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Hirano Y, Ishizaka K, Sugimori H, Taniguchi Y, Amemiya T, Bito Y, Kudo K. Assessment of accuracy and repeatability of quantitative parameter mapping in MRI. Radiol Phys Technol 2024:10.1007/s12194-024-00836-4. [PMID: 39198352 DOI: 10.1007/s12194-024-00836-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 08/01/2024] [Accepted: 08/12/2024] [Indexed: 09/01/2024]
Abstract
We aimed to evaluate the accuracy and repeatability of the T1, T2*, and proton density (PD) values obtained by quantitative parameter mapping (QPM) using the ISMRM/NIST MRI system phantom and compared them with computer simulations. We compared the relaxation times and PD obtained through QPM with the reference values of the ISMRM/NIST MRI system phantom and conventional methods. Furthermore, we evaluated the presence or absence of influences other than noise in T1 and T2* values obtained by QPM by comparing the obtained coefficient of variation (CV) with simulation results. The T1, T2*, and PD values by QPM showed a strong correlation with the measured values and the referenced values. The simulated CVs of QPM calculated for each sphere showed similar trends to those of the actual scans.
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Affiliation(s)
- Yuya Hirano
- Department of Radiological Technology, Hokkaido University Hospital, N15, W7, Kita-Ku, Sapporo, 060-8638, Japan
| | - Kinya Ishizaka
- Department of Radiological Technology, Hokkaido University Hospital, N15, W7, Kita-Ku, Sapporo, 060-8638, Japan
| | - Hiroyuki Sugimori
- Faculty of Health Sciences, Hokkaido University, Sapporo, 060-0812, Japan
- Clinical AI Human Resources Development Program, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, 060-8648, Japan
| | - Yo Taniguchi
- Medical Systems Research & Development Center, FUJIFILM Corporation, Tokyo, Japan
| | - Tomoki Amemiya
- Medical Systems Research & Development Center, FUJIFILM Corporation, Tokyo, Japan
| | - Yoshitaka Bito
- FUJIFILM Healthcare Corporation, Tokyo, Japan
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15, W7, Kita-Ku, Sapporo, 060-8638, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15, W7, Kita-Ku, Sapporo, 060-8638, Japan.
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N15, W7, Kita-Ku, Sapporo, 060-8638, Japan.
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Choi Y, Ko JS, Park JE, Jeong G, Seo M, Jun Y, Fujita S, Bilgic B. Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain. Invest Radiol 2024:00004424-990000000-00248. [PMID: 39159333 DOI: 10.1097/rli.0000000000001114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
ABSTRACT Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.
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Affiliation(s)
- Yangsean Choi
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea (Y.C., J.S.K., J.E.P.); AIRS Medical LLC, Seoul, Republic of Korea (G.J.); Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea (M.S.); Department of Radiology, Harvard Medical School, Boston, MA (Y.J., S.F., B.B.); Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA (Y.J., S.F., B.B.); and Harvard/MIT Health Sciences and Technology, Cambridge, MA (B.B.)
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Zhu Y, Wang G, Gu Y, Zhao W, Lu J, Zhu J, MacAskill CJ, Dupuis A, Griswold MA, Ma D, Flask CA, Yu X. 3D MR fingerprinting for dynamic contrast-enhanced imaging of whole mouse brain. Magn Reson Med 2024. [PMID: 39164799 DOI: 10.1002/mrm.30253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/15/2024] [Accepted: 07/28/2024] [Indexed: 08/22/2024]
Abstract
PURPOSE Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. METHOD We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method's accuracy for T1 and T2 measurements was validated in vitro, while its repeatability of T1 and T2 measurements was evaluated in vivo (n = 3). The utility of the 3D MRF sequence for dynamic tracking of intracisternally infused gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) in the whole mouse brain was demonstrated (n = 5). RESULTS Phantom studies confirmed accurate T1 and T2 measurements by 3D MRF with an undersampling factor of up to 48. Dynamic contrast-enhanced MRF scans achieved a spatial resolution of 192 × 192 × 500 μm3 and a temporal resolution of 4.3 min, allowing for the analysis and comparison of dynamic changes in concentration and transport kinetics of intracisternally infused Gd-DTPA across brain regions. The sequence also enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse brain (192 × 192 × 250 μm3) in 30 min. CONCLUSION We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF.
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Affiliation(s)
- Yuran Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Guanhua Wang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Yuning Gu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jiahao Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Junqing Zhu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Christina J MacAskill
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Andrew Dupuis
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Mark A Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Chris A Flask
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, USA
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Zhu Y, Wang G, Gu Y, Zhao W, Lu J, Zhu J, MacAskill CJ, Dupuis A, Griswold MA, Ma D, Flask CA, Yu X. 3D MR Fingerprinting for Dynamic Contrast-Enhanced Imaging of Whole Mouse Brain. ARXIV 2024:arXiv:2405.00513v2. [PMID: 38745701 PMCID: PMC11092875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Purpose Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent transport dynamically in mouse brain. We developed a 3D MR fingerprinting (MRF) method for simultaneous T1 and T2 mapping across the whole mouse brain with 4.3-min temporal resolution. Method We designed a 3D MRF sequence with variable acquisition segment lengths and magnetization preparations on a 9.4T preclinical MRI scanner. Model-based reconstruction approaches were employed to improve the accuracy and speed of MRF acquisition. The method's accuracy for T1 and T2 measurements was validated in vitro, while its repeatability of T1 and T2 measurements was evaluated in vivo (n=3). The utility of the 3D MRF sequence for dynamic tracking of intracisternally infused Gd-DTPA in the whole mouse brain was demonstrated (n=5). Results Phantom studies confirmed accurate T1 and T2 measurements by 3D MRF with an undersampling factor up to 48. Dynamic contrast-enhanced (DCE) MRF scans achieved a spatial resolution of 192 ✕ 192 ✕ 500 μm3 and a temporal resolution of 4.3 min, allowing for the analysis and comparison of dynamic changes in concentration and transport kinetics of intracisternally infused Gd-DTPA across brain regions. The sequence also enabled highly repeatable, high-resolution T1 and T2 mapping of the whole mouse brain (192 ✕ 192 ✕ 250 μm3) in 30 min. Conclusion We present the first dynamic and multi-parametric approach for quantitatively tracking contrast agent transport in the mouse brain using 3D MRF.
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Affiliation(s)
- Yuran Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Guanhua Wang
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Yuning Gu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jiahao Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Junqing Zhu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Christina J. MacAskill
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Andrew Dupuis
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Mark A. Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Chris A. Flask
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Pediatrics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, USA
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Yablonski M, Zhou Z, Cao X, Schauman S, Liao C, Setsompop K, Yeatman JD. Fast and reliable quantitative measures of white matter development with magnetic resonance fingerprinting. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600735. [PMID: 38979185 PMCID: PMC11230456 DOI: 10.1101/2024.06.26.600735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Developmental cognitive neuroscience aims to shed light on evolving relationships between brain structure and cognitive development. To this end, quantitative methods that reliably measure individual differences in brain tissue properties are fundamental. Standard qualitative MRI sequences are influenced by scan parameters and hardware-related biases, and also lack physical units, making the analysis of individual differences problematic. In contrast, quantitative MRI can measure physical properties of the tissue but with the cost of long scan durations and sensitivity to motion. This poses a critical limitation for studying young children. Here, we examine the reliability and validity of an efficient quantitative multiparameter mapping method - Magnetic Resonance Fingerprinting (MRF) - in children scanned longitudinally. We focus on T1 values in white matter, since quantitative T1 values are known to primarily reflect myelin content, a key factor in brain development. Forty-nine children aged 8-13y (mean 10.3y ±1.4) completed two scanning sessions 2-4 months apart. In each session, two 2-minute 3D-MRF scans at 1mm isotropic resolution were collected to evaluate the effect of scan duration on image quality and scan-rescan reliability. A separate calibration scan was used to measure B0 inhomogeneity and correct for bias. We examined the impact of scan time and B0 inhomogeneity correction on scan-rescan reliability of values in white matter, by comparing single 2-min and combined two 2-min scans, with and without B0-correction. Whole-brain voxel-based reliability analysis showed that combining two 2-min MRF scans improved reliability (pearson's r=0.87) compared with a single 2-min scan (r=0.84), while B0-correction had no effect on reliability in white matter (r=0.86 and 0.83 4-min vs 2-min). Using diffusion tractography, we delineated MRF-derived T1 profiles along major white matter fiber tracts and found similar or higher reliability for T1 from MRF compared to diffusion parameters (based on a 10-minute dMRI scan). Lastly, we found that T1 values in multiple white matter tracts were significantly correlated with age. In sum, MRF-derived T1 values were highly reliable in a longitudinal sample of children and replicated known age effects. Reliability in white matter was improved by longer scan duration but was not affected by B0-correction, making it a quick and straightforward scan to collect. We propose that MRF provides a promising avenue for acquiring quantitative brain metrics in children and patient populations where scan time and motion are of particular concern.
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Chekhonin IV, Cohen O, Otazo R, Young RJ, Holodny AI, Pronin IN. Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review. Neuroradiol J 2024; 37:267-275. [PMID: 37133228 PMCID: PMC11138331 DOI: 10.1177/19714009231173100] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2023] Open
Abstract
Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.
