative‐denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel‐wise f...
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| Vydáno v: | Magnetic resonance in medicine Ročník 75; číslo 6; s. 2481 - 2492 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Blackwell Publishing Ltd
01.06.2016
Wiley Subscription Services, Inc |
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| ISSN: | 0740-3194, 1522-2594, 1522-2594 |
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| Abstract | Purpose
To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting.
Methods
An iterative‐denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel‐wise fidelity to the best‐matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable‐density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method.
Results
The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three‐fold reduction in acquisition time.
Conclusion
The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481–2492, 2016. © 2015 Wiley Periodicals, Inc. |
|---|---|
| AbstractList | To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting.PURPOSETo reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting.An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method.METHODSAn iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method.The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time.RESULTSThe proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time.The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc.CONCLUSIONThe proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc. Purpose To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. Conclusion The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc. To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc. Purpose To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B sub(0) field variations in the brain was achieved in vivo for a 256 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. Conclusion The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. Purpose To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative‐denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel‐wise fidelity to the best‐matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable‐density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three‐fold reduction in acquisition time. Conclusion The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481–2492, 2016. © 2015 Wiley Periodicals, Inc. |
| Author | Ma, Dan Badve, Chaitra Pierre, Eric Y. Griswold, Mark A. Chen, Yong |
| Author_xml | – sequence: 1 givenname: Eric Y. surname: Pierre fullname: Pierre, Eric Y. organization: Department of Biomedical Engineering, Case Western Reserve University, Ohio, Cleveland, USA – sequence: 2 givenname: Dan surname: Ma fullname: Ma, Dan organization: Department of Biomedical Engineering, Case Western Reserve University, Ohio, Cleveland, USA – sequence: 3 givenname: Yong surname: Chen fullname: Chen, Yong organization: Department of Radiology, Case Western Reserve University & University Hospitals, Ohio, Cleveland, USA – sequence: 4 givenname: Chaitra surname: Badve fullname: Badve, Chaitra organization: Department of Radiology, Case Western Reserve University & University Hospitals, Ohio, Cleveland, USA – sequence: 5 givenname: Mark A. surname: Griswold fullname: Griswold, Mark A. email: mark.griswold@case.edu organization: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26132462$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1097/00004728-199803000-00032 10.1109/TMI.2009.2023119 10.1109/ISBI.2007.357020 10.1109/TSP.2006.881199 10.1002/mrm.23128 10.1109/IEMBS.2010.5627889 10.1093/biomet/81.3.425 10.2307/2532051 10.1016/0005-1098(75)90044-8 10.1002/(SICI)1522-2594(200005)43:5<682::AID-MRM10>3.0.CO;2-G 10.1038/nature11971 10.1109/MSP.2007.914728 10.1016/j.mri.2014.02.022 10.1016/j.neuroimage.2006.03.052 10.1002/mrm.10658 10.1002/mrm.22483 |
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| Keywords | Parameter Mapping Fingerprinting Multiscale Image Reconstruction Compressed Sensing |
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| References | Doneva M, Börnert P, Eggers H, Stehning C, Sénégas J, Mertins A. Compressed sensing reconstruction for magnetic resonance parameter mapping. Magn Reson Med 2010;64:1114-1120. Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 2006;54:4311-4322. Huang C, Graff CG, Clarkson EW, Bilgin A, Altbach MI. T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing. Magn Reson Med 2012;67:1355-1366. Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, Griswold MA. Magnetic resonance fingerprinting. Nature 2013;495:187-192. Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC. Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr 1998;22:324-333. Lin L. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989;45:355-368. Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994;81:425-455. Aubert-Broche B, Evans AC, Collins L. A new improved version of the realistic digital brain phantom. Neuroimage 2006;32:138-145. Saybasili H, Herzka DA, Seiberlich N, Griswold MA. Real-time imaging with radial GRAPPA: implementation on a heterogeneous architecture for low-latency reconstructions. Magn Reson Imaging 2014;32:747-758. Lustig M, Donoho DL, Santos JM, Pauly JM. Compressed sensing MRI. IEEE Signal Process Mag 2008;25:72-82. Hennig J, Weigel M, Scheffler K. Calculation of flip angles for echo trains with predefined amplitudes with the extended phase graph (EPG)-algorithm: principles and applications to hyperecho and TRAPS sequences. Magn Reson Med 2004;51:68-80. Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med 2000;43:682-690. Otsu N. A tlreshold selection method from gray-level histograms. Automatica 1975;11:285-296. Block KT, Uecker M, Frahm J. Model-based iterative reconstruction for radial fast spin-echo MRI. IEEE Trans Med Imaging 2009;28:1759-1769. 2010; 64 2004; 51 1989; 45 2010 2006; 54 2006; 32 2000; 43 2008; 25 2007 2013; 495 1975; 11 2014 1994; 81 2012; 67 1998; 22 2014; 32 2009; 28 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 |
