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Fujita S, Hagiwara A, Kimura K, Taniguchi Y, Ito K, Nagao H, Takizawa M, Uchida W, Kamagata K, Tateishi U, Aoki S. Three-dimensional simultaneous T1 and T2* relaxation times and quantitative susceptibility mapping at 3 T: A multicenter validation study. Magn Reson Imaging 2024; 112:100-106. [PMID: 38971266 DOI: 10.1016/j.mri.2024.07.004] [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: 03/05/2024] [Revised: 06/27/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024]
Abstract
We aimed to determine the intra-site repeatability and cross-site reproducibility of T1 and T2* relaxation times and quantitative susceptibility (χ) values obtained through quantitative parameter mapping (QPM) at 3 T. This prospective study included three 3-T scanners with the same hardware and software platform at three sites. The brains of twelve healthy volunteers were scanned three times using QPM at three sites. Intra-site repeatability and cross-site reproducibility were evaluated based on voxel-wise and region-of-interest analyses. The within-subject coefficient of variation (wCV), within-subject standard deviation (wSD), linear regression, Bland-Altman plot, and intraclass correlation coefficient (ICC) were used for evaluation. The intra-site repeatability wCV was 11.9 ± 6.86% for T1 and 3.15 ± 0.03% for T2*, and wSD of χ at 3.35 ± 0.10 parts per billion (ppb). Intra-site ICC(1,k) values for T1, T2*, and χ were 0.878-0.904, 0.972-0.976, and 0.966-0.972, respectively, indicating high consistency within the same scanner. Linear regression analysis revealed a strong agreement between measurements from each site and the site-average measurement, with R-squared values ranging from 0.79 to 0.83 for T1, 0.94-0.95 for T2*, and 0.95-0.96 for χ. The cross-site wCV was 13.4 ± 5.47% for T1 and 3.69 ± 2.25% for T2*, and cross-site wSD of χ at 4.08 ± 3.22 ppb. The cross-site ICC(2,1) was 0.707, 0.913, and 0.902 for T1, T2*, and χ, respectively. QPM provides T1, T2*, and χ values with an intra-site repeatability of <12% and cross-site reproducibility of <14%. These findings may contribute to the development of multisite studies.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University, 1-2-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Department of Radiology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University, 1-2-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Koichiro Kimura
- Department of Radiology and Nuclear Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Yo Taniguchi
- Medical Systems Research & Development Center, FUJIFILM Corporation
| | - Kosuke Ito
- Medical Systems Research & Development Center, FUJIFILM Healthcare Corporation
| | - Hisako Nagao
- Medical Systems Research & Development Center, FUJIFILM Healthcare Corporation
| | - Masahiro Takizawa
- Medical Systems Research & Development Center, FUJIFILM Healthcare Corporation
| | - Wataru Uchida
- Department of Radiology, Juntendo University, 1-2-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Department of Health Data Science, Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba 279-0013, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University, 1-2-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Ukihide Tateishi
- Department of Radiology and Nuclear Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, 1-2-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Department of Health Data Science, Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba 279-0013, Japan
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2
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Buelo CJ, Velikina J, Mao L, Zhao R, Yuan Q, Ghasabeh MA, Ruschke S, Karampinos DC, Harris DT, Mattison RJ, Jeng MR, Pedrosa I, Kamel IR, Vasanawala S, Yokoo T, Reeder SB, Hernando D. Multicenter, multivendor validation of liver quantitative susceptibility mapping in patients with iron overload at 1.5 T and 3 T. Magn Reson Med 2024. [PMID: 39238238 DOI: 10.1002/mrm.30251] [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: 04/29/2024] [Revised: 06/21/2024] [Accepted: 07/27/2024] [Indexed: 09/07/2024]
Abstract
PURPOSE To evaluate the repeatability and reproducibility of QSM of the liver via single breath-hold chemical shift-encoded MRI at both 1.5 T and 3 T in a multicenter, multivendor study in subjects with iron overload. METHODS This prospective study included four academic medical centers with three different MRI vendors at 1.5 T and 3 T. Subjects with known or suspected liver iron overload underwent multi-echo spoiled gradient-recalled-echo scans at each field strength. A subset received repeatability testing at either 1.5 T or 3 T. Susceptibility andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed from the multi-echo images and analyzed at a single center. QSM-measured susceptibility was compared withR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and a commercial R2-based liver iron concentration method across centers and field strengths using linear regression and F-tests on the intercept and slope. Field-strength reproducibility and test/retest repeatability were evaluated using Bland-Altman analysis. RESULTS A total of 155/80 data sets (test/retest) were available at 1.5 T, and 159/70 data sets (test/retest) were available at 3 T. Calibrations across sites were reproducible, with some variability (e.g., susceptibility slope with liver iron concentration ranged from 0.102 to 0.123 g/[mg· $$ \cdotp $$ ppm] across centers at 1.5 T). Field strength reproducibility was good (concordance correlation coefficient = 0.862), and test/retest repeatability was excellent (intraclass correlation coefficient = 0.951). CONCLUSION QSM as an imaging biomarker of liver iron overload is feasible and repeatable across centers and MR vendors. It may be complementary withR 2 * $$ {\mathrm{R}}_2^{\ast } $$ as they are obtained from the same acquisition. Although good reproducibility was observed, liver QSM may benefit from standardization of acquisition parameters. Overall, QSM is a promising method for liver iron quantification.