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Affiliation(s)
- Ivan V Chekhonin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
- Federal State Budgetary Institution V.P. Serbsky National Medical Research Centre for Psychiatry and Narcology of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY, USA
| | - Igor N Pronin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
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Dupuis A, Chen Y, Hansen M, Chow K, Sun JE, Badve C, Ma D, Griswold MA, Boyacioglu R. Quantifying 3D MR fingerprinting (3D-MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases. Magn Reson Med 2024; 91:2074-2088. [PMID: 38192239 PMCID: PMC10950529 DOI: 10.1002/mrm.29983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE Quantitative MRI techniques such as MR fingerprinting (MRF) promise more objective and comparable measurements of tissue properties at the point-of-care than weighted imaging. However, few direct cross-modal comparisons of MRF's repeatability and reproducibility versus weighted acquisitions have been performed. This work proposes a novel fully automated pipeline for quantitatively comparing cross-modal imaging performance in vivo via atlas-based sampling. METHODS We acquire whole-brain 3D-MRF, turbo spin echo, and MPRAGE sequences three times each on two scanners across 10 subjects, for a total of 60 multimodal datasets. The proposed automated registration and analysis pipeline uses linear and nonlinear registration to align all qualitative and quantitative DICOM stacks to Montreal Neurological Institute (MNI) 152 space, then samples each dataset's native space through transformation inversion to compare performance within atlas regions across subjects, scanners, and repetitions. RESULTS Voxel values within MRF-derived maps were found to be more repeatable (σT1 = 1.90, σT2 = 3.20) across sessions than vendor-reconstructed MPRAGE (σT1w = 6.04) or turbo spin echo (σT2w = 5.66) images. Additionally, MRF was found to be more reproducible across scanners (σT1 = 2.21, σT2 = 3.89) than either qualitative modality (σT1w = 7.84, σT2w = 7.76). Notably, differences between repeatability and reproducibility of in vivo MRF were insignificant, unlike the weighted images. CONCLUSION MRF data from many sessions and scanners can potentially be treated as a single dataset for harmonized analysis or longitudinal comparisons without the additional regularization steps needed for qualitative modalities.
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Affiliation(s)
- Andrew Dupuis
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University Hospitals, Cleveland, Ohio, USA
| | | | - Kelvin Chow
- Siemens Medical Solutions USA, Inc, Chicago, Illinois, USA
| | - Jessie E.P. Sun
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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Ma D, Badve C, Sun JEP, Hu S, Wang X, Chen Y, Nayate A, Wien M, Martin D, Singer LT, Durieux JC, Flask C, Costello DW. Motion Robust MR Fingerprinting Scan to Image Neonates With Prenatal Opioid Exposure. J Magn Reson Imaging 2024; 59:1758-1768. [PMID: 37515516 PMCID: PMC10823040 DOI: 10.1002/jmri.28907] [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: 02/06/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/31/2023] Open
Abstract
PURPOSE To explore whether MR fingerprinting (MRF) scans provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure (POE). STUDY TYPE Prospective. POPULATION 13 infants with POE (3 male; 12 newborns (age 7-65 days) and 1 infant aged 9-months). FIELD STRENGTH/SEQUENCE 3T, 3D T1-weighted MPRAGE, 3D T2-weighted TSE and MRF sequences. ASSESSMENT The image quality of MRF and MRI was assessed in a fully crossed, multiple-reader, multiple-case study. Sixteen image quality features in three types-image artifacts, structure and myelination visualization-were ranked by four neuroradiologists (8, 7, 5, and 8 years of experience respectively), using a 3-point scale. MRF T1 and T2 values in 8 white matter brain regions were compared between babies younger than 1 month and babies between 1 and 2 months. STATISTICAL TESTS Generalized estimating equations model to test the significance of differences of regional T1 and T2 values of babies under 1 month and those older. MRI and MRF image quality was assessed using Gwet's second order auto-correlation coefficient (AC2) with confidence levels. The Cochran-Mantel-Haenszel test was used to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features. A P value <0.05 was considered statistically significant. RESULTS The MRF of two infants were excluded in T1 and T2 value analysis due to severe motion artifact but were included in the image quality assessment. In infants under 1 month of age (N = 6), the T1 and T2 values were significantly higher compared to those between 1 and 2 months of age (N = 4). MRF images showed significantly higher image quality ratings in all three feature types compared to MRI images. CONCLUSIONS MR Fingerprinting scans have potential to be a motion-robust and efficient method for nonsedated infants. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Chaitra Badve
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Jessie EP Sun
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Xiaofeng Wang
- Quantitative Health Science, Cleveland Clinic, Cleveland, OH
| | - Yong Chen
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Ameya Nayate
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Michael Wien
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Douglas Martin
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Lynn T Singer
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland
| | - Jared C. Durieux
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Chris Flask
- Radiology, Case Western Reserve University, Cleveland, OH
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Zhao W, Hu Z, Kazerooni AF, Körzdörfer G, Nittka M, Davatzikos C, Viswanath SE, Wang X, Badve C, Ma D. Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI. Invest Radiol 2024; 59:359-371. [PMID: 37812483 PMCID: PMC10997475 DOI: 10.1097/rli.0000000000001026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
OBJECTIVE Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping. MATERIALS AND METHODS A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation. RESULTS First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization. CONCLUSIONS The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI.
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Affiliation(s)
- Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Zheyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, Ohio 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
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Gu Y, Pan Y, Fang Z, Ma L, Zhu Y, Androjna C, Zhong K, Yu X, Shen D. Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T 1 and T 2 mapping of entire macaque brain. Magn Reson Med 2024; 91:1149-1164. [PMID: 37929695 DOI: 10.1002/mrm.29905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/10/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system. METHODS Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains. RESULTS The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35× $$ \times $$ 0.35× $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol. CONCLUSION Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.
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Affiliation(s)
- Yuning Gu
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Yongsheng Pan
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Zhenghan Fang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lei Ma
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Yuran Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Charlie Androjna
- Cleveland Clinic Pre-Clinical Magnetic Resonance Imaging Center, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Kai Zhong
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China
- Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Biomedical Engineering Department, Peking University, Beijing, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dinggang Shen
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
- Shanghai United Imaging Intelligence, Shanghai, China
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
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11
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Monga A, Singh D, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review. Bioengineering (Basel) 2024; 11:236. [PMID: 38534511 DOI: 10.3390/bioengineering11030236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.
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Affiliation(s)
- Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector L de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V W Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder R Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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12
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Kumar S, Saber H, Charron O, Freeman L, Tamir JI. Correcting synthetic MRI contrast-weighted images using deep learning. Magn Reson Imaging 2024; 106:43-54. [PMID: 38092082 DOI: 10.1016/j.mri.2023.11.015] [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: 08/30/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/17/2023]
Abstract
Synthetic magnetic resonance imaging (MRI) offers a scanning paradigm where a fast multi-contrast sequence can be used to estimate underlying quantitative tissue parameter maps, which are then used to synthesize any desirable clinical contrast by retrospectively changing scan parameters in silico. Two benefits of this approach are the reduced exam time and the ability to generate arbitrary contrasts offline. However, synthetically generated contrasts are known to deviate from the contrast of experimental scans. The reason for contrast mismatch is the necessary exclusion of some unmodeled physical effects such as partial voluming, diffusion, flow, susceptibility, magnetization transfer, and more. The inclusion of these effects in signal encoding would improve the synthetic images, but would make the quantitative imaging protocol impractical due to long scan times. Therefore, in this work, we propose a novel deep learning approach that generates a multiplicative correction term to capture unmodeled effects and correct the synthetic contrast images to better match experimental contrasts for arbitrary scan parameters. The physics inspired deep learning model implicitly accounts for some unmodeled physical effects occurring during the scan. As a proof of principle, we validate our approach on synthesizing arbitrary inversion recovery fast spin-echo scans using a commercially available 2D multi-contrast sequence. We observe that the proposed correction visually and numerically reduces the mismatch with experimentally collected contrasts compared to conventional synthetic MRI. Finally, we show results of a preliminary reader study and find that the proposed method statistically significantly improves in contrast and SNR as compared to synthetic MR images.
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Affiliation(s)
- Sidharth Kumar
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin 78712, TX, USA.
| | - Hamidreza Saber
- Dell Medical School Department of Neurology, The University of Texas at Austin, Austin 78712, TX, USA; Dell Medical School Department of Neurosurgery, The University of Texas at Austin, Austin 78712, TX, USA
| | - Odelin Charron
- Dell Medical School Department of Neurology, The University of Texas at Austin, Austin 78712, TX, USA
| | - Leorah Freeman
- Dell Medical School Department of Neurology, The University of Texas at Austin, Austin 78712, TX, USA; Dell Medical School Department of Diagnostic Medicine, The University of Texas at Austin, Austin 78712, TX, USA
| | - Jonathan I Tamir
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin 78712, TX, USA; Dell Medical School Department of Diagnostic Medicine, The University of Texas at Austin, Austin 78712, TX, USA; Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin 78712, TX, USA
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13
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Wang K, Doneva M, Meineke J, Amthor T, Karasan E, Tan F, Tamir JI, Yu SX, Lustig M. High-fidelity direct contrast synthesis from magnetic resonance fingerprinting. Magn Reson Med 2023; 90:2116-2129. [PMID: 37332200 DOI: 10.1002/mrm.29766] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/03/2023] [Accepted: 05/31/2023] [Indexed: 06/20/2023]
Abstract
PURPOSE This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations. METHODS To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. The performance of our proposed method is demonstrated on in vivo MRF scans from healthy volunteers. Quantitative metrics, including normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID), were used to evaluate the performance of the proposed method and compare it with others. RESULTS In-vivo experiments demonstrated excellent image quality with respect to that of simulation-based contrast synthesis and previous DCS methods, both visually and according to quantitative metrics. We also demonstrate cases in which our trained model is able to mitigate the in-flow and spiral off-resonance artifacts typically seen in MRF reconstructions, and thus more faithfully represent conventional spin echo-based contrast-weighted images. CONCLUSION We present N-DCSNet to directly synthesize high-fidelity multicontrast MR images from a single MRF acquisition. This method can significantly decrease examination time. By directly training a network to generate contrast-weighted images, our method does not require any model-based simulation and therefore can avoid reconstruction errors due to dictionary matching and contrast simulation (code available at:https://github.com/mikgroup/DCSNet).
