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Zimphango C, Mada MO, Sawiak SJ, Giorgi-Coll S, Carpenter TA, Hutchinson PJ, Carpenter KLH, Stovell MG. In-vitro gadolinium retro-microdialysis in agarose gel-a human brain phantom study. FRONTIERS IN RADIOLOGY 2024; 4:1085834. [PMID: 38356693 PMCID: PMC10864450 DOI: 10.3389/fradi.2024.1085834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/11/2024] [Indexed: 02/16/2024]
Abstract
Rationale and objectives Cerebral microdialysis is a technique that enables monitoring of the neurochemistry of patients with significant acquired brain injury, such as traumatic brain injury (TBI) and subarachnoid haemorrhage (SAH). Cerebral microdialysis can also be used to characterise the neuro-pharmacokinetics of small-molecule study substrates using retrodialysis/retromicrodialysis. However, challenges remain: (i) lack of a simple, stable, and inexpensive brain tissue model for the study of drug neuropharmacology; and (ii) it is unclear how far small study-molecules administered via retrodialysis diffuse within the human brain. Materials and methods Here, we studied the radial diffusion distance of small-molecule gadolinium-DTPA from microdialysis catheters in a newly developed, simple, stable, inexpensive brain tissue model as a precursor for in-vivo studies. Brain tissue models consisting of 0.65% weight/volume agarose gel in two kinds of buffers were created. The distribution of a paramagnetic contrast agent gadolinium-DTPA (Gd-DTPA) perfusion from microdialysis catheters using magnetic resonance imaging (MRI) was characterized as a surrogate for other small-molecule study substrates. Results We found the mean radial diffusion distance of Gd-DTPA to be 18.5 mm after 24 h (p < 0.0001). Conclusion Our brain tissue model provides avenues for further tests and research into infusion studies using cerebral microdialysis, and consequently effective focal drug delivery for patients with TBI and other brain disorders.
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Affiliation(s)
- Chisomo Zimphango
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Marius O. Mada
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Stephen J. Sawiak
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
| | - Susan Giorgi-Coll
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - T. Adrian Carpenter
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Peter J. Hutchinson
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Keri L. H. Carpenter
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Matthew G. Stovell
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Department of Neurosurgery, The Walton Centre, Liverpool, United Kingdom
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Chen JJ, Uthayakumar B, Hyder F. Mapping oxidative metabolism in the human brain with calibrated fMRI in health and disease. J Cereb Blood Flow Metab 2022; 42:1139-1162. [PMID: 35296177 PMCID: PMC9207484 DOI: 10.1177/0271678x221077338] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Conventional functional MRI (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping human brain activity non-invasively. Recent interest in quantitative fMRI has renewed the importance of oxidative neuroenergetics as reflected by cerebral metabolic rate of oxygen consumption (CMRO2) to support brain function. Dynamic CMRO2 mapping by calibrated fMRI require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and/or volume (CBV). In human subjects this "calibration" is typically performed using a gas mixture containing small amounts of carbon dioxide and/or oxygen-enriched medical air, which are thought to produce changes in CBF (and CBV) and BOLD signal with minimal or no CMRO2 changes. However non-human studies have demonstrated that the "calibration" can also be achieved without gases, revealing good agreement between CMRO2 changes and underlying neuronal activity (e.g., multi-unit activity and local field potential). Given the simpler set-up of gas-free calibrated fMRI, there is evidence of recent clinical applications for this less intrusive direction. This up-to-date review emphasizes technological advances for such translational gas-free calibrated fMRI experiments, also covering historical progression of the calibrated fMRI field that is impacting neurological and neurodegenerative investigations of the human brain.
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Affiliation(s)
- J Jean Chen
- Medical Biophysics, University of Toronto, Toronto, Canada.,Rotman Research Institute, Baycrest, Toronto, Canada
| | - Biranavan Uthayakumar
- Medical Biophysics, University of Toronto, Toronto, Canada.,Sunnybrook Research Institute, Toronto, Canada
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, USA.,Department of Radiology, Yale University, New Haven, Connecticut, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Research Program, Yale University, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
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3
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Wang F, Dong Z, Reese TG, Rosen B, Wald LL, Setsompop K. 3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI. Neuroimage 2022; 250:118963. [PMID: 35122969 PMCID: PMC8920906 DOI: 10.1016/j.neuroimage.2022.118963] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 12/09/2021] [Accepted: 02/01/2022] [Indexed: 12/11/2022] Open
Abstract
Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1+ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures.