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Affiliation(s)
- Collin J Buelo
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Julia Velikina
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lu Mao
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ruiyang Zhao
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
- GE Healthcare, Waukesha, Wisconsin, USA
| | - Qing Yuan
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar and Health, Technical University of Munich, Munich, Germany
| | | | - David T Harris
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ryan J Mattison
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Michael R Jeng
- Department of Pediatrics, Stanford University, Stanford, California, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ihab R Kamel
- Department of Radiology, The John Hopkins University, Baltimore, Maryland, USA
| | | | - Takeshi Yokoo
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Emergency Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Pirozzi MA, Canna A, Nardo FD, Sansone M, Trojsi F, Cirillo M, Esposito F. Reliability of quantitative magnetic susceptibility imaging metrics for cerebral cortex and major subcortical structures. J Neuroimaging 2024. [PMID: 39210534 DOI: 10.1111/jon.13234] [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: 06/28/2024] [Revised: 08/02/2024] [Accepted: 08/18/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND AND PURPOSE Susceptibility estimates derived from quantitative susceptibility mapping (QSM) images for the cerebral cortex and major subcortical structures are variably reported in brain magnetic resonance imaging (MRI) studies, as average of all (μ all ${{{{\mu}}}_{{\mathrm{all}}}}$ ), absolute (μ abs ${{{{\mu}}}_{{\mathrm{abs}}}}$ ), or positive- (μ p ${{{{\mu}}}_{\mathrm{p}}}$ ) and negative-only (μ n ${{{{\mu}}}_{\mathrm{n}}}$ ) susceptibility values using a region of interest (ROI) approach. This pilot study presents a reliability analysis of currently used ROI-QSM metrics and an alternative ROI-based approach to obtain voxel-weighted ROI-QSM metrics (μ wp ${{{{\mu}}}_{{\mathrm{wp}}}}$ andμ wn ${{{{\mu}}}_{{\mathrm{wn}}}}$ ). METHODS Ten healthy subjects underwent repeated (test-retest) 3-dimensional multi-echo gradient-echo (3DMEGE) 3 Tesla MRI measurements. Complex-valued 3DMEGE images were acquired and reconstructed with slice thicknesses of 1 and 2 mm (3DMEGE1, 3DMEGE2) along with 3DT1-weighted isometric (voxel 1 mm3) images for independent registration and ROI segmentation. Agreement, consistency, and reproducibility of ROI-QSM metrics were assessed through Bland-Altman analysis, intraclass correlation coefficient, and interscan and intersubject coefficient of variation (CoV). RESULTS All ROI-QSM metrics exhibited good to excellent consistency and test-retest agreement with no proportional bias. Interscan CoV was higher forμ all ${{{{\mu}}}_{{\mathrm{all}}}}$ in comparison to the other metrics where it was below 15%, in both 3DMEGE1 and 3DMEGE2 datasets. Intersubject CoV forμ all ${{{{\mu}}}_{{\mathrm{all}}}}$ andμ abs ${{{{\mu}}}_{{\mathrm{abs}}}}$ exceeded 50% in all ROIs. CONCLUSIONS Among the evaluated ROI-QSM metrics,μ all ${{{{\mu}}}_{{\mathrm{all}}}}$ andμ abs ${{{{\mu}}}_{{\mathrm{abs}}}}$ estimates were less reliable, whereas separating positive and negative values (usingμ p , μ n , μ wp , μ wn ${{{{\mu}}}_{\mathrm{p}}},\ {{{{\mu}}}_{\mathrm{n}}},\ {{{{\mu}}}_{{\mathrm{wp}}}},\ {{{{\mu}}}_{{\mathrm{wn}}}}$ ) improved the reproducibility within, and the comparability between, subjects, even when reducing the slice thickness. These preliminary findings may offer valuable insights toward standardizing ROI-QSM metrics across different patient cohorts and imaging settings in future clinical MRI studies.