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Affiliation(s)
- Ke Wang
- Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA
- International Computer Science Institute, University of California at Berkeley, Berkeley, California, USA
| | | | | | | | - Ekin Karasan
- Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA
| | - Fei Tan
- Bioengineering, UC Berkeley-UCSF, San Francisco, California, USA
| | - Jonathan I Tamir
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA
| | - Stella X Yu
- Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, California, USA
- International Computer Science Institute, University of California at Berkeley, Berkeley, California, USA
- Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA
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14
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Ontaneda D, Gulani V, Deshmane A, Shah A, Guruprakash DK, Jiang Y, Ma D, Fisher E, Rudick RA, Raza P, Kilbane M, Cohen JA, Sakaie K, Lowe MJ, Griswold MA, Nakamura K. Magnetic resonance fingerprinting in multiple sclerosis. Mult Scler Relat Disord 2023; 79:105024. [PMID: 37783196 DOI: 10.1016/j.msard.2023.105024] [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: 02/06/2023] [Revised: 08/15/2023] [Accepted: 09/23/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND In this cross sectional study, we used MRF to investigate tissue properties of normal-appearing white matter, gray matter, and lesions in relapsing remitting MS (n = 21), secondary progressive MS (n = 16) and healthy controls (n = 9). A FISP-based MRF sequence was used for acquisition, imaging time 5 min 15 s. MRF T1 and T2 relaxation times were measured from lesional tissue, normal-appearing frontal white matter, corpus callous, thalamus, and caudate. Differences between healthy controls and MS were examined using ANCOVA adjusted for age and sex. Spearman rank correlations were assessed between T1 and T2 relaxation times and clinical measures. OBJECTIVES To examine brain T1 and T2 values using magnetic resonance fingerprinting (MRF) in healthy controls and MS. METHODS The subjects included 21 relapsing-remitting (RR) MS, 16 secondary progressive (SP) MS, and 9 age- and sex-matched HC without manifest neurological disease participating in a longitudinal MRI study. A 3T/ FISP-based MRF sequence was acquired. Regions of interest were drawn for lesions and normal appearing white matter. ANCOVA adjusted for age and sex were used to compare the groups with significance set at 0.05. RESULTS A step-wise increase in T1 and T2 relaxation times was found between healthy controls, relapsing remitting MS, and secondary progressive MS. Significant differences were found in T1 and T2 between MS and healthy controls in the frontal normal-appearing white matter, corpus callosum, and thalamus (p < 0.04 for all). Significant differences in T1 and T2 between RR and SPMS were found in the frontal normal-appearing white matter and T2 lesions (p < 0.02 for all). T1 relaxation from the frontal normal-appearing white matter correlated with the Expanded Disability Status Scale [ρ = 0.62, p < 0.001], timed 25 foot walk (ρ = 0.45, p = 0.01), 9 hole peg test (ρ = 0.62, p < 0.001), and paced auditory serial addition test (ρ = -0.4, p = 0.01). CONCLUSION These results suggest that MRF may be a clinically feasible quantitative approach for characterizing tissue damage in MS.
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Affiliation(s)
- Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States.
| | - Vikas Gulani
- Department of Radiology, University of Michigan, Michigan, United States
| | - Anagha Deshmane
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Amisha Shah
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States
| | - Deepti K Guruprakash
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
| | - Yun Jiang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States; Department of Radiology, University of Michigan, Ann Arbor, United States
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States
| | - Elizabeth Fisher
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
| | - Richard A Rudick
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Praneeta Raza
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Meghan Kilbane
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Jeffrey A Cohen
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, United States
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, United States
| | - Mark J Lowe
- Imaging Institute, Cleveland Clinic, Cleveland, United States
| | - Mark A Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, United States
| | - Kunio Nakamura
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
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15
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Hu S, Chen Y, Zong X, Lin W, Griswold M, Ma D. Improving motion robustness of 3D MR fingerprinting with a fat navigator. Magn Reson Med 2023; 90:1802-1817. [PMID: 37345703 PMCID: PMC10524525 DOI: 10.1002/mrm.29761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/01/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE To develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging. METHODS A rapid fat navigator was developed using the stack-of-spirals acquisition and non-Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition-encoding direction were both examined in the study. RESULTS Both phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non-Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole-brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion-corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion-free MRF scans. CONCLUSION We developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.
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Affiliation(s)
- Siyuan Hu
- Department of Biomedical Engineering Cleveland, Ohio, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xiaopeng Zong
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering Cleveland, Ohio, USA
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16
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Jun Y, Cho J, Wang X, Gee M, Grant PE, Bilgic B, Gagoski B. SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS. Magn Reson Med 2023; 90:2019-2032. [PMID: 37415389 PMCID: PMC10527557 DOI: 10.1002/mrm.29786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/27/2023] [Accepted: 06/15/2023] [Indexed: 07/08/2023]
Abstract
PURPOSE To develop and evaluate a method for rapid estimation of multiparametric T1 , T2 , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. METHODS An SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. The SSL-QALAS and the dictionary-matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. RESULTS Phantom experiments showed that both the dictionary-matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1 , T2 , proton density, and inversion efficiency maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. CONCLUSION The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.
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Affiliation(s)
- Yohan Jun
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Xiaoqing Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Michael Gee
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - P. Ellen Grant
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States
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17
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Taniguchi Y, Yokosawa S, Shirai T, Sato R, Amemiya T, Soutome Y, Bito Y, Ochi H. Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo. Magn Reson Med Sci 2023; 22:459-468. [PMID: 35908880 PMCID: PMC10552665 DOI: 10.2463/mrms.mp.2021-0045] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/27/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T1, T2*, PD, and susceptibility) and B1 simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE). We verified the accuracy and repeatability of QPM in phantom and volunteer experiments. METHODS Tissue-related parameters are estimated by varying four scan parameters of the 3D pRSGE: flip angle, RF-pulse phase increment, TR and TE, performing multiple image scans, and finding a least-squares fit for an intensity function (which expresses the relationship between the scan parameters and intensity values). The intensity function is analytically complex, but by using a Bloch simulation to create it numerically, the least-squares fitting can be used to estimate the quantitative values. This has the advantage of shortening the image-reconstruction processing time needed to estimate the quantitative values than with methods using pattern matching. RESULTS A 1.1-mm isotropic resolution scan covering the whole brain was completed with a scan time of approximately 12 minutes, and the reconstruction time using a GPU was approximately 1 minute. The phantom experiments confirmed that both the accuracy and repeatability of the quantitative values were high. The volunteer scans also confirmed that the accuracy of the quantitative values was comparable to that of conventional methods. CONCLUSION The proposed QPM method can map T1, T2*, PD, susceptibility, and B1 simultaneously within a scan time that can be applied to human subjects.
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Affiliation(s)
- Yo Taniguchi
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Suguru Yokosawa
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Toru Shirai
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Ryota Sato
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Tomoki Amemiya
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | - Yoshihisa Soutome
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
| | | | - Hisaaki Ochi
- Innovative Technology Laboratory, FUJIFILM Healthcare Corporation, Tokyo, Japan
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18
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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19
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Gaur S, Panda A, Fajardo JE, Hamilton J, Jiang Y, Gulani V. Magnetic Resonance Fingerprinting: A Review of Clinical Applications. Invest Radiol 2023; 58:561-577. [PMID: 37026802 PMCID: PMC10330487 DOI: 10.1097/rli.0000000000000975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
ABSTRACT Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.
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Affiliation(s)
- Sonia Gaur
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Ananya Panda
- All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | | | - Jesse Hamilton
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Yun Jiang
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Vikas Gulani
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
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20
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Huang BS, Hsieh CY, Chai WY, Lin Y, Huang YL, Lu KY, Chiang HJ, Schulte RF, Lin CYE, Lin G. Comparing Magnetic Resonance Fingerprinting (MRF) and the MAGiC Sequence for Simultaneous T1 and T2 Quantitative Measurements in the Female Pelvis: A Prospective Study. Diagnostics (Basel) 2023; 13:2147. [PMID: 37443541 DOI: 10.3390/diagnostics13132147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/29/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
The aim of this study was to explore the potential of magnetic resonance fingerprinting (MRF), an emerging quantitative MRI technique, in measuring relaxation values of female pelvic tissues compared to the conventional magnetic resonance image compilation (MAGiC) sequence. The study included 32 female patients who underwent routine pelvic MRI exams using anterior and posterior array coils on a 3T clinical scanner. Our findings demonstrated significant correlations between MRF and MAGiC measured T1 and T2 values (p < 0.0001) for various pelvic tissues, including ilium, femoral head, gluteus, obturator, iliopsoas, erector spinae, uterus, cervix, and cutaneous fat. The tissue contrasts generated from conventional MRI and synthetic MRF also showed agreement in bone, muscle, and uterus for both T1-weighted and T2-weighted images. This study highlights the strengths of MRF in providing simultaneous T1 and T2 mapping. MRF offers distinct tissue contrast and has the potential for accurate diagnosis of female pelvic diseases, including tumors, fibroids, endometriosis, and pelvic inflammatory disease. Additionally, MRF shows promise in monitoring disease progression or treatment response. Overall, the study demonstrates the potential of MRF in the field of female pelvic organ imaging and suggests that it could be a valuable addition to the clinical practice of pelvic MRI exams. Further research is needed to establish the clinical utility of MRF and to develop standardized protocols for its implementation in clinical practice.