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Affiliation(s)
- Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Timothy G Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA; Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA; Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, USA; Department of Electrical Engineering, Stanford University, Stanford, USA
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4
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Barbieri M, Lee PK, Brizi L, Giampieri E, Solera F, Castellani G, Hargreaves BA, Testa C, Lodi R, Remondini D. Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach. NMR IN BIOMEDICINE 2022; 35:e4670. [PMID: 35088466 DOI: 10.1002/nbm.4670] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.
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Affiliation(s)
- Marco Barbieri
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- Department of Radiology, Stanford University, California, United States
| | - Philip K Lee
- Department of Electrical Engineering, Stanford University, California, United States
| | - Leonardo Brizi
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- INFN, Sezione di Bologna, Bologna, Italy
| | - Enrico Giampieri
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | | | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, California, United States
- Department of Electrical Engineering, Stanford University, California, United States
- Department of Bioengineering, Stanford University, California, United States
| | - Claudia Testa
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
| | - Raffaele Lodi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- INFN, Sezione di Bologna, Bologna, Italy
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A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations. Phys Med 2021; 89:80-92. [PMID: 34352679 DOI: 10.1016/j.ejmp.2021.07.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 11/22/2022] Open
Abstract
MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1, and IR-bSSFP-B1. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.
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Khajehim M, Christen T, Tam F, Graham SJ. Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation. Neuroimage 2021; 238:118237. [PMID: 34091035 DOI: 10.1016/j.neuroimage.2021.118237] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 01/02/2023] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. This study aims to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1+ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm3 and 1.7×1.7×3 mm3, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R = 1-1.04, R2>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R2>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4 ± 0.4%, 3.6 ± 0.3% and 6.0 ± 0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.
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Affiliation(s)
- Mahdi Khajehim
- Department of Medical Biophysics, University of Toronto, 101 College St Suite 15-701, Toronto, ON M5G 1L7, Canada.
| | - Thomas Christen
- Grenoble Institute of Neuroscience, Inserm, Grenoble, France
| | - Fred Tam
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Simon J Graham
- Department of Medical Biophysics, University of Toronto, 101 College St Suite 15-701, Toronto, ON M5G 1L7, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
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Marriott A, Bowen C, Rioux J, Brewer K. Simultaneous quantification of SPIO and gadolinium contrast agents using MR fingerprinting. Magn Reson Imaging 2021; 79:121-129. [PMID: 33774098 DOI: 10.1016/j.mri.2021.03.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Develop a magnetic resonance fingerprinting (MRF) methodology with R2∗ quantification, intended for use with simultaneous contrast agent concentration mapping, particularly gadolinium (Gd) and iron labelled CD8+ T cells. METHODS Variable-density spiral SSFP MRF was used, modified to allow variable TE, and with an exp.(-TE·R2∗) dictionary modulation. In vitro phantoms containing SPIO labelled cells and/or gadolinium were used to validate parameter maps, probe undersampling capacity, and verify dual quantification capabilities. A C57BL/6 mouse was imaged using MRF to demonstrate acceptable in vivo resolution and signal at 8× undersampling necessary for a 25-min scan. RESULTS Strong agreement was found between conventional and MRF-derived values for R1, R2, and R2∗. Expanded MRF allowed quantification of iron-loaded CD8+ T cells. Results were robust to 8× undersampling and enabled recreation of relaxation profiles for both a Gd agent and iron labelled cells simultaneously. In vivo data demonstrated sufficient SNR in undersampled data for parameter mapping to visualise key features. CONCLUSION MRF can be expanded to include R1, R2, and R2∗ mapping required for simultaneous quantification of gadolinium and SPIO in vitro, allowing for potential implementation of a variety of future in vivo studies using dual MR contrast agents, including molecular imaging of labelled cells.
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Affiliation(s)
- Anna Marriott
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada
| | - Chris Bowen
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada
| | - James Rioux
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada
| | - Kimberly Brewer
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada.