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Affiliation(s)
- Maria Agnese Pirozzi
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Antonietta Canna
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Federica Di Nardo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Sansone
- Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy
| | - Francesca Trojsi
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
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Hervouin A, Bézy-Wendling J, Noury F. How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T. NMR IN BIOMEDICINE 2024:e5182. [PMID: 38993048 DOI: 10.1002/nbm.5182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/06/2024] [Accepted: 05/06/2024] [Indexed: 07/13/2024]
Abstract
Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.
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Affiliation(s)
| | | | - Fanny Noury
- Univ Rennes, Inserm, LTSI-UMR 1099, Rennes, France
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5
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Radunsky D, Solomon C, Stern N, Blumenfeld-Katzir T, Filo S, Mezer A, Karsa A, Shmueli K, Soustelle L, Duhamel G, Girard OM, Kepler G, Shrot S, Hoffmann C, Ben-Eliezer N. A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla. PLoS One 2024; 19:e0297244. [PMID: 38820354 PMCID: PMC11142522 DOI: 10.1371/journal.pone.0297244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 01/01/2024] [Indexed: 06/02/2024] Open
Abstract
Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.
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Affiliation(s)
- Dvir Radunsky
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Chen Solomon
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Shir Filo
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv Mezer
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | | | | | - Gal Kepler
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Shai Shrot
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Chen Hoffmann
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States of America
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Seada SA, van der Eerden AW, Boon AJW, Hernandez-Tamames JA. Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design. Neuroimage Clin 2023; 39:103506. [PMID: 37696098 PMCID: PMC10500558 DOI: 10.1016/j.nicl.2023.103506] [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: 03/30/2023] [Revised: 06/21/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.
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Affiliation(s)
- Samy Abo Seada
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Anke W van der Eerden
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Agnita J W Boon
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Imaging Physics, TU Delft, The Netherlands.
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Bordin V, Pirastru A, Bergsland N, Cazzoli M, Baselli G, Baglio F. Optimal echo times for quantitative susceptibility mapping: A test-retest study on basal ganglia and subcortical brain nuclei. Neuroimage 2023; 278:120272. [PMID: 37437701 DOI: 10.1016/j.neuroimage.2023.120272] [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: 04/13/2023] [Revised: 06/16/2023] [Accepted: 07/09/2023] [Indexed: 07/14/2023] Open
Abstract
Quantitative Susceptibility Mapping (QSM) is a recent MRI-technique able to quantify the bulk magnetic susceptibility of myelin, iron, and calcium in the brain. Its variability across different acquisition parameters has prompted the need for standardisation across multiple centres and MRI vendors. However, a high level of agreement between repeated imaging acquisitions is equally important. With this study we aimed to assess the inter-scan repeatability of an optimised multi-echo GRE sequence in 28 healthy volunteers. We extracted and compared the susceptibility measures from the scan and rescan acquisitions across 7 bilateral brain regions (i.e., 14 regions of interest (ROIs)) relevant for neurodegeneration. Repeatability was first assessed while reconstructing QSM with a fixed number of echo times (i.e., 8). Excellent inter-scan repeatability was found for putamen, globus pallidus and caudate nucleus, while good performance characterised the remaining structures. An increased variability was instead noted for small ROIs like red nucleus and substantia nigra. Secondly, we assessed the impact exerted on repeatability by the number of echoes used to derive QSM maps. Results were impacted by this parameter, especially in smaller regions. Larger brain structures, on the other hand, showed more consistent performance. Nevertheless, with either 8 or 7 echoes we managed to obtain good inter-scan repeatability on almost all ROIs. These findings indicate that the designed acquisition/reconstruction protocol has wide applicability, particularly in clinical or research settings involving longitudinal acquisitions (e.g. rehabilitation studies).