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Affiliation(s)
- Bo-Syuan Huang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
| | - Ching-Yi Hsieh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, No.259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 33302, Taiwan
| | - Wen-Yen Chai
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 33382, Taiwan
| | - Yenpo Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
| | - Yen-Ling Huang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 33382, Taiwan
| | - Kuan-Ying Lu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, No.259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 33302, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 33382, Taiwan
| | - Hsin-Ju Chiang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 33382, Taiwan
| | | | | | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33382, Taiwan
- Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University, No.259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 33302, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 33382, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 33382, Taiwan
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21
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Beracha I, Seginer A, Tal A. Adaptive model-based Magnetic Resonance. Magn Reson Med 2023. [PMID: 37154407 DOI: 10.1002/mrm.29688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time. METHODS We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T2 s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T2 , which was used to guide the selection of sequence parameters in real time. RESULTS Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T2 for n-acetyl-aspartate by a factor of 2.5. CONCLUSION Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.
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Affiliation(s)
- Inbal Beracha
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
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22
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Hu S, Jordan S, Boyacioglu R, Rozada I, Troyer M, Griswold M, McGivney D, Ma D. A fast MR fingerprinting simulator for direct error estimation and sequence optimization. Magn Reson Imaging 2023; 98:105-114. [PMID: 36681312 PMCID: PMC10002151 DOI: 10.1016/j.mri.2023.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 01/14/2023] [Indexed: 01/19/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation of the quantitative maps and efficient sequence optimization. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. The simulations from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. We also demonstrate the power of applying the fast MRF simulator in MRF sequence optimization. The optimized sequences are validated with in vivo scans to assess the image quality and accuracy. The optimized sequences produce artifact-free T1 and T2 maps in 2D and 3D scans with equivalent mapping accuracy as the human-designed sequence but at shorter scan times. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling artifacts and field inhomogeneity.
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Affiliation(s)
- Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | | | - Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Ignacio Rozada
- 1QBit Information Technologies Inc., Vancouver, BC V6E 4B1, Canada
| | | | - Mark Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Debra McGivney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
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23
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Zhou Z, Li Q, Liao C, Cao X, Liang H, Chen Q, Pu R, Ye H, Tong Q, He H, Zhong J. Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping. Hum Brain Mapp 2023; 44:2209-2223. [PMID: 36629336 PMCID: PMC10028641 DOI: 10.1002/hbm.26203] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/12/2022] [Accepted: 01/01/2023] [Indexed: 01/12/2023] Open
Abstract
Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.
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Affiliation(s)
- Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing Li
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- MR Collaborations, Siemens Healthineers Ltd, Shanghai, China
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Hui Liang
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Quan Chen
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Run Pu
- Neusoft Medical Systems, Shanghai, China
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
- School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
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24
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Wicaksono KP, Fushimi Y, Nakajima S, Sakata A, Okuchi S, Hinoda T, Oshima S, Otani S, Tagawa H, Urushibata Y, Nakamoto Y. Accuracy, repeatability, and reproducibility of T 1 and T 2 relaxation times measurement by 3D magnetic resonance fingerprinting with different dictionary resolutions. Eur Radiol 2023; 33:2895-2904. [PMID: 36422648 PMCID: PMC10017611 DOI: 10.1007/s00330-022-09244-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 08/29/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To assess the accuracy, repeatability, and reproducibility of T1 and T2 relaxation time measurements by three-dimensional magnetic resonance fingerprinting (3D MRF) using various dictionary resolutions. METHODS The ISMRM/NIST phantom was scanned daily for 10 days in two 3 T MR scanners using a 3D MRF sequence reconstructed using four dictionaries with varying step sizes and one dictionary with wider ranges. Thirty-nine healthy volunteers were enrolled: 20 subjects underwent whole-brain MRF scans in both scanners and the rest in one scanner. ROI/VOI analyses were performed on phantom and brain MRF maps. Accuracy, repeatability, and reproducibility metrics were calculated. RESULTS In the phantom study, all dictionaries showed high T1 linearity to the reference values (R2 > 0.99), repeatability (CV < 3%), and reproducibility (CV < 3%) with lower linearity (R2 > 0.98), repeatability (CV < 6%), and reproducibility (CV ≤ 4%) for T2 measurement. The volunteer study demonstrated high T1 reproducibility of within-subject CV (wCV) < 4% by all dictionaries with the same ranges, both in the brain parenchyma and CSF. Yet, reproducibility was moderate for T2 measurement (wCV < 8%). In CSF measurement, dictionaries with a smaller range showed a seemingly better reproducibility (T1, wCV 3%; T2, wCV 8%) than the much wider range dictionary (T1, wCV 5%; T2, wCV 13%). Truncated CSF relaxometry values were evident in smaller range dictionaries. CONCLUSIONS The accuracy, repeatability, and reproducibility of 3D MRF across various dictionary resolutions were high for T1 and moderate for T2 measurements. A lower-resolution dictionary with a well-defined range may be adequate, thus significantly reducing the computational load. KEY POINTS • A lower-resolution dictionary with a well-defined range may be sufficient for 3D MRF reconstruction. • CSF relaxation times might be underestimated due to truncation by the upper dictionary range. • Dictionary with a higher upper range might be advisable, especially for CSF evaluation and elderly subjects whose perivascular spaces are more prominent.
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Affiliation(s)
- Krishna Pandu Wicaksono
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sachi Okuchi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Takuya Hinoda
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sonoko Oshima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sayo Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Hiroshi Tagawa
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
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25
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Choi JY, Hu S, Su TY, Murakami H, Tang Y, Blümcke I, Najm I, Sakaie K, Jones S, Griswold M, Wang ZI, Ma D. Normative quantitative relaxation atlases for characterization of cortical regions using magnetic resonance fingerprinting. Cereb Cortex 2023; 33:3562-3574. [PMID: 35945683 PMCID: PMC10068276 DOI: 10.1093/cercor/bhac292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/14/2022] Open
Abstract
Quantitative magnetic resonance (MR) has been used to study cyto- and myelo-architecture of the human brain non-invasively. However, analyzing brain cortex using high-resolution quantitative MR acquisition can be challenging to perform using 3T clinical scanners. MR fingerprinting (MRF) is a highly efficient and clinically feasible quantitative MR technique that simultaneously provides T1 and T2 relaxation maps. Using 3D MRF from 40 healthy subjects (mean age = 25.6 ± 4.3 years) scanned on 3T magnetic resonance imaging, we generated whole-brain gyral-based normative MR relaxation atlases and investigated cortical-region-based T1 and T2 variations. Gender and age dependency of T1 and T2 variations were additionally analyzed. The coefficient of variation of T1 and T2 for each cortical-region was 3.5% and 7.3%, respectively, supporting low variability of MRF measurements across subjects. Significant differences in T1 and T2 were identified among 34 brain regions (P < 0.001), lower in the precentral, postcentral, paracentral lobule, transverse temporal, lateral occipital, and cingulate areas, which contain sensorimotor, auditory, visual, and limbic functions. Significant correlations were identified between age and T1 and T2 values. This study established whole-brain MRF T1 and T2 atlases of healthy subjects using a clinical 3T scanner, which can provide a quantitative and region-specific baseline for future brain studies and pathology detection.
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Affiliation(s)
- Joon Yul Choi
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
| | - Ting-Yu Su
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
| | - Hiroatsu Murakami
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Yingying Tang
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
- Department of Neurology, West China Hospital of Sichuan University, 37 Guoxue Ln, Wuhou District, Chengdu, Sichuan 610041, China
| | - Ingmar Blümcke
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
- Imaging Institute, Cleveland Clinic, 1950 E 89th St U Bldg, Cleveland, OH 44195, United States
| | - Imad Najm
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Ken Sakaie
- Department of Neuropathology, University of Erlangen, Schlobplatz 4, Erlangen 91054, Germany
| | - Stephen Jones
- Department of Neuropathology, University of Erlangen, Schlobplatz 4, Erlangen 91054, Germany
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106, United States
| | - Zhong Irene Wang
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
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26
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Cheng F, Liu Y, Chen Y, Yap PT. High-Resolution 3D Magnetic Resonance Fingerprinting With a Graph Convolutional Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:674-683. [PMID: 36269931 PMCID: PMC10081960 DOI: 10.1109/tmi.2022.3216527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging framework for rapid and simultaneous quantification of multiple tissue properties. 3D MRF allows higher through-plane resolution, but the acquisition process is slow when whole-brain coverage is needed. Existing methods for acceleration mainly rely on GRAPPA for k-space interpolation in the partition-encoding direction, limiting the acceleration factor to 2 or 3. In this work, we replace GRAPPA with a deep learning approach for accurate tissue quantification with greater acceleration. Specifically, a graph convolution network (GCN) is developed to cater to the non-Cartesian spiral sampling trajectories typical in MRF acquisition. The GCN maintains high quantification accuracy with up to 6-fold acceleration and allows 1mm isotropic resolution whole-brain 3D MRF data to be acquired in 3min and submillimeter 3D MRF (0.8mm) in 5min, greatly improving the feasibility of MRF in clinical settings.
