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Guan X, Lancione M, Ayton S, Dusek P, Langkammer C, Zhang M. Neuroimaging of Parkinson's disease by quantitative susceptibility mapping. Neuroimage 2024; 289:120547. [PMID: 38373677 DOI: 10.1016/j.neuroimage.2024.120547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 02/02/2024] [Accepted: 02/17/2024] [Indexed: 02/21/2024] Open
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Scott Ayton
- Florey Institute, The University of Melbourne, Australia
| | - Petr Dusek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czechia; Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Auenbruggerplatz 22, Prague 8036, Czechia
| | | | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou 31009, China.
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2
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Mao A, Flassbeck S, Gultekin C, Assländer J. Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI. ARXIV 2023:arXiv:2305.00326v2. [PMID: 37961734 PMCID: PMC10635289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression. Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with negligible cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications, permitting the use of smaller basis sizes in subspace reconstruction and offering significant computational savings.
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Affiliation(s)
- Andrew Mao
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
| | | | - Cem Gultekin
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012
| | - Jakob Assländer
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
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3
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Belsley G, Tyler DJ, Robson MD, Tunnicliffe EM. Optimal flip angles for in vivo liver 3D T 1 mapping and B 1+ mapping at 3T. Magn Reson Med 2023; 90:950-962. [PMID: 37125661 PMCID: PMC10952198 DOI: 10.1002/mrm.29683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 04/01/2023] [Accepted: 04/10/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE The spoiled gradient recalled echo (SPGR) sequence with variable flip angles (FAs) enables whole liverT 1 $$ {T}_1 $$ mapping at high spatial resolutions but is strongly affected byB 1 + $$ {B}_1^{+} $$ inhomogeneities. The aim of this work was to study how the precision of acquiredT 1 $$ {T}_1 $$ maps is affected by theT 1 $$ {T}_1 $$ andB 1 + $$ {B}_1^{+} $$ ranges observed in the liver at 3T, as well as how noise propagates from the acquired signals into the resultingT 1 $$ {T}_1 $$ map. THEORY TheT 1 $$ {T}_1 $$ variance was estimated through the Fisher information matrix with a total noise variance including, for the first time, theB 1 + $$ {B}_1^{+} $$ map noise as well as contributions from the SPGR noise. METHODS Simulations were used to find the optimal FAs for both theB 1 + $$ {B}_1^{+} $$ mapping andT 1 $$ {T}_1 $$ mapping. The simulations results were validated in 10 volunteers. RESULTS Four optimized SPGR FAs of 2°, 2°, 15°, and 15° (TR = 4.1 ms) andB 1 + $$ {B}_1^{+} $$ map FAs of 65° and 130° achieved aT 1 $$ {T}_1 $$ coefficient of variation of 6.2 ± 1.7% across 10 volunteers and validated our theoretical model. Four optimal FAs outperformed five uniformly spaced FAs, saving the patient one breath-hold. For the liverB 1 + $$ {B}_1^{+} $$ andT 1 $$ {T}_1 $$ parameter space at 3T, a higher return inT 1 $$ {T}_1 $$ precision was obtained by investing FAs in the SPGR acquisition rather than in theB 1 + $$ {B}_1^{+} $$ map. CONCLUSION A novel framework was developed and validated to calculate the SPGRT 1 $$ {T}_1 $$ variance. This framework efficiently identifies optimal FA values and determines the total number of SPGR andB 1 + $$ {B}_1^{+} $$ measurements needed to achieve a desiredT 1 $$ {T}_1 $$ precision.