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Affiliation(s)
- Valentina Bordin
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Alice Pirastru
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Niels Bergsland
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy; Department of Neurology, Buffalo Neuroimaging Analysis Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Marta Cazzoli
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Giuseppe Baselli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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Feasibility and intra-and interobserver reproducibility of quantitative susceptibility mapping with radiomic features for intracranial dissecting intramural hematomas and atherosclerotic calcifications. Sci Rep 2023; 13:3651. [PMID: 36871117 PMCID: PMC9985647 DOI: 10.1038/s41598-023-30745-2] [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: 10/19/2022] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) for 61 patients with dissecting intramural hematomas (n = 36) or atherosclerotic calcifications (n = 25) in intracranial vertebral arteries were collected to assess intra- and interobserver reproducibility in a 3.0-T MR system between January 2015 and December 2017. Two independent observers each segmented regions of interest for lesions twice. The reproducibility was evaluated using intra-class correlation coefficients (ICC) and within-subject coefficients of variation (wCV) for means and concordance correlation coefficients (CCC) and ICC for radiomic features (CCC and ICC > 0.85) were used. Mean QSM values were 0.277 ± 0.092 ppm for dissecting intramural hematomas and - 0.208 ± 0.078 ppm for atherosclerotic calcifications. ICCs and wCVs were 0.885-0.969 and 6.5-13.7% in atherosclerotic calcifications and 0.712-0.865 and 12.4-18.7% in dissecting intramural hematomas, respectively. A total of 9 and 19 reproducible radiomic features were observed in dissecting intramural hematomas and atherosclerotic calcifications, respectively. QSM measurements in dissecting intramural hematomas and atherosclerotic calcifications were feasible and reproducible between intra- and interobserver comparisons, and some reproducible radiomic features were demonstrated.
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Fuchs P, Shmueli K. Incomplete spectrum QSM using support information. Front Neurosci 2023; 17:1130524. [PMID: 37139523 PMCID: PMC10149841 DOI: 10.3389/fnins.2023.1130524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/28/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction Reconstructing a bounded object from incomplete k-space data is a well posed problem, and it was recently shown that this incomplete spectrum approach can be used to reconstruct undersampled MRI images with similar quality to compressed sensing approaches. Here, we apply this incomplete spectrum approach to the field-to-source inverse problem encountered in quantitative magnetic susceptibility mapping (QSM). The field-to-source problem is an ill-posed problem because of conical regions in frequency space where the dipole kernel is zero or very small, which leads to the kernel's inverse being ill-defined. These "ill-posed" regions typically lead to streaking artifacts in QSM reconstructions. In contrast to compressed sensing, our approach relies on knowledge of the image-space support, more commonly referred to as the mask, of our object as well as the region in k-space with ill-defined values. In the QSM case, this mask is usually available, as it is required for most QSM background field removal and reconstruction methods. Methods We tuned the incomplete spectrum method (mask and band-limit) for QSM on a simulated dataset from the most recent QSM challenge and validated the QSM reconstruction results on brain images acquired in five healthy volunteers, comparing incomplete spectrum QSM to current state-of-the art-methods: FANSI, nonlinear dipole inversion, and conventional thresholded k-space division. Results Without additional regularization, incomplete spectrum QSM performs slightly better than direct QSM reconstruction methods such as thresholded k-space division (PSNR of 39.9 vs. 39.4 of TKD on a simulated dataset) and provides susceptibility values in key iron-rich regions similar or slightly lower than state-of-the-art algorithms, but did not improve the PSNR in comparison to FANSI or nonlinear dipole inversion. With added (ℓ1-wavelet based) regularization the new approach produces results similar to compressed sensing based reconstructions (at sufficiently high levels of regularization). Discussion Incomplete spectrum QSM provides a new approach to handle the "ill-posed" regions in the frequency-space data input to QSM.
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10
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Naji N, Lauzon ML, Seres P, Stolz E, Frayne R, Lebel C, Beaulieu C, Wilman AH. Multisite reproducibility of quantitative susceptibility mapping and effective transverse relaxation rate in deep gray matter at 3 T using locally optimized sequences in 24 traveling heads. NMR IN BIOMEDICINE 2022; 35:e4788. [PMID: 35704837 DOI: 10.1002/nbm.4788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/28/2022] [Accepted: 06/05/2022] [Indexed: 06/15/2023]
Abstract
Iron concentration in the human brain plays a crucial role in several neurodegenerative diseases and can be monitored noninvasively using quantitative susceptibility mapping (QSM) and effective transverse relaxation rate (R2 *) mapping from multiecho T2 *-weighted images. Large population studies enable better understanding of pathologies and can benefit from pooling multisite data. However, reproducibility may be compromised between sites and studies using different hardware and sequence protocols. This work investigates QSM and R2 * reproducibility at 3 T using locally optimized sequences from three centers and two vendors, and investigates possible reduction of cross-site variability through postprocessing approaches. Twenty-four healthy subjects traveled between three sites and were scanned twice at each site. Scan-rescan measurements from seven deep gray matter regions were used for assessing within-site and cross-site reproducibility using intraclass correlation coefficient (ICC) and within-subject standard deviation (SDw) measures. In addition, multiple QSM and R2 * postprocessing options were investigated with the aim to minimize cross-site sequence-related variations, including: mask generation approach, echo-timing selection, harmonizing spatial resolution, field map estimation, susceptibility inversion method, and linear field correction for magnitude images. The same-subject cross-site region of interest measurements for QSM and R2 * were highly correlated (R2 ≥ 0.94) and reproducible (mean ICC of 0.89 and 0.82 for QSM and R2 *, respectively). The mean cross-site SDw was 4.16 parts per billion (ppb) for QSM and 1.27 s-1 for R2 *. For within-site measurements of QSM and R2 *, the mean ICC was 0.97 and 0.87 and mean SDw was 2.36 ppb and 0.97 s-1 , respectively. The precision level is regionally dependent and is reduced in the frontal lobe, near brain edges, and in white matter regions. Cross-site QSM variability (mean SDw) was reduced up to 46% through postprocessing approaches, such as masking out less reliable regions, matching available echo timings and spatial resolution, avoiding the use of the nonconsistent magnitude contrast between scans in field estimation, and minimizing streaking artifacts.