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27
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Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study. Cancers (Basel) 2022; 14:cancers14225606. [PMID: 36428699 PMCID: PMC9688653 DOI: 10.3390/cancers14225606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/17/2022] Open
Abstract
The purpose of the present pilot study was to estimate T1 and T2 metric values derived simultaneously from a new, rapid Magnetic Resonance Fingerprinting (MRF) technique, as well as to assess their ability to characterize-brain metastases (BM) and normal-appearing brain tissues. Fourteen patients with BM underwent MRI, including prototype MRF, on a 3T scanner. In total, 108 measurements were analyzed: 42 from solid parts of BM's (21 each on T1 and T2 maps) and 66 from normal-appearing brain tissue (11 ROIs each on T1 and T2 maps for gray matter [GM], white matter [WM], and cerebrospinal fluid [CSF]). The BM's mean T1 and T2 values differed significantly from normal-appearing WM (p < 0.05). The mean T1 values from normal-appearing GM, WM, and CSF regions were 1205 ms, 840 ms, and 4233 ms, respectively. The mean T2 values were 108 ms, 78 ms, and 442 ms, respectively. The mean T1 and T2 values for untreated BM (n = 4) were 2035 ms and 168 ms, respectively. For treated BM (n = 17) the T1 and T2 values were 2163 ms and 141 ms, respectively. MRF technique appears to be a promising and rapid quantitative method for the characterization of free water content and tumor morphology in BMs.
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28
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Afzali M, Mueller L, Sakaie K, Hu S, Chen Y, Szczepankiewicz F, Griswold MA, Jones DK, Ma D. MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan. Magn Reson Med 2022; 88:2043-2057. [PMID: 35713357 PMCID: PMC9420788 DOI: 10.1002/mrm.29352] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time. METHODS We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps. RESULTS T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts. CONCLUSION We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.
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Affiliation(s)
- Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
| | - Ken Sakaie
- Imaging Institute, Cleveland ClinicClevelandOhioUSA
| | - Siyuan Hu
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
| | - Yong Chen
- RadiologyCase Western Reserve UniversityClevelandOhioUSA
| | | | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Dan Ma
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
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29
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Lo W, Bittencourt LK, Panda A, Jiang Y, Tokuda J, Seethamraju R, Tempany‐Afdhal C, Obmann V, Wright K, Griswold M, Seiberlich N, Gulani V. Multicenter Repeatability and Reproducibility of MR Fingerprinting in Phantoms and in Prostatic Tissue. Magn Reson Med 2022; 88:1818-1827. [PMID: 35713379 PMCID: PMC9469467 DOI: 10.1002/mrm.29264] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 02/15/2022] [Accepted: 03/22/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE To evaluate multicenter repeatability and reproducibility of T1 and T2 maps generated using MR fingerprinting (MRF) in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and in prostatic tissues. METHODS MRF experiments were performed on 5 different 3 Tesla MRI scanners at 3 different institutions: University Hospitals Cleveland Medical Center (Cleveland, OH), Brigham and Women's Hospital (Boston, MA) in the United States, and Diagnosticos da America (Rio de Janeiro, RJ) in Brazil. Raw MRF data were reconstructed using a Gadgetron-based MRF online reconstruction pipeline to yield quantitative T1 and T2 maps. The repeatability of T1 and T2 values over 6 measurements in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom was assessed to demonstrate intrascanner variation. The reproducibility between the 4 clinical scanners was assessed to demonstrate interscanner variation. The same-day test-retest normal prostate mean T1 and T2 values from peripheral zone and transitional zone were also compared using the intraclass correlation coefficient and Bland-Altman analysis. RESULTS The intrascanner variation of values measured using MRF was less than 2% for T1 and 4.7% for T2 for relaxation values, within the range of 307.7 to 2360 ms for T1 and 19.1 to 248.5 ms for T2 . Interscanner measurements showed that the T1 variation was less than 4.9%, and T2 variation was less than 8.1% between multicenter scanners. Both T1 and T2 values in in vivo prostatic tissue demonstrated high test-retest reliability (intraclass correlation coefficient > 0.92) and strong linear correlation (R2 > 0.840). CONCLUSION Prostate MRF measurements of T1 and T2 are repeatable and reproducible between MRI scanners at different centers on different continents for the above measurement ranges.
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Affiliation(s)
- Wei‐Ching Lo
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOhio
- Siemens Medical Solutions IncBostonMassachusetts
| | - Leonardo Kayat Bittencourt
- Department of RadiologyUniversity Hospital and Case Western Reserve UniversityClevelandOhio
- DASA companyRio de JaneiroRJBrazil
| | - Ananya Panda
- Department of RadiologyMayo ClinicRochesterMinnesota
| | - Yun Jiang
- Department of RadiologyUniversity of MichiganAnn ArborMichigan
| | - Junichi Tokuda
- Department of Radiology, Harvard Medical SchoolHarvard UniversityBostonMassachusetts
- Department of RadiologyBrigham and Women's HospitalBostonMassachusetts
| | | | - Clare Tempany‐Afdhal
- Department of Radiology, Harvard Medical SchoolHarvard UniversityBostonMassachusetts
- Department of RadiologyBrigham and Women's HospitalBostonMassachusetts
| | - Verena Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital BernUniversity of BernBerneSwitzerland
| | | | - Mark Griswold
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOhio
- Department of RadiologyUniversity Hospital and Case Western Reserve UniversityClevelandOhio
| | | | - Vikas Gulani
- Department of RadiologyUniversity of MichiganAnn ArborMichigan
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30
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Tang Y, Su TY, Choi JY, Hu S, Wang X, Sakaie K, Murakami H, Alexopoulos A, Griswold M, Jones S, Najm I, Ma D, Wang ZI. Characterizing Thalamic and Basal Ganglia Nuclei in Medically Intractable Focal Epilepsy by MR Fingerprinting. Epilepsia 2022; 63:1998-2010. [PMID: 35661353 DOI: 10.1111/epi.17318] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 05/11/2022] [Accepted: 06/02/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Magnetic resonance fingerprinting (MRF) is a novel, quantitative and noninvasive technique to measure brain tissue properties. We aim to use MRF for characterizing normal-appearing thalamic and basal ganglia nuclei in the epileptic brain. METHODS A 3D MRF protocol (1mm3 isotropic resolution) was acquired from 48 patients with unilateral medically refractory focal epilepsy and 39 healthy controls (HCs). Whole-brain T1 and T2 maps (containing T1 and T2 relaxation times) were reconstructed for each subject. Ten subcortical nuclei in the thalamus and basal ganglia were segmented as regions of interest (ROIs), within which the mean T1 and T2 values, as well as their coefficient of variation (CV) were compared between the patients and HCs at group level. Subgroup and correlation analyses were performed to examine the relationship between significant MRF measures and various clinical characteristics. Using significantly abnormal MRF measures from the group-level analyses, support vector machine (SVM) and logistic regression machine learning models were built and tested with 5-fold and 10-fold cross-validations, to separate patients from HCs, and to separate patients with left-sided and right-sided epilepsy, at individual level. RESULTS MRF revealed increased T1 mean value in the ipsilateral thalamus and nucleus accumbens; increased T1 CV in bilateral thalamus, bilateral pallidum, and ipsilateral caudate; and increased T2 CV in the ipsilateral thalamus in patients compared to HCs (P<0.05, FDR corrected). The SVM classifier produced 78.2% average accuracy to separate individual patients from HCs, with AUC of 0.83. The logistic regression classifier produced 67.4% average accuracy to separate patients with left-sided and right-sided epilepsy, with AUC of 0.72. SIGNIFICANCE MRF revealed bilateral tissue-property changes in the normal-appearing thalamus and basal ganglia, with ipsilateral predominance and thalamic preference, suggesting subcortical involvement/impairment in patients with medically intractable focal epilepsy. The individual-level performance of the MRF-based machine-learning models suggests potential opportunities for predicting lateralization.