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Affiliation(s)
- Gabriela Belsley
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
| | - Damian J. Tyler
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
| | - Matthew D. Robson
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- PerspectumOxfordUK
| | - Elizabeth M. Tunnicliffe
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
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4
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Scope Crafts E, Lu H, Ye H, Wald LL, Zhao B. An efficient approach to optimal experimental design for magnetic resonance fingerprinting with B‐splines. Magn Reson Med 2022; 88:239-253. [DOI: 10.1002/mrm.29212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/30/2022] [Accepted: 02/08/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Evan Scope Crafts
- Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas USA
| | - Hengfa Lu
- Department of Biomedical Engineering University of Texas at Austin Austin Texas USA
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering Zhejiang University Hangzhou Zhejiang China
- Center for Brain Imaging Science and Technology Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University Hangzhou Zhejiang China
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA
- Department of Radiology Harvard Medical School Boston Massachusetts USA
- Harvard‐MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Bo Zhao
- Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas USA
- Department of Biomedical Engineering University of Texas at Austin Austin Texas USA
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Zibetti MVW, Sharafi A, Regatte RR. Optimization of spin-lock times in T 1ρ mapping of knee cartilage: Cramér-Rao bounds versus matched sampling-fitting. Magn Reson Med 2022; 87:1418-1434. [PMID: 34738252 PMCID: PMC8822470 DOI: 10.1002/mrm.29063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To compare different optimization approaches for choosing the spin-lock times (TSLs), in spin-lattice relaxation time in the rotating frame (T1ρ ) mapping. METHODS Optimization criteria for TSLs based on Cramér-Rao lower bounds (CRLB) are compared with matched sampling-fitting (MSF) approaches for T1ρ mapping on synthetic data, model phantoms, and knee cartilage. The MSF approaches are optimized using robust methods for noisy cost functions. The MSF approaches assume that optimal TSLs depend on the chosen fitting method. An iterative non-linear least squares (NLS) and artificial neural networks (ANN) are tested as two possible T1ρ fitting methods for MSF approaches. RESULTS All optimized criteria were better than non-optimized ones. However, we observe that a modified CRLB and an MSF based on the mean of the normalized absolute error (MNAE) were more robust optimization approaches, performing well in all tested cases. The optimized TSLs obtained the best performance with synthetic data (3.5-8.0% error), model phantoms (1.5-2.8% error), and healthy volunteers (7.7-21.1% error), showing stable and improved quality results, comparing to non-optimized approaches (4.2-13.3% error on synthetic data, 2.1-6.2% error on model phantoms, 9.8-27.8% error on healthy volunteers). CONCLUSION A modified CRLB and the MSF based on MNAE are robust optimization approaches for choosing TSLs in T1ρ mapping. All optimized criteria allowed good results even using rapid scans with two TSLs when a complex-valued fitting is done with iterative NLS or ANN.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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6
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Leitão D, Teixeira RPAG, Price A, Uus A, Hajnal JV, Malik SJ. Efficiency analysis for quantitative MRI of T1 and T2 relaxometry methods. Phys Med Biol 2021; 66:15NT02. [PMID: 34192676 PMCID: PMC8312556 DOI: 10.1088/1361-6560/ac101f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/12/2021] [Accepted: 06/30/2021] [Indexed: 11/11/2022]
Abstract
This study presents a comparison of quantitative MRI methods based on an efficiency metric that quantifies their intrinsic ability to extract information about tissue parameters. Under a regime of unbiased parameter estimates, an intrinsic efficiency metricηwas derived for fully-sampled experiments which can be used to both optimize and compare sequences. Here we optimize and compare several steady-state and transient gradient-echo based qMRI methods, such as magnetic resonance fingerprinting (MRF), for jointT1andT2mapping. The impact of undersampling was also evaluated, assuming incoherent aliasing that is treated as noise by parameter estimation.In vivovalidation of the efficiency metric was also performed. Transient methods such as MRF can be up to 3.5 times more efficient than steady-state methods, when spatial undersampling is ignored. If incoherent aliasing is treated as noise during least-squares parameter estimation, the efficiency is reduced in proportion to the SNR of the data, with reduction factors of 5 often seen for practical SNR levels.In vivovalidation showed a very good agreement between the theoretical and experimentally predicted efficiency. This work presents and validates an efficiency metric to optimize and compare the performance of qMRI methods. Transient methods were found to be intrinsically more efficient than steady-state methods, however the effect of spatial undersampling can significantly erode this advantage.
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Affiliation(s)
- David Leitão
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Communication Address: Perinatal Imaging and Health 1st Floor South Wing, St Thomas’ Hospital London SE1 7EHUK, United Kingdom
| | - Rui Pedro A. G. Teixeira
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Anthony Price
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Shaihan J. Malik
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
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7
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Mitchell DP, Hwang KP, Bankson JA, Jason Stafford R, Banerjee S, Takei N, Fuentes D. An information theory model for optimizing quantitative magnetic resonance imaging acquisitions. Phys Med Biol 2020; 65:225008. [PMID: 32947269 DOI: 10.1088/1361-6560/abb9f6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Acquisition parameter selection is currently performed empirically for many quantitative MRI (qMRI) acquisitions. Tuning parameters for different scan times, tissues, and resolutions requires some amount of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to minimize variability of quantitative maps and post-processing techniques such as synthetic image generation. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for qMRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of potential acquisitions and allowing informed parameter selection. The information theory framework was tested on artificial data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The phantom measurements showed that higher mutual information calculated by the model correlated with smaller coefficient of variation in the reconstructed parametric maps, and in vivo measurements demonstrated that information-based calibration of acquisition parameters resulted in a decrease in parametric map variability consistent with model predictions.