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Affiliation(s)
- Nashwan Naji
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - M Louis Lauzon
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Emily Stolz
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Catherine Lebel
- Department of Radiology, Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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11
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Rahmanzadeh R, Weigel M, Lu PJ, Melie-Garcia L, Nguyen TD, Cagol A, La Rosa F, Barakovic M, Lutti A, Wang Y, Bach Cuadra M, Radue EW, Gaetano L, Kappos L, Kuhle J, Magon S, Granziera C. A comparative assessment of myelin-sensitive measures in multiple sclerosis patients and healthy subjects. Neuroimage Clin 2022; 36:103177. [PMID: 36067611 PMCID: PMC9468574 DOI: 10.1016/j.nicl.2022.103177] [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: 03/01/2022] [Revised: 08/22/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Multiple Sclerosis (MS) is a common neurological disease primarily characterized by myelin damage in lesions and in normal - appearing white and gray matter (NAWM, NAGM). Several quantitative MRI (qMRI) methods are sensitive to myelin characteristics by measuring specific tissue biophysical properties. However, there are currently few studies assessing the relative reproducibility and sensitivity of qMRI measures to MS pathology in vivo in patients. METHODS We performed two studies. The first study assessed of the sensitivity of qMRI measures to MS pathology: in this work, we recruited 150 MS and 100 healthy subjects, who underwent brain MRI at 3 T including quantitative T1 mapping (qT1), quantitative susceptibility mapping (QSM), magnetization transfer saturation imaging (MTsat) and myelin water imaging for myelin water fraction (MWF). The sensitivity of qMRIs to MS focal pathology (MS lesions vs peri-plaque white/gray matter (PPWM/PPGM)) was studied lesion-wise; the sensitivity to diffuse normal appearing (NA) pathology was measured using voxel-wise threshold-free cluster enhancement (TFCE) in NAWM and vertex-wise inflated cortex analysis in NAGM. Furthermore, the sensitivity of qMRI to the identification of lesion tissue was investigated using a voxel-wise logistic regression analysis to distinguish MS lesion and PP voxels. The second study assessed the reproducibility of myelin-sensitive qMRI measures in a single scanner. To evaluate the intra-session and inter-session reproducibility of qMRI measures, we have investigated 10 healthy subjects, who underwent two brain 3 T MRIs within the same day (without repositioning), and one after 1-week interval. Five region of interest (ROIs) in white and deep grey matter areas were segmented, and inter- and intra- session reproducibility was studied using the intra-class correlation coefficient (ICC). Further, we also investigated the voxel-wise reproducibility of qMRI measures in NAWM and NAGM. RESULTS qT1 and QSM showed the highest sensitivity to distinguish MS focal WM and cortical pathology from peri-plaque WM (P < 0.0001), although QSM also showed the highest variance when applied to lesions. MWF and MTsat exhibited the highest sensitivity to NAWM pathology (P < 0.01). On the other hand, qT1 appeared to be the most sensitive measure to NAGM pathology (P < 0.01). All myelin-sensitive qMRI measures exhibited high inter/intra sessional ICCs in various WM and deep GM ROIs, in NAWM and in NAGM (ICC 0.82 ± 0.12). CONCLUSION This work shows that the applied qT1, MWF, MTsat and QSM are highly reproducible and exhibit differential sensitivity to focal and diffuse WM and GM pathology in MS patients.
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Affiliation(s)
- Reza Rahmanzadeh
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Alessandro Cagol
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | | | - Ludwig Kappos
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stefano Magon
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Corresponding author.