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Affiliation(s)
- Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,Charles Shor Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Ting Yu Su
- Charles Shor Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA.,Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Joon Yul Choi
- Charles Shor Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Xiaofeng Wang
- Quantitative Health Science, Cleveland Clinic, Cleveland, OH, USA
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - Mark Griswold
- Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - Stephen Jones
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Imad Najm
- Charles Shor Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Zhong Irene Wang
- Charles Shor Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
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31
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Yu Z, Hodono S, Dergachyova O, Hilbert T, Wang B, Zhang B, Brown R, Sodickson DK, Madelin G, Cloos MA. Simultaneous 3D acquisition of 1 H MRF and 23 Na MRI. Magn Reson Med 2022; 87:2299-2312. [PMID: 34971454 PMCID: PMC8847332 DOI: 10.1002/mrm.29135] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 11/23/2021] [Accepted: 12/10/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop a 3D MR technique to simultaneously acquire proton multiparametric maps (T1 , T2 , and proton density) and sodium density weighted images over the whole brain. METHODS We implemented a 3D stack-of-stars MR pulse sequence which consists of interleaved proton (1 H) and sodium (23 Na) excitations, tailored slice encoding gradients that can encode the same slice for both nuclei, and simultaneous readout with different radial trajectories (1 H, full-radial; 23 Na, center-out radial). The receive chain of our 7T scanner was modified to enable simultaneous acquisition of 1 H and 23 Na signal. A heuristically optimized flip angle train was implemented for proton MR fingerprinting (MRF). The SNR and the accuracy of proton T1 and T2 were evaluated in phantoms. Finally, in vivo application of the method was demonstrated in five healthy subjects. RESULTS The SNR for the simultaneous measurement was almost identical to that for the single-nucleus measurements (<2% change). The proton T1 and T2 maps remained similar to the results from a reference 2D MRF technique (normalized RMS error in T1 ≈ 4.2% and T2 ≈ 11.3%). Measurements in healthy subjects corroborated these results and demonstrated the feasibility of our method for in vivo application. The in vivo T1 values measured using our method were lower than the results measured by other conventional techniques. CONCLUSIONS With the 3D simultaneous implementation, we were able to acquire sodium and proton density weighted images in addition to proton T1 , T2 , and B1+ from 1 H MRF that covers the whole brain volume within 21 min.
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Affiliation(s)
- Zidan Yu
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Shota Hodono
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA,The Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, Australia
| | - Olga Dergachyova
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bili Wang
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Bei Zhang
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ryan Brown
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Guillaume Madelin
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Martijn A. Cloos
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA,The Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA,The Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, Australia
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32
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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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33
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Mickevicius NJ, Glide‐Hurst CK. Low‐rank
inversion reconstruction for
through‐plane
accelerated radial
MR
fingerprinting applied to relaxometry at 0.
35 T. Magn Reson Med 2022; 88:840-848. [PMID: 35403235 PMCID: PMC9324087 DOI: 10.1002/mrm.29244] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/31/2022] [Accepted: 03/03/2022] [Indexed: 11/15/2022]
Abstract
Purpose To reduce scan time, methods to accelerate phase‐encoded/non‐Cartesian MR fingerprinting (MRF) acquisitions for variable density spiral acquisitions have recently been developed. These methods are not applicable to MRF acquisitions, wherein a single k‐space spoke is acquired per frame. Therefore, we propose a low‐rank inversion method to resolve MRF contrast dynamics from through‐plane accelerated Cartesian/radial measurements applied to quantitative relaxation‐time mapping on a 0.35T system. Methods An algorithm was implemented to reconstruct through‐plane aliased low‐rank images describing the contrast dynamics occurring because of the transient‐state MRF acquisition. T1 and T2 times from accelerated acquisitions were compared with those from unaccelerated linear reconstructions in a standardized system phantom and within in vivo brain and prostate experiments on a hybrid 0.35T MRI/linear accelerator. Results No significant differences between T1 and T2 times for the accelerated reconstructions were observed compared to fully sampled acquisitions (p = 0.41 and p = 0.36, respectively). The mean absolute errors in T1 and T2 were 5.6% and 2.9%, respectively, between the full and accelerated acquisitions. The SDs in T1 and T2 decreased with the advanced accelerated reconstruction compared with the unaccelerated reconstruction (p = 0.02 and p = 0.03, respectively). The quality of the T1 and T2 maps generated with the proposed approach are comparable to those obtained using the unaccelerated data sets. Conclusions Through‐plane accelerated MRF with radial k‐space coverage was demonstrated at a low field strength of 0.35 T. This method enabled 3D T1 and T2 mapping at 0.35 T with a 3‐min scan.
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Affiliation(s)
| | - Carri K. Glide‐Hurst
- Department of Human Oncology University of Wisconsin‐Madison Madison Wisconsin USA
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34
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Fujita S, Hagiwara A, Takei N, Fukunaga I, Hagiwara Y, Ogawa T, Hatano T, Rettmann D, Banerjee S, Hwang KP, Amemiya S, Kamagata K, Hattori N, Abe O, Aoki S. Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain. Neuroimage 2022; 255:119176. [PMID: 35390461 DOI: 10.1016/j.neuroimage.2022.119176] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/03/2022] [Accepted: 04/01/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE To develop a rigid real-time prospective motion-corrected multiparametric mapping technique and to test the performance of quantitative estimates. METHODS Motion tracking and correction were performed by integrating single-shot spiral navigators into a multiparametric imaging technique, three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). The spiral navigator was optimized, and quantitative measurements were validated using a standard system phantom. The effect of motion correction on whole-brain T1 and T2 mapping under different types of head motion during the scan was evaluated in 10 healthy volunteers. Finally, six patients with Parkinson's disease, which is known to be associated with a high prevalence of motion artifacts, were scanned to evaluate the effectiveness of our method in the real world. RESULTS The phantom study demonstrated that the proposed motion correction method did not introduce quantitative bias. Improved parametric map quality and repeatability were shown in volunteer experiments with both in-plane and through-plane motions, comparable to the no-motion ground truth. In real-life validation in patients, the approach showed improved parametric map quality compared to images obtained without motion correction. CONCLUSIONS Real-time prospective motion-corrected multiparametric relaxometry based on 3D-QALAS provided robust and repeatable whole-brain multiparametric mapping.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | - Naoyuki Takei
- MR Applications and Workflow, GE Healthcare, Tokyo, Japan
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | - Yasuhiro Hagiwara
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Takashi Ogawa
- Department of Neurology, Juntendo University, Tokyo, Japan
| | - Taku Hatano
- Department of Neurology, Juntendo University, Tokyo, Japan
| | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, MN, United States
| | | | - Ken-Pin Hwang
- Department of Radiology, MD Anderson Cancer Center, Houston, TX, United States
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | | | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
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35
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Choi JY, Krishnan B, Hu S, Martinez D, Tang Y, Wang X, Sakaie K, Jones S, Murakami H, Blümcke I, Najm I, Ma D, Wang ZI. Using magnetic resonance fingerprinting to characterize periventricular nodular heterotopias in pharmacoresistant epilepsy. Epilepsia 2022; 63:1225-1237. [PMID: 35343593 PMCID: PMC9081261 DOI: 10.1111/epi.17191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 02/01/2022] [Accepted: 02/01/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE We aimed to use a novel magnetic resonance fingerprinting (MRF) technique to examine in vivo tissue property characteristics of periventricular nodular heterotopia (PVNH). These characteristics were further correlated with stereotactic-electroencephalographic (SEEG) ictal onset findings. METHODS We included five patients with PVNH who had SEEG-guided surgery and at least 1 year of seizure freedom or substantial seizure reduction. High-resolution MRF scans were acquired at 3 T, generating three-dimensional quantitative T1 and T2 maps. We assessed the differences between T1 and T2 values from the voxels in the nodules located in the SEEG-defined seizure onset zone (SOZ) and non-SOZ, on -individual and group levels. Receiver operating characteristic analyses were performed to obtain the optimal classification performance. Quantification of SEEG ictal onset signals from the nodules was performed by calculating power spectrum density (PSD). The association between PSD and T1 /T2 values was further assessed at different frequency bands. RESULTS Individual-level analysis showed T1 was significantly higher in SOZ voxels than non-SOZ voxels (p < .05), with an average 73% classification accuracy. Group-level analysis also showed higher T1 was significantly associated with SOZ voxels (p < .001). At the optimal cutoff (normalized T1 of 1.1), a 76% accuracy for classifying SOZ nodules from non-SOZ nodules was achieved. T1 values were significantly associated with ictal onset PSD at the ultraslow, θ, β, γ, and ripple bands (p < .05). T2 values were significantly associated with PSD only at the ultraslow band (p < .05). SIGNIFICANCE Quantitative MRF measures, especially T1 , can provide additional noninvasive information to separate nodules in SOZ and non-SOZ. The T1 and T2 tissue property changes carry electrophysiological underpinnings relevant to the epilepsy, as shown by their significant positive associations with power changes during the SEEG seizure onset. The use of MRF as a supplementary noninvasive tool may improve presurgical evaluation for patients with PVNH and pharmacoresistant epilepsy.
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Affiliation(s)
- Joon Yul Choi
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA
| | - Balu Krishnan
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - David Martinez
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA
| | - Yinging Tang
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA.,Department of Neurology, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaofeng Wang
- Quantitative Health Science, Cleveland Clinic, Cleveland, Ohio, USA
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Stephen Jones
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Hiroatsu Murakami
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA
| | - Ingmar Blümcke
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA.,Neuropathology, University of Erlangen, Erlangen, Germany
| | - Imad Najm
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA
| | - Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Zhong Irene Wang
- Charles Shor Epilepsy Center, Cleveland Clinic, Neurological Institute, Cleveland, Ohio, USA
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36
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Wyatt CR, Guimaraes AR. 3D MR fingerprinting using Seiffert spirals. Magn Reson Med 2022; 88:151-163. [PMID: 35324040 DOI: 10.1002/mrm.29197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 01/17/2022] [Accepted: 01/23/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Seiffert spirals were recently explored as an efficient way to traverse 3D k-space compared to traditional 3D techniques. Several studies have shown the ability of 3D MR fingerprinting (MRF) techniques to acquire T1 and T2 relaxation maps in a short period of time. However, these sequences do not sample across a large region of 3D k-space every TR, especially in the way that Seiffert trajectories can. METHODS A 3D MRF sequence was designed using 8 Seiffert spirals rotated in 3D k-space, with flip angle modulation for T1 and T2 sensitivity. The sequence was compared to an MRF sequence using a 2D spiral rotated in 3D k-space using the tiny golden angle acquisition with similar resolution/readout duration. Both sequences were evaluated using simulations, phantom validation, and in vivo imaging. RESULTS In all experiments, the Seiffert spiral MRF sequence performed similar to if not better than the multi-axis 2D spiral MRF sequence. Strong intraclass correlation coefficients (> 0.9) were found between conventional and MRF sequences in phantoms, whereas the in vivo results showed slightly less aliasing artifact with the Seiffert trajectory. CONCLUSION In this study, Seiffert spirals were used within the MRF framework to acquire high-resolution T1 and T2 relaxation time maps in less than 2.5 min. The reduced aliasing artifacts seen with the Seiffert sequence suggests that sampling over 3D k-space evenly each TR can improve quantification or shorten scan times.