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Affiliation(s)
- Drew P Mitchell
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
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8
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Whitaker ST, Nataraj G, Nielsen JF, Fessler JA. Myelin water fraction estimation using small-tip fast recovery MRI. Magn Reson Med 2020; 84:1977-1990. [PMID: 32281179 PMCID: PMC7478173 DOI: 10.1002/mrm.28259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 02/05/2020] [Accepted: 02/26/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE To demonstrate the feasibility of an optimized set of small-tip fast recovery (STFR) MRI scans for rapidly estimating myelin water fraction (MWF) in the brain. METHODS We optimized a set of STFR scans to minimize the Cramér-Rao Lower Bound of MWF estimates. We evaluated the RMSE of MWF estimates from the optimized scans in simulation. We compared STFR-based MWF estimates (both modeling exchange and not modeling exchange) to multi-echo spin echo (MESE)-based estimates. We used the optimized scans to acquire in vivo data from which a MWF map was estimated. We computed the STFR-based MWF estimates using PERK, a recently developed kernel regression technique, and the MESE-based MWF estimates using both regularized non-negative least squares (NNLS) and PERK. RESULTS In simulation, the optimized STFR scans led to estimates of MWF with low RMSE across a range of tissue parameters and across white matter and gray matter. The STFR-based MWF estimates that modeled exchange compared well to MESE-based MWF estimates in simulation. When the optimized scans were tested in vivo, the MWF map that was estimated using a 3-compartment model with exchange was closer to the MESE-based MWF map. CONCLUSIONS The optimized STFR scans appear to be well suited for estimating MWF in simulation and in vivo when we model exchange in training. In this case, the STFR-based MWF estimates are close to the MESE-based estimates.
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Affiliation(s)
- Steven T. Whitaker
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
| | - Gopal Nataraj
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jon-Fredrik Nielsen
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Jeffrey A. Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA
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9
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Tamir JI, Ong F, Anand S, Karasan E, Wang K, Lustig M. Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging. IEEE SIGNAL PROCESSING MAGAZINE 2020; 37:94-104. [PMID: 33746469 PMCID: PMC7977016 DOI: 10.1109/msp.2019.2940062] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite differences, and low rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the image formation. On the other hand, MRI signal dynamics are governed by physical laws, which can be explicitly modeled and used as priors for reconstruction. These explicit and implicit signal priors can be synergistically combined in an inverse problem framework to recover sharp, multi-contrast images from highly accelerated scans. Furthermore, the physics-based constraints provide a recipe for recovering quantitative, bio-physical parameters from the data. This article introduces physics-based modeling constraints in MRI and shows how they can be used in conjunction with compressed sensing for image reconstruction and quantitative imaging. We describe model-based quantitative MRI, as well as its linear subspace approximation. We also discuss approaches to selecting user-controllable scan parameters given knowledge of the physical model. We present several MRI applications that take advantage of this framework for the purpose of multi-contrast imaging and quantitative mapping.
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Affiliation(s)
- Jonathan I Tamir
- Department of Electrical Engineering and Computer Sciences, University of California
| | - Frank Ong
- Department of Electrical Engineering, Stanford University
| | - Suma Anand
- Department of Electrical Engineering and Computer Sciences, University of California
| | - Ekin Karasan
- Department of Electrical Engineering and Computer Sciences, University of California
| | - Ke Wang
- Department of Electrical Engineering and Computer Sciences, University of California
| | - Michael Lustig
- Department of Electrical Engineering and Computer Sciences, University of California
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10
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Lahiri A, Fessler JA, Hernandez‐Garcia L. Optimizing MRF‐ASL scan design for precise quantification of brain hemodynamics using neural network regression. Magn Reson Med 2019; 83:1979-1991. [DOI: 10.1002/mrm.28051] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 09/13/2019] [Accepted: 10/05/2019] [Indexed: 01/02/2023]
Affiliation(s)
- Anish Lahiri
- Department of Electrical and Computer Engineering University of Michigan Ann Arbor Michigan USA
| | - Jeffrey A. Fessler
- Department of Electrical and Computer Engineering University of Michigan Ann Arbor Michigan USA
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11
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Zhao B, Haldar JP, Liao C, Ma D, Jiang Y, Griswold MA, Setsompop K, Wald LL. Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramér-Rao Bound Meets Spin Dynamics. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:844-861. [PMID: 30295618 PMCID: PMC6447464 DOI: 10.1109/tmi.2018.2873704] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Magnetic resonance (MR) fingerprinting is a new quantitative imaging paradigm, which simultaneously acquires multiple MR tissue parameter maps in a single experiment. In this paper, we present an estimation-theoretic framework to perform experiment design for MR fingerprinting. Specifically, we describe a discrete-time dynamic system to model spin dynamics, and derive an estimation-theoretic bound, i.e., the Cramér-Rao bound, to characterize the signal-to-noise ratio (SNR) efficiency of an MR fingerprinting experiment. We then formulate an optimal experiment design problem, which determines a sequence of acquisition parameters to encode MR tissue parameters with the maximal SNR efficiency, while respecting the physical constraints and other constraints from the image decoding/reconstruction process. We evaluate the performance of the proposed approach with numerical simulations, phantom experiments, and in vivo experiments. We demonstrate that the optimized experiments substantially reduce data acquisition time and/or improve parameter estimation. For example, the optimized experiments achieve about a factor of two improvement in the accuracy of T2 maps, while keeping similar or slightly better accuracy of T1 maps. Finally, as a remarkable observation, we find that the sequence of optimized acquisition parameters appears to be highly structured rather than randomly/pseudo-randomly varying as is prescribed in the conventional MR fingerprinting experiments.