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Boyacioglu R, Wang C, Ma D, McGivney DF, Yu X, Griswold MA. 3D magnetic resonance fingerprinting with quadratic RF phase. Magn Reson Med 2020; 85:2084-2094. [PMID: 33179822 DOI: 10.1002/mrm.28581] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/25/2020] [Accepted: 10/12/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To implement 3D magnetic resonance fingerprinting (MRF) with quadratic RF phase (qRF-MRF) for simultaneous quantification of T1 , T2 , ΔB0 , and T 2 ∗ . METHODS 3D MRF data with effective undersampling factor of 3 in the slice direction were acquired with quadratic RF phase patterns for T1 , T2 , and T 2 ∗ sensitivity. Quadratic RF phase encodes the off-resonance by modulating the on-resonance frequency linearly in time. Transition to 3D brings practical limitations for reconstruction and dictionary matching because of increased data and dictionary sizes. Randomized singular value decomposition (rSVD)-based compression in time and reduction in dictionary size with a quadratic interpolation method are combined to be able to process prohibitively large data sets in feasible reconstruction and matching times. RESULTS Accuracy of 3D qRF-MRF maps in various resolutions and orientations are compared to 3D fast imaging with steady-state precession (FISP) for T1 and T2 contrast and to 2D qRF-MRF for T 2 ∗ contrast and ΔB0 . The precision of 3D qRF-MRF was 1.5-2 times higher than routine clinical scans. 3D qRF-MRF ΔB0 maps were further processed to highlight the susceptibility contrast. CONCLUSION Natively co-registered 3D whole brain T1 , T2 , T 2 ∗ , ΔB0 , and QSM maps can be acquired in as short as 5 min with 3D qRF-MRF.
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Affiliation(s)
- Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Charlie Wang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Debra F McGivney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Mark A Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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9
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Kim B, Schär M, Park H, Heo HY. A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging. Neuroimage 2020; 221:117165. [DOI: 10.1016/j.neuroimage.2020.117165] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/01/2020] [Accepted: 07/09/2020] [Indexed: 01/05/2023] Open
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10
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Ma S, Wang N, Fan Z, Kaisey M, Sicotte NL, Christodoulou AG, Li D. Three-dimensional whole-brain simultaneous T1, T2, and T1ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis. Magn Reson Med 2020; 85:1938-1952. [PMID: 33107126 DOI: 10.1002/mrm.28553] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9 min. METHODS MR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in age-matched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined. RESULTS Our framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel "T1ρ-FLAIR" contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, compared to using any single parameter alone. CONCLUSION MR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases, such as MS.
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Affiliation(s)
- Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Marwa Kaisey
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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12
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Meng Y, Cheung J, Sun PZ. Improved MR fingerprinting for relaxation measurement in the presence of semisolid magnetization transfer. Magn Reson Med 2020; 84:727-737. [PMID: 31898839 PMCID: PMC7180097 DOI: 10.1002/mrm.28159] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE To characterize and minimize the magnetization transfer (MT) effect in MR fingerprinting (MRF) relaxation measurements with a 2-pool (2P) MT model of multiple tissue types. THEORY AND METHODS Semisolid MT effect in MRF was modeled using 2P Bloch-McConnell equations. The combinations of MT parameters of multiple tissues (white [WM] and gray matter [GM]) were used to build the MRF dictionary. Both 1-pool (1P) and 2P models were simulated to characterize the dependence on MT. Relaxations measured using MRF with spin-echo saturation-recovery (SR) or inversion-recovery preparations were compared with conventional SR-prepared T1 and multiple spin-echo T2 measurements. The simulations results were validated with phantoms and brain tissue samples. RESULTS The MRF signal was different from the 1P and 2P models. 1P MRF produced significantly (P < .05) underestimated T1 in WM (20-30%) and GM (7-10%), while 2P MRF measured consistent T1 and T2 in both WM and GM with conventional measurements (pairwise test P > .1; correlated P < .05). Simulations showed that SR-prepared MRF measuring T1 had much less errors against the variation of the macromolecular fraction. Compared with inversion-recovery preparation, SR-prepared MRF produced higher relaxation correlations (R > 0.9) with conventional measurements in both WM and GM across samples, suggesting that SR-prepared MRF was less sensitive to the compositive effect of multiple MT parameters variations. CONCLUSIONS 2P MRF using a combination of MT parameters for multiple tissue types can measure consistent relaxations with conventional methods. With the 2P models, SR-prepared MRF would provide an option for robust relaxation measurement under heterogeneous MT.