| References_xml | – reference: Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, Griswold MA. Magnetic resonance fingerprinting. Nature 2013;495:187-192. – reference: Doneva M, Börnert P, Eggers H, Stehning C, Sénégas J, Mertins A. Compressed sensing reconstruction for magnetic resonance parameter mapping. Magn Reson Med 2010;64:1114-1120. – reference: Saybasili H, Herzka DA, Seiberlich N, Griswold MA. Real-time imaging with radial GRAPPA: implementation on a heterogeneous architecture for low-latency reconstructions. Magn Reson Imaging 2014;32:747-758. – reference: Huang C, Graff CG, Clarkson EW, Bilgin A, Altbach MI. T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing. Magn Reson Med 2012;67:1355-1366. – reference: Donoho DL, Johnstone IM. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994;81:425-455. – reference: Lin L. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989;45:355-368. – reference: Hennig J, Weigel M, Scheffler K. Calculation of flip angles for echo trains with predefined amplitudes with the extended phase graph (EPG)-algorithm: principles and applications to hyperecho and TRAPS sequences. Magn Reson Med 2004;51:68-80. – reference: Block KT, Uecker M, Frahm J. Model-based iterative reconstruction for radial fast spin-echo MRI. IEEE Trans Med Imaging 2009;28:1759-1769. – reference: Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC. Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr 1998;22:324-333. – reference: Otsu N. A tlreshold selection method from gray-level histograms. Automatica 1975;11:285-296. – reference: Lustig M, Donoho DL, Santos JM, Pauly JM. Compressed sensing MRI. IEEE Signal Process Mag 2008;25:72-82. – reference: Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction of phased array MR imagery. Magn Reson Med 2000;43:682-690. – reference: Aubert-Broche B, Evans AC, Collins L. A new improved version of the realistic digital brain phantom. Neuroimage 2006;32:138-145. – reference: Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 2006;54:4311-4322. – volume: 25 start-page: 72 year: 2008 end-page: 82 article-title: Compressed sensing MRI publication-title: IEEE Signal Process Mag – volume: 11 start-page: 285 year: 1975 end-page: 296 article-title: A tlreshold selection method from gray‐level histograms publication-title: Automatica – volume: 495 start-page: 187 year: 2013 end-page: 192 article-title: Magnetic resonance fingerprinting publication-title: Nature – volume: 22 start-page: 324 year: 1998 end-page: 333 article-title: Enhancement of MR images using registration for signal averaging publication-title: J Comput Assist Tomogr – year: 2007 – volume: 54 start-page: 4311 year: 2006 end-page: 4322 article-title: SVD: an algorithm for designing overcomplete dictionaries for sparse representation publication-title: IEEE Trans Signal Process – volume: 45 start-page: 355 year: 1989 end-page: 368 article-title: A concordance correlation coefficient to evaluate reproducibility publication-title: Biometrics – volume: 32 start-page: 747 year: 2014 end-page: 758 article-title: Real‐time imaging with radial GRAPPA: implementation on a heterogeneous architecture for low‐latency reconstructions publication-title: Magn Reson Imaging – volume: 81 start-page: 425 year: 1994 end-page: 455 article-title: Ideal spatial adaptation by wavelet shrinkage publication-title: Biometrika – start-page: 871 year: 2010 end-page: 874 – volume: 32 start-page: 138 year: 2006 end-page: 145 article-title: A new improved version of the realistic digital brain phantom publication-title: Neuroimage – volume: 43 start-page: 682 year: 2000 end-page: 690 article-title: Adaptive reconstruction of phased array MR imagery publication-title: Magn Reson Med – volume: 64 start-page: 1114 year: 2010 end-page: 1120 article-title: Compressed sensing reconstruction for magnetic resonance parameter mapping publication-title: Magn Reson Med – volume: 67 start-page: 1355 year: 2012 end-page: 1366 article-title: T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing publication-title: Magn Reson Med – volume: 51 start-page: 68 year: 2004 end-page: 80 article-title: Calculation of flip angles for echo trains with predefined amplitudes with the extended phase graph (EPG)‐algorithm: principles and applications to hyperecho and TRAPS sequences publication-title: Magn Reson Med – volume: 28 start-page: 1759 year: 2009 end-page: 1769 article-title: Model‐based iterative reconstruction for radial fast spin‐echo MRI publication-title: IEEE Trans Med Imaging – year: 2014 – ident: e_1_2_7_12_1 doi: 10.1097/00004728-199803000-00032 – ident: e_1_2_7_6_1 doi: 10.1109/TMI.2009.2023119 – ident: e_1_2_7_7_1 doi: 10.1109/ISBI.2007.357020 – ident: e_1_2_7_9_1 doi: 10.1109/TSP.2006.881199 – ident: e_1_2_7_5_1 doi: 10.1002/mrm.23128 – ident: e_1_2_7_8_1 doi: 10.1109/IEMBS.2010.5627889 – ident: e_1_2_7_10_1 doi: 10.1093/biomet/81.3.425 – ident: e_1_2_7_14_1 – ident: e_1_2_7_17_1 doi: 10.2307/2532051 – ident: e_1_2_7_19_1 – ident: e_1_2_7_16_1 doi: 10.1016/0005-1098(75)90044-8 – ident: e_1_2_7_15_1 doi: 10.1002/(SICI)1522-2594(200005)43:5<682::AID-MRM10>3.0.CO;2-G – ident: e_1_2_7_2_1 doi: 10.1038/nature11971 – ident: e_1_2_7_11_1 doi: 10.1109/MSP.2007.914728 – ident: e_1_2_7_18_1 doi: 10.1016/j.mri.2014.02.022 – ident: e_1_2_7_13_1 doi: 10.1016/j.neuroimage.2006.03.052 – ident: e_1_2_7_3_1 doi: 10.1002/mrm.10658 – ident: e_1_2_7_4_1 doi: 10.1002/mrm.22483 |
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To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting.
Methods
An iterative‐denoising... To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. An iterative-denoising algorithm is... Purpose To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising... To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting.PURPOSETo reduce the acquisition time needed to... |
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| SubjectTerms | Algorithms Brain - diagnostic imaging Compressed Sensing Fingerprinting Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Multiscale Image Reconstruction Parameter Mapping Phantoms, Imaging |
| Title | Multiscale reconstruction for MR fingerprinting |
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