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12
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Salluzzi M, McCreary CR, Gobbi DG, Lauzon ML, Frayne R. Short-term repeatability and long-term reproducibility of quantitative MR imaging biomarkers in a single centre longitudinal study. Neuroimage 2022; 260:119488. [PMID: 35878725 DOI: 10.1016/j.neuroimage.2022.119488] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/21/2022] [Accepted: 07/14/2022] [Indexed: 10/16/2022] Open
Abstract
Quantitative imaging biomarkers (QIBs) can be defined as objective measures that are sensitive and specific to changes in tissue physiology. Provided the acquired QIBs are not affected by scanner changes, they could play an important role in disease diagnosis, prognosis, management, and treatment monitoring. The precision of selected QIBs was assessed from data collected on a 3-T scanner in four healthy participants over a 5-year period. Inevitable scanner changes and acquisition protocol revisions occurred during this time. Standard and custom processing pipelines were used to calculate regional brain volume, cortical thickness, T2, T2*, quantitative susceptibility, cerebral blood flow, axial, radial and mean diffusivity, peak width of skeletonized mean diffusivity, and fractional anisotropy from the acquired images. Coefficient of variation (CoV) and intra-class correlation (ICC) indices were determined in the short-term (i.e., repeatable over three acquisitions within 4 weeks) and in the long-term (i.e., reproducible over four acquisition sessions in 5 years). Precision indices varied based on acquisition technique, processing pipeline, and anatomical region. Good repeatability (average CoV=2.40% and ICC=0.78) and reproducibility (average CoV=8.86 % and ICC=0.72) were found over all QIBs. The best performance indices were obtained for diffusion derived biomarkers (CoV∼0.96% and ICCs=0.87); conversely, the poorest indices were found for the cerebral blood flow biomarker (CoV>10% and ICC<0.5). These results demonstrate that changes in protocol, along with hardware and software upgrades, did not affect the estimates of the selected biomarkers and their precision. Further characterization of the QIB is necessary to understand meaningful changes in the biomarkers in longitudinal studies of normal brain aging and translation to clinical research.
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Affiliation(s)
- Marina Salluzzi
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre (CIPAC), Foothills Medical Centre, Calgary, Alberta, Canada.
| | - Cheryl R McCreary
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - David G Gobbi
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre (CIPAC), Foothills Medical Centre, Calgary, Alberta, Canada
| | - Michel Louis Lauzon
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada; Calgary Image Processing and Analysis Centre (CIPAC), Foothills Medical Centre, Calgary, Alberta, Canada
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13
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Oxenford S, Roediger J, Neudorfer C, Milosevic L, Güttler C, Spindler P, Vajkoczy P, Neumann WJ, Kühn A, Horn A. Lead-OR: A multimodal platform for deep brain stimulation surgery. eLife 2022; 11:e72929. [PMID: 35594135 PMCID: PMC9177150 DOI: 10.7554/elife.72929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 05/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Methods Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. Results We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. Conclusions This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages. Funding Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR).
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Affiliation(s)
- Simón Oxenford
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Jan Roediger
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
- Charité — Universitätsmedizin Berlin, Einstein Center for Neurosciences BerlinBerlinGermany
| | - Clemens Neudorfer
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
- Center for Brain Circuit Therapeutics Department of Neurology, Brigham & Women’s Hospital, Harvard Medical SchoolBostonUnited States
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
| | - Luka Milosevic
- Institute of Biomedical Engineering, University of TorontoTorontoCanada
- Krembil Brain Institute, University Health NetworkTorontoCanada
| | - Christopher Güttler
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Philipp Spindler
- Department of Neurosurgery, Charité — Universitätsmedizin BerlinBerlinGermany
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité — Universitätsmedizin BerlinBerlinGermany
| | - Wolf-Julian Neumann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Andrea Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité — Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu BerlinBerlinGermany
- Center for Brain Circuit Therapeutics Department of Neurology, Brigham & Women’s Hospital, Harvard Medical SchoolBostonUnited States
- MGH Neurosurgery & Center for Neurotechnology and Neurorecovery (CNTR) at MGH Neurology Massachusetts General Hospital, Harvard Medical SchoolBostonUnited States
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14
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Longitudinal Observation of Asymmetric Iron Deposition in an Intracerebral Hemorrhage Model Using Quantitative Susceptibility Mapping. Symmetry (Basel) 2022. [DOI: 10.3390/sym14020350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) is used to obtain quantitative magnetic susceptibility maps of materials from magnitude and phase images acquired by three-dimensional gradient-echo using inverse problem-solving. Few preclinical studies have evaluated the intracerebral hemorrhage (ICH) model and asymmetric iron deposition. We created a rat model of ICH and compared QSM and conventional magnetic resonance imaging (MRI) during the longitudinal evaluation of ICH. Collagenase was injected in the right striatum of 12-week-old Wistar rats. QSM and conventional MRI were performed on days 0, 1, 7, and 28 after surgery using 7-Tesla MRI. Susceptibility, normalized signal value, and area of the hemorrhage site were statistically compared during image analysis. Susceptibility decreased monotonically up to day 7 but increased on day 28. Other imaging methods showed a significant increase in signal from day 0 to day 1 but a decreasing trend after day 1. During the area evaluation, conventional MRI methods showed an increase from day 0 to day 1; however, decreases were observed thereafter. QSM showed a significant increase from day 0 to day 1. The temporal evaluation of ICH by QSM suggested the possibility of detecting of asymmetric iron deposition for normal brain site.