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Affiliation(s)
- Yuguang Meng
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
| | - Jesse Cheung
- Department of Anthropology and Human Biology, Emory University, Atlanta, GA, USA
| | - Phillip Zhe Sun
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
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13
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McGivney DF, Boyacioğlu R, Jiang Y, Poorman ME, Seiberlich N, Gulani V, Keenan KE, Griswold MA, Ma D. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020; 51:993-1007. [PMID: 31347226 PMCID: PMC6980890 DOI: 10.1002/jmri.26877] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/05/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993-1007.
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Affiliation(s)
- Debra F. McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioğlu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Megan E. Poorman
- Department of Physics, University of Colorado Boulder, Boulder, Colorado, USA
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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14
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Hermann I, Chacon-Caldera J, Brumer I, Rieger B, Weingärtner S, Schad LR, Zöllner FG. Magnetic resonance fingerprinting for simultaneous renal T 1 and T 2 * mapping in a single breath-hold. Magn Reson Med 2020; 83:1940-1948. [PMID: 31900983 DOI: 10.1002/mrm.28160] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate the use of magnetic resonance fingerprinting (MRF) for simultaneous quantification of T 1 and T 2 ∗ in a single breath-hold in the kidneys. METHODS The proposed kidney MRF sequence was based on MRF echo-planar imaging. Thirty-five measurements per slice and overall 4 slices were measured in 15.4 seconds. Group matching was performed for in-line quantification of T 1 and T 2 ∗ . Images were acquired in a phantom and 8 healthy volunteers in coronal orientation. To evaluate our approach, region of interests were drawn in the kidneys to calculate mean values and standard deviations of the T 1 and T 2 ∗ times. Precision was calculated across multiple repeated MRF scans. Gaussian filtering is applied on baseline images to improve SNR and match stability. RESULTS T 1 and T 2 ∗ times acquired with MRF in the phantom showed good agreement with reference measurements and conventional mapping methods with deviations of less than 5% for T 1 and less than 10% for T 2 ∗ . Baseline images in vivo were free of artifacts and relaxation times yielded good agreement with conventional methods and literature (deviation T 1 : 7 ± 4 % , T 2 ∗ : 6 ± 3 % ). CONCLUSIONS In this feasibility study, the proposed renal MRF sequence resulted in accurate T 1 and T 2 ∗ quantification in a single breath-hold.
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Affiliation(s)
- Ingo Hermann
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, Netherlands
| | - Jorge Chacon-Caldera
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Irène Brumer
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Benedikt Rieger
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Weingärtner
- Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, Netherlands
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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15
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Anderson CE, Johansen M, Erokwu BO, Hu H, Gu Y, Zhang Y, Kavran M, Vincent J, Drumm ML, Griswold MA, Steinmetz NF, Li M, Clark H, Darrah RJ, Yu X, Brady-Kalnay SM, Flask CA. Dynamic, Simultaneous Concentration Mapping of Multiple MRI Contrast Agents with Dual Contrast - Magnetic Resonance Fingerprinting. Sci Rep 2019; 9:19888. [PMID: 31882792 PMCID: PMC6934650 DOI: 10.1038/s41598-019-56531-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 12/09/2019] [Indexed: 12/31/2022] Open
Abstract
Synchronous assessment of multiple MRI contrast agents in a single scanning session would provide a new "multi-color" imaging capability similar to fluorescence imaging but with high spatiotemporal resolution and unlimited imaging depth. This multi-agent MRI technology would enable a whole new class of basic science and clinical MRI experiments that simultaneously explore multiple physiologic/molecular events in vivo. Unfortunately, conventional MRI acquisition techniques are only capable of detecting and quantifying one paramagnetic MRI contrast agent at a time. Herein, the Dual Contrast - Magnetic Resonance Fingerprinting (DC-MRF) methodology was extended for in vivo application and evaluated by simultaneously and dynamically mapping the intra-tumoral concentration of two MRI contrast agents (Gd-BOPTA and Dy-DOTA-azide) in a mouse glioma model. Co-registered gadolinium and dysprosium concentration maps were generated with sub-millimeter spatial resolution and acquired dynamically with just over 2-minute temporal resolution. Mean tumor Gd and Dy concentration measurements from both single agent and dual agent DC-MRF studies demonstrated significant correlations with ex vivo mass spectrometry elemental analyses. This initial in vivo study demonstrates the potential for DC-MRF to provide a useful dual-agent MRI platform.