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Affiliation(s)
- Bo Zhao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115 USA
| | - Justin P. Haldar
- Signal and Image Processing Institute and Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang Province 310027 China
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115 USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115 USA, and also with the Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
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12
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Ramos-Llorden G, Vegas-Sanchez-Ferrero G, Bjork M, Vanhevel F, Parizel PM, San Jose Estepar R, den Dekker AJ, Sijbers J. NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRI Mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2414-2427. [PMID: 29993537 PMCID: PMC6277233 DOI: 10.1109/tmi.2018.2833288] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
In quantitative magnetic resonance mapping, the variable flip angle (VFA) steady state spoiled gradient recalled echo (SPGR) imaging technique is popular as it provides a series of high resolution weighted images in a clinically feasible time. Fast, linear methods that estimate maps from these weighted images have been proposed, such as DESPOT1 and iterative re-weighted linear least squares. More accurate, non-linear least squares (NLLS) estimators are in play, but these are generally much slower and require careful initialization. In this paper, we present NOVIFAST, a novel NLLS-based algorithm specifically tailored to VFA SPGR mapping. By exploiting the particular structure of the SPGR model, a computationally efficient, yet accurate and precise map estimator is derived. Simulation and in vivo human brain experiments demonstrate a twenty-fold speed gain of NOVIFAST compared with conventional gradient-based NLLS estimators while maintaining a high precision and accuracy. Moreover, NOVIFAST is eight times faster than the efficient implementations of the variable projection (VARPRO) method. Furthermore, NOVIFAST is shown to be robust against initialization.
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13
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Bipin Mehta B, Coppo S, Frances McGivney D, Ian Hamilton J, Chen Y, Jiang Y, Ma D, Seiberlich N, Gulani V, Alan Griswold M. Magnetic resonance fingerprinting: a technical review. Magn Reson Med 2018; 81:25-46. [PMID: 30277265 DOI: 10.1002/mrm.27403] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 05/01/2018] [Accepted: 05/21/2018] [Indexed: 01/31/2023]
Abstract
Multiparametric quantitative imaging is gaining increasing interest due to its widespread advantages in clinical applications. Magnetic resonance fingerprinting is a recently introduced approach of fast multiparametric quantitative imaging. In this article, magnetic resonance fingerprinting acquisition, dictionary generation, reconstruction, and validation are reviewed.
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Affiliation(s)
- Bhairav Bipin Mehta
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Simone Coppo
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Debra Frances McGivney
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Jesse Ian Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Yong Chen
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Yun Jiang
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Dan Ma
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Mark Alan Griswold
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
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14
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Nataraj G, Nielsen JF, Scott C, Fessler JA. Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2103-2114. [PMID: 29994085 PMCID: PMC7017957 DOI: 10.1109/tmi.2018.2817547] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these parameter-measurement pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. PERK admits a simple implementation as per-voxel nonlinear lifting of MRI measurements followed by linear minimum mean-squared error regression. We demonstrate PERK for $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimation, a well-studied application where it is simple to compare PERK estimates against dictionary-based grid search estimates and iterative optimization estimates. Numerical simulations as well as single-slice phantom and in vivo experiments demonstrate that PERK and other tested methods produce comparable $ {\textit {T}_{1}}, {\textit {T}_{2}}$ estimates in white and gray matter, but PERK is consistently at least $140\times $ faster. This acceleration factor may increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel.
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Affiliation(s)
- Gopal Nataraj
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jon-Fredrik Nielsen
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Clayton Scott
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Jeffrey A. Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
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