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15
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Jellen LC, Lewis MM, Du G, Wang X, Galvis MLE, Krzyzanowski S, Capan CD, Snyder AM, Connor JR, Kong L, Mailman RB, Brundin P, Brundin L, Huang X. Low plasma serotonin linked to higher nigral iron in Parkinson's disease. Sci Rep 2021; 11:24384. [PMID: 34934078 PMCID: PMC8692322 DOI: 10.1038/s41598-021-03700-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/18/2021] [Indexed: 12/30/2022] Open
Abstract
A growing body of evidence suggests nigral iron accumulation plays an important role in the pathophysiology of Parkinson's disease (PD), contributing to dopaminergic neuron loss in the substantia nigra pars compacta (SNc). Converging evidence suggests this accumulation might be related to, or increased by, serotonergic dysfunction, a common, often early feature of the disease. We investigated whether lower plasma serotonin in PD is associated with higher nigral iron. We obtained plasma samples from 97 PD patients and 89 controls and MRI scans from a sub-cohort (62 PD, 70 controls). We measured serotonin concentrations using ultra-high performance liquid chromatography and regional iron content using MRI-based quantitative susceptibility mapping. PD patients had lower plasma serotonin (p < 0.0001) and higher nigral iron content (SNc: p < 0.001) overall. Exclusively in PD, lower plasma serotonin was correlated with higher nigral iron (SNc: r(58) = - 0.501, p < 0.001). This correlation was significant even in patients newly diagnosed (< 1 year) and stronger in the SNc than any other region examined. This study reveals an early, linear association between low serotonin and higher nigral iron in PD patients, which is absent in controls. This is consistent with a serotonin-iron relationship in the disease process, warranting further studies to determine its cause and directionality.
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Affiliation(s)
- Leslie C Jellen
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Mechelle M Lewis
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Pharmacology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Guangwei Du
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Xi Wang
- Public Health Sciences, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Martha L Escobar Galvis
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA
| | - Stanislaw Krzyzanowski
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA
| | - Colt D Capan
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA
| | - Amanda M Snyder
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - James R Connor
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Lan Kong
- Public Health Sciences, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Richard B Mailman
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
- Department of Pharmacology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Patrik Brundin
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA
| | - Lena Brundin
- Parkinson's Disease Center, Department of Neurodegenerative Science, Van Andel Institute, 333 Bostwick Ave NE, Grand Rapids, MI, 49503, USA.
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA.
| | - Xuemei Huang
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA.
- Department of Pharmacology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA.
- Departments of Neurosurgery and Radiology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA.
- Department of Kinesiology, Penn State University-Milton S. Hershey Medical Center, Hershey, PA, USA.
- Translational Brain Research Center, Penn State University-Hershey Medical Center, 500 University Dr., Mail Code H037, Hershey, PA, 17033, USA.