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Affiliation(s)
- Christian E Anderson
- Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mette Johansen
- Department of Molecular Biology and Microbiology, Case Western Reserve University, Cleveland, OH, USA
| | - Bernadette O Erokwu
- Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - He Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of NanoEngineering, University of California-San Diego, La Jolla, CA, USA
| | - Yuning Gu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Yifan Zhang
- Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - Michael Kavran
- Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - Jason Vincent
- Department of Molecular Biology and Microbiology, Case Western Reserve University, Cleveland, OH, USA
| | - Mitchell L Drumm
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
- Department of Pediatrics, Case Western Reserve University, Cleveland, OH, USA
| | - Mark A Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Nicole F Steinmetz
- Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of NanoEngineering, University of California-San Diego, La Jolla, CA, USA
- Department of Radiology, University of California-San Diego, La Jolla, CA, USA
- Moores Cancer Center, University of California-San Diego, La Jolla, CA, USA
| | - Ming Li
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Heather Clark
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
- Institute of Systems Bioanalysis and Chemical Imaging, Northeastern University, Boston, MA, USA
| | - Rebecca J Darrah
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
- Francis Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, OH, USA
| | - Susann M Brady-Kalnay
- Department of Molecular Biology and Microbiology, Case Western Reserve University, Cleveland, OH, USA
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, USA
| | - Chris A Flask
- Department of Radiology, Case Western Reserve University, Cleveland, OH, USA.
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Department of Pediatrics, Case Western Reserve University, Cleveland, OH, USA.
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16
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Lee PK, Watkins LE, Anderson TI, Buonincontri G, Hargreaves BA. Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations. Magn Reson Med 2019; 82:1438-1451. [PMID: 31131500 PMCID: PMC8057531 DOI: 10.1002/mrm.27832] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 05/06/2019] [Accepted: 05/08/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate a computationally efficient method for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal. METHODS Automatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. Multi-echo spin echo and DESPO T 1 were used as benchmarks to verify the CRLB implementation. The CRLB of the Magnetic Resonance Fingerprinting (MRF) sequence, which has a complicated analytical formulation, was also optimized using automatic differentiation. RESULTS The sequence parameters obtained for multi-echo spin echo and DESPO T 1 matched results obtained using conventional methods. In vivo, MRF scans demonstrate that the CRLB optimization obtained with automatic differentiation can improve performance in presence of white noise. For MRF, the CRLB optimization converges in 1.1 CPU hours for N TR = 400 and has O ( N TR ) asymptotic runtime scaling for the calculation of the CRLB objective and gradient. CONCLUSIONS Automatic differentiation can be used to optimize the CRLB of quantitative sequences without using approximations or analytical expressions. For MRF, the runtime is computationally efficient and can be used to investigate confounding factors as well as MRF sequences with a greater number of repetitions.
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Affiliation(s)
- Philip K. Lee
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Lauren E. Watkins
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Guido Buonincontri
- IRCCS Fondazione Stella Maris, Pisa, PI, 56128, Italy
- Fondazione Imago7, Pisa, PI, 56128, Italy
| | - Brian A. Hargreaves
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
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17
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Poorman ME, Martin MN, Ma D, McGivney DF, Gulani V, Griswold MA, Keenan KE. Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations. J Magn Reson Imaging 2019; 51:675-692. [PMID: 31264748 DOI: 10.1002/jmri.26836] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 05/31/2019] [Indexed: 12/11/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a powerful quantitative MRI technique capable of acquiring multiple property maps simultaneously in a short timeframe. The MRF framework has been adapted to a wide variety of clinical applications, but faces challenges in technical development, and to date has only demonstrated repeatability and reproducibility in small studies. In this review, we discuss the current implementations of MRF and their use in a clinical setting. Based on this analysis, we highlight areas of need that must be addressed before MRF can be fully adopted into the clinic and make recommendations to the MRF community on standardization and validation strategies of MRF techniques. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:675-692.
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Affiliation(s)
- Megan E. Poorman
- Department of PhysicsUniversity of Colorado Boulder Boulder Colorado USA
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
| | - Michele N. Martin
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
| | - Dan Ma
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Debra F. McGivney
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Vikas Gulani
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Mark A. Griswold
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Kathryn E. Keenan
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
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