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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| Published in: | Magnetic resonance in medicine Vol. 75; no. 6; pp. 2481 - 2492 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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United States
Blackwell Publishing Ltd
01.06.2016
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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 |
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| Keywords | Parameter Mapping Fingerprinting Multiscale Image Reconstruction Compressed Sensing |
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| 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. 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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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