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16
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Keenan KE, Berman BP, Rýger S, Russek SE, Wang WT, Butman JA, Pham DL, Dagher J. Comparison of Phase Estimation Methods for Quantitative Susceptibility Mapping Using a Rotating-Tube Phantom. Radiol Res Pract 2021; 2021:1898461. [PMID: 34868681 PMCID: PMC8635951 DOI: 10.1155/2021/1898461] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 10/08/2021] [Indexed: 11/17/2022] Open
Abstract
Quantitative Susceptibility Mapping (QSM) is an MRI tool with the potential to reveal pathological changes from magnetic susceptibility measurements. Before phase data can be used to recover susceptibility (Δχ), the QSM process begins with two steps: data acquisition and phase estimation. We assess the performance of these steps, when applied without user intervention, on several variations of a phantom imaging task. We used a rotating-tube phantom with five tubes ranging from Δχ=0.05 ppm to Δχ=0.336 ppm. MRI data was acquired at nine angles of rotation for four different pulse sequences. The images were processed by 10 phase estimation algorithms including Laplacian, region-growing, branch-cut, temporal unwrapping, and maximum-likelihood methods, resulting in approximately 90 different combinations of data acquisition and phase estimation methods. We analyzed errors between measured and expected phases using the probability mass function and Cumulative Distribution Function. Repeatable acquisition and estimation methods were identified based on the probability of relative phase errors. For single-echo GRE and segmented EPI sequences, a region-growing method was most reliable with Pr (relative error <0.1) = 0.95 and 0.90, respectively. For multiecho sequences, a maximum-likelihood method was most reliable with Pr (relative error <0.1) = 0.97. The most repeatable multiecho methods outperformed the most repeatable single-echo methods. We found a wide range of repeatability and reproducibility for off-the-shelf MRI acquisition and phase estimation approaches, and this variability may prevent the techniques from being widely integrated in clinical workflows. The error was dominated in many cases by spatially discontinuous phase unwrapping errors. Any postprocessing applied on erroneous phase estimates, such as QSM's background field removal and dipole inversion, would suffer from error propagation. Our paradigm identifies methods that yield consistent and accurate phase estimates that would ultimately yield consistent and accurate Δχ estimates.
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Affiliation(s)
- Kathryn E. Keenan
- National Institute of Standards and Technology, Physical Measurement Laboratory, 325 Broadway, Boulder, CO 80305, USA
| | - Ben P. Berman
- The MITRE Corporation, 7515 Colshire Dr, McLean, VA 22102, USA
| | - Slávka Rýger
- National Institute of Standards and Technology, Physical Measurement Laboratory, 325 Broadway, Boulder, CO 80305, USA
| | - Stephen E. Russek
- National Institute of Standards and Technology, Physical Measurement Laboratory, 325 Broadway, Boulder, CO 80305, USA
| | - Wen-Tung Wang
- Henry M. Jackson Foundation, 10 Center Drive, Bethesda, MD 20892, USA
| | - John A. Butman
- Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, USA
| | - Dzung L. Pham
- Henry M. Jackson Foundation, 10 Center Drive, Bethesda, MD 20892, USA
| | - Joseph Dagher
- The MITRE Corporation, 7515 Colshire Dr, McLean, VA 22102, USA
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17
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Xie H, Zhuang H, Guo Y, Sharma RD, Zhang Q, Li J, Lu S, Xu L, Chan Q, Yoneda T, Spincemaille P, Zhang H, Guo H, Prince MR, Yu C, Wang Y. The appearance of magnetic susceptibility objects in SWI phase depends on object size: Comparison with QSM and CT. Clin Imaging 2021; 82:67-72. [PMID: 34798560 DOI: 10.1016/j.clinimag.2021.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/14/2021] [Accepted: 11/07/2021] [Indexed: 11/03/2022]
Abstract
PURPOSE Tissue magnetic susceptibility sign can potentially be detected on susceptibility weighted imaging (SWI) phase (SW-P). This study aims to investigate its performance for depicting brain susceptibility structures. METHODS A simulation was performed to depict magnetic susceptibility structures of various geometries on SW-P and quantitative susceptibility mapping (QSM). Brain MRI was performed on 25 subjects using SWI on a 3 T MRI system. QSM was generated from the same data. SW-P and QSM were analyzed according to radiological assessment for depicting globus pallidus nuclei, optic radiation white matter tracts, and lateral ventricular choroid plexus calcifications. In 11 of these subjects, CT was available and correlated with SW-P and QSM to assess their performance in quantifying calcifications in the choroid plexus. RESULTS In simulation, the appearance of a sphere on SW-P ranged from centric nodule to mixed positive and negative values as the diameter increased. Large cylinders also appeared as mixed positive and negative values. In comparison, QSM correctly depicted the susceptibility distribution of all magnetic structures. On human brain images, SW-P depicted the globus pallidus and optic radiation with mixed positive and negative values, consistent with simulation, and small choroid plexus calcifications as either mixed positive and negative values or as centric nodules; QSM depicted all structures as solid structures with the expected signs. For measuring calcification in the choroid plexus, QSM vs CT linear regression had a higher coefficient of determination compared to SW-P vs CT and SW-P vs QSM. CONCLUSION Appearance of susceptibility sources on SW-P changes with object size. This problem can be overcome using QSM.
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Affiliation(s)
- Hong Xie
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | - Hangwei Zhuang