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Affiliation(s)
- Cory R Wyatt
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon, USA.,Advanced Imaging Research Center, Oregon Health & Sciences University, Portland, Oregon, USA
| | - Alexander R Guimaraes
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon, USA.,Advanced Imaging Research Center, Oregon Health & Sciences University, Portland, Oregon, USA
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37
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Fujita S, Hagiwara A, Yasaka K, Akai H, Kunimatsu A, Kiryu S, Fukunaga I, Kato S, Akashi T, Kamagata K, Wada A, Abe O, Aoki S. Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features. Eur Radiol 2022; 32:4791-4800. [PMID: 35304637 PMCID: PMC9213334 DOI: 10.1007/s00330-022-08555-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 11/23/2021] [Accepted: 12/23/2021] [Indexed: 11/17/2022]
Abstract
Objectives We aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans. Methods Scan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22–72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32–53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases. Results The overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62–0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps. Conclusion MRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries. Key Points • MRF-based radiomic features are highly repeatable in various dictionary step sizes. • Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used. • Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08555-3.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan. .,Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Akira Kunimatsu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852, Hatakeda, Narita, Chiba, 286-8520, Japan
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Shimpei Kato
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
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Fujita S, Cencini M, Buonincontri G, Takei N, Schulte RF, Fukunaga I, Uchida W, Hagiwara A, Kamagata K, Hagiwara Y, Matsuyama Y, Abe O, Tosetti M, Aoki S. Simultaneous relaxometry and morphometry of human brain structures with 3D magnetic resonance fingerprinting: a multicenter, multiplatform, multifield-strength study. Cereb Cortex 2022; 33:729-739. [PMID: 35271703 PMCID: PMC9890456 DOI: 10.1093/cercor/bhac096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test-retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.
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Affiliation(s)
- Shohei Fujita
- Corresponding author: Department of Radiology, Juntendo University School of Medicine, 12-1 Hongo, Bunkyo, Tokyo 113-8421, Japan.
| | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | | | | | | | - Issei Fukunaga
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University, Tokyo, Japan
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Yasuhiro Hagiwara
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Yutaka Matsuyama
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Michela Tosetti
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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Cao X, Liao C, Iyer SS, Wang Z, Zhou Z, Dai E, Liberman G, Dong Z, Gong T, He H, Zhong J, Bilgic B, Setsompop K. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. Magn Reson Med 2022; 88:133-150. [PMID: 35199877 DOI: 10.1002/mrm.29194] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve image quality and accelerate the acquisition of 3D MR fingerprinting (MRF). METHODS Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low-rank constraint and a modified spiral-projection spatiotemporal encoding scheme called tiny golden-angle shuffling were implemented for rapid whole-brain high-resolution quantitative mapping. Reconstruction parameters such as the locally low-rank regularization parameter and the subspace rank were tuned using retrospective in vivo data and simulated examinations. B0 inhomogeneity correction using multifrequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect. RESULTS The proposed MRF acquisition and reconstruction framework yields high-quality 1-mm isotropic whole-brain quantitative maps in 2 min at better quality compared with 6-min acquisitions of prior approaches. The proposed method was validated to not induce bias in T1 and T2 mapping. High-quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 min using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. CONCLUSIONS The proposed tiny golden-angle shuffling, MRF with optimized spiral-projection trajectory and subspace reconstruction enables high-resolution quantitative mapping in ultrafast acquisition time.
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Affiliation(s)
- Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, California, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Gilad Liberman
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ting Gong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Cambridge, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
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40
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Perlman O, Farrar CT, Heo HY. MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification. NMR IN BIOMEDICINE 2022; 36:e4710. [PMID: 35141967 PMCID: PMC9808671 DOI: 10.1002/nbm.4710] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/18/2022] [Accepted: 02/04/2022] [Indexed: 05/11/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Christian T. Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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41
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Gu Y, Wang L, Yang H, Wu Y, Kim K, Zhu Y, Androjna C, Zhu X, Chen Y, Zhong K, Yu X. Three-dimensional high-resolution T 1 and T 2 mapping of whole macaque brain at 9.4 T using magnetic resonance fingerprinting. Magn Reson Med 2022; 87:2901-2913. [PMID: 35129226 DOI: 10.1002/mrm.29181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/10/2022] [Accepted: 01/10/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE Quantitative T1 and T2 mapping in non-human primates with whole-brain coverage is challenged by the requirement of sub-millimeter resolution and the inhomogeneity of the transmit magnetic field (B1 + ) covering a large field of view. The goal of the current study is to develop a magnetic resonance fingerprinting (MRF) method for simultaneous T1 and T2 mapping of the entire macaque brain within feasible scan time. METHODS A three-dimensional (3D) MRF sequence with both inversion- and T2 -preparation modules was developed and evaluated on a 9.4 T preclinical scanner. Data acquisition used a 3D stack-of-spirals trajectory, with undersampling along both the in-plane and the through-plane directions. The effect of B1 + inhomogeneity was accounted for by matching the acquired fingerprint to a dictionary simulated with the B1 + factors measured from a separate scan. In vitro and ex vivo studies were performed to evaluate the accuracy and the undersampling capacity of the MRF method. The application of the MRF method for in vivo, brain-wide T1 and T2 mapping was demonstrated on macaques at 4, 6, and 12 years of age. RESULTS The MRF method enabled highly repeatable T1 and T2 mapping at high spatial resolution (0.35 × 0.35 × 1 mm3 ) with an acceleration factor of 24. In vivo studies showed significant age-related T2 reduction in deep gray nuclei including the globus pallidus, the putamen, and the caudate nucleus. CONCLUSIONS This study demonstrates the first MRF study for brain-wide, multi-parametric quantification in non-human primates with sub-millimeter resolution.
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Affiliation(s)
- Yuning Gu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Lulu Wang
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Hongyi Yang
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,School of Graduate Studies, Science Island Branch, University of Science and Technology of China, Hefei, China
| | - Yun Wu
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Kihwan Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yuran Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Charlie Androjna
- Center for Preclinical Magnetic Resonance Imaging, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kai Zhong
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China.,Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,Biomedical Engineering Department, Peking University, Beijing, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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42
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Pirkl CM, Cencini M, Kurzawski JW, Waldmannstetter D, Li H, Sekuboyina A, Endt S, Peretti L, Donatelli G, Pasquariello R, Costagli M, Buonincontri G, Tosetti M, Menzel MI, Menze BH. Learning residual motion correction for fast and robust 3D multiparametric MRI. Med Image Anal 2022; 77:102387. [DOI: 10.1016/j.media.2022.102387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/25/2021] [Accepted: 02/01/2022] [Indexed: 11/28/2022]
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43
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Yu N, Kim JY, Han D, Kim SY, Lee HM, Kim DH, Kim HG. Three-Dimensional Magnetic Resonance Fingerprinting in Neonates: Quantifying Regional Difference and Maturation in the Brain. Invest Radiol 2022; 57:44-51. [PMID: 34101674 DOI: 10.1097/rli.0000000000000800] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Magnetic resonance fingerprinting (MRF) allows the simultaneous measurement of multiple tissue properties in a single acquisition. Three-dimensional (3D) MRF with high spatial resolution can be used for neonatal brain imaging. The aim of this study is to apply 3D MRF to neonates and show regional differences and maturation in the brain. MATERIALS AND METHODS In this prospective study, 3D MRF using hybrid radial-interleaved acquisition was performed on phantoms and neonates from December 2019 to October 2020. For the reconstruction of 3D MRF, singular value decomposition was applied to reduce reconstruction time, and the iterative reconstruction technique was applied to improve image quality. The accuracies of T1 and T2 values derived from 3D MRF were evaluated in a phantom experiment. Regional T1 and T2 values were obtained from neonates' brain T1 and T2 maps derived from 3D MRF. Regional T1 and T2 values were compared, and their changes according to corrected gestational age were evaluated. RESULTS The acquisition time for 3D MRF with a spatial resolution of 0.7 × 0.7 × 2 mm3 was less than 5 minutes. The phantom study showed high correlation between T1 and T2 values derived from 3D MRF and those from conventional spin echo sequences (T1, R2 = 0.998, P < 0.001; T2, R2 = 0.998, P < 0.001). Three-dimensional MRF was performed in 25 neonates (15 boys, 10 girls; median corrected gestational age, 263 days; interquartile range, 10 days). In neonates, T1 and T2 values differed in the frontal (median [interquartile range], 2785 [2684-2888] milliseconds and 189.8 [176.7-222.9] milliseconds), parietal (2849 [2741-2950] milliseconds and 191.6 [167.5-232.9] milliseconds), and occipital white matter (2621 [2513-2722] milliseconds and 162.9 [143.5-186.1] milliseconds), showing lower values in occipital white matter (P < 0.001). Regional T1 values showed a negative relationship with corrected gestational age (coefficient, -0.775 to -0.480; P < 0.05). CONCLUSIONS Fast and high spatial resolution 3D MRF was applied to neonates. T1 and T2 maps derived from 3D MRF enabled the quantification of regional differences and maturation in the neonatal brain.
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Affiliation(s)
- Nali Yu
- From the Departments of Pediatrics
| | - Jee Young Kim
- Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea
| | | | | | | | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea
| | - Hyun Gi Kim
- Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea
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44
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Wang C, Kim K, Yu X. Rapid In Vivo Quantification of Creatine Kinase Activity by Phosphorous-31 Magnetic Resonance Spectroscopic Fingerprinting ( 31P-MRSF). Methods Mol Biol 2022; 2393:597-609. [PMID: 34837201 DOI: 10.1007/978-1-0716-1803-5_31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Creatine kinase (CK) plays an important role in tissue metabolism by providing a buffering mechanism for maintaining a constant supply of adenosine triphosphate (ATP) during metabolic perturbations. Phosphorous-31 magnetic resonance spectroscopy (31P-MRS) employing magnetization transfer techniques is the only noninvasive method for measuring the rate of ATP synthesis via creatine kinase. However, due to the low concentrations of phosphate metabolites, current 31P-MRS methods require long acquisition time to achieve adequate measurement accuracy. In this chapter, we present a new framework of data acquisition and parameter estimation, the 31P magnetic resonance spectroscopic fingerprinting (31P-MRSF) method, for rapid quantification of CK reaction rate constant in the hindlimb of small laboratory animals.
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Affiliation(s)
- Charlie Wang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kihwan Kim
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
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45
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Abstract
Magnetic resonance fingerprinting (MRF) is increasingly being used to evaluate brain development and differentiate normal and pathologic tissues in children. MRF can provide reliable and accurate intrinsic tissue properties, such as T1 and T2 relaxation times. MRF is a powerful tool in evaluating brain disease in pediatric population. MRF is a new quantitative MR imaging technique for rapid and simultaneous quantification of multiple tissue properties.
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Affiliation(s)
- Sheng-Che Hung
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
| | - Pew-Thian Yap
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA.
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46
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Chen Y, Lu L, Zhu T, Ma D. Technical overview of magnetic resonance fingerprinting and its applications in radiation therapy. Med Phys 2021; 49:2846-2860. [PMID: 34633687 DOI: 10.1002/mp.15254] [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: 08/01/2021] [Revised: 08/23/2021] [Accepted: 09/17/2021] [Indexed: 11/07/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is an emerging imaging technique for rapid and simultaneous quantification of multiple tissue properties. The technique has been developed for quantitative imaging of different organs. The obtained quantitative measures have the potential to improve multiple steps of a typical radiotherapy workflow and potentially further improve integration of magnetic resonance imaging guided clinical decision making. In this review paper, we first provide a technical overview of the MRF method from data acquisition to postprocessing, along with recent development in advanced reconstruction methods. We further discuss critical aspects that could influence its usage in radiation therapy, such as accuracy and precision, repeatability and reproducibility, geometric distortion, and motion robustness. Finally, future directions for MRF application in radiation therapy are discussed.
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Affiliation(s)
- Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Lan Lu
- Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, USA
| | - Tong Zhu
- Radiation Oncology, Washington University in St Louis, St Louis, Missouri, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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47
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Jordan SP, Hu S, Rozada I, McGivney DF, Boyacioğlu R, Jacob DC, Huang S, Beverland M, Katzgraber HG, Troyer M, Griswold MA, Ma D. Automated design of pulse sequences for magnetic resonance fingerprinting using physics-inspired optimization. Proc Natl Acad Sci U S A 2021; 118:e2020516118. [PMID: 34593630 PMCID: PMC8501900 DOI: 10.1073/pnas.2020516118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/18/2021] [Indexed: 11/18/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a method to extract quantitative tissue properties such as [Formula: see text] and [Formula: see text] relaxation rates from arbitrary pulse sequences using conventional MRI hardware. MRF pulse sequences have thousands of tunable parameters, which can be chosen to maximize precision and minimize scan time. Here, we perform de novo automated design of MRF pulse sequences by applying physics-inspired optimization heuristics. Our experimental data suggest that systematic errors dominate over random errors in MRF scans under clinically relevant conditions of high undersampling. Thus, in contrast to prior optimization efforts, which focused on statistical error models, we use a cost function based on explicit first-principles simulation of systematic errors arising from Fourier undersampling and phase variation. The resulting pulse sequences display features qualitatively different from previously used MRF pulse sequences and achieve fourfold shorter scan time than prior human-designed sequences of equivalent precision in [Formula: see text] and [Formula: see text] Furthermore, the optimization algorithm has discovered the existence of MRF pulse sequences with intrinsic robustness against shading artifacts due to phase variation.
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Affiliation(s)
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Ignacio Rozada
- Optimization Solutions, 1QBit, Vancouver, BC V6E 4B1, Canada
| | - Debra F McGivney
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Rasim Boyacioğlu
- Radiology Department, Case Western Reserve University, Cleveland, OH 44106
| | - Darryl C Jacob
- Department of Physics and Astronomy, Texas A & M University, College Station, TX 77843
| | - Sherry Huang
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | | | | | | | - Mark A Griswold
- Radiology Department, Case Western Reserve University, Cleveland, OH 44106
| | - Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106;
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Ding H, Velasco C, Ye H, Lindner T, Grech-Sollars M, O’Callaghan J, Hiley C, Chouhan MD, Niendorf T, Koh DM, Prieto C, Adeleke S. Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer. Cancers (Basel) 2021; 13:4742. [PMID: 34638229 PMCID: PMC8507535 DOI: 10.3390/cancers13194742] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/08/2021] [Accepted: 09/16/2021] [Indexed: 11/25/2022] Open
Abstract
Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.
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Affiliation(s)
- Hao Ding
- Imperial College School of Medicine, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Huihui Ye
- State Key Laboratory of Modern Optical instrumentation, Zhejiang University, Hangzhou 310027, China;
| | - Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg Eppendorf, 20246 Hamburg, Germany;
| | - Matthew Grech-Sollars
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Surrey GU2 7XX, UK;
- Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK
| | - James O’Callaghan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Crispin Hiley
- Cancer Research UK, Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK;
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Manil D. Chouhan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck, Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany;
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SM2 5NG, UK;
- Department of Radiology, Royal Marsden Hospital, London SW3 6JJ, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Sola Adeleke
- High Dimensional Neurology Group, Queen’s Square Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Oncology, Guy’s & St Thomas’ Hospital, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, King’s College London, London WC2R 2LS, UK
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Stout JN, Liao C, Gagoski B, Turk EA, Feldman HA, Bibbo C, Barth WH, Shainker SA, Wald LL, Grant PE, Adalsteinsson E. Quantitative T 1 and T 2 mapping by magnetic resonance fingerprinting (MRF) of the placenta before and after maternal hyperoxia. Placenta 2021; 114:124-132. [PMID: 34537569 DOI: 10.1016/j.placenta.2021.08.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 06/16/2021] [Accepted: 08/05/2021] [Indexed: 11/13/2022]
Abstract
INTRODUCTION MR relaxometry has been used to assess placental exchange function, but methods to date are not sufficiently fast to be robust to placental motion. Magnetic resonance fingerprinting (MRF) permits rapid, voxel-wise, intrinsically co-registered T1 and T2 mapping. After characterizing measurement error, we scanned pregnant women during air and oxygen breathing to demonstrate MRF's ability to detect placental oxygenation changes. METHODS The accuracy of FISP-based, sliding-window reconstructed MRF was tested on phantoms. MRF scans in 9-s breath holds were acquired at 3T in 31 pregnant women during air and oxygen breathing. A mixed effects model was used to test for changes in placenta relaxation times between physiological states, to assess the dependency on gestational age (GA), and the impact of placental motion. RESULTS MRF estimates of known phantom relaxation times resulted in mean absolute errors for T1 of 92 ms (4.8%), but T2 was less accurate at 16 ms (13.6%). During normoxia, placental T1 = 1825 ± 141 ms (avg ± standard deviation) and T2 = 60 ± 16 ms (gestational age range 24.3-36.7, median 32.6 weeks). In the statistical model, placental T2 rose and T1 remained contant after hyperoxia, and no GA dependency was observed for T1 or T2. DISCUSSION Well-characterized, motion-robust MRF was used to acquire T1 and T2 maps of the placenta. Changes with hyperoxia are consistent with a net increase in oxygen saturation. Toward the goal of whole-placenta quantitative oxygenation imaging over time, we aim to implement 3D MRF with integrated motion correction to improve T2 accuracy.
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Affiliation(s)
- Jeffrey N Stout
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA.
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Borjan Gagoski
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Esra Abaci Turk
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Henry A Feldman
- Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Carolina Bibbo
- Brigham and Women's Hospital, Division of Maternal-Fetal Medicine, Boston, MA, 02115, USA
| | - William H Barth
- Maternal-Fetal Medicine, Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Scott A Shainker
- Maternal-Fetal Medicine, Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 02129, USA
| | - P Ellen Grant
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Elfar Adalsteinsson
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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50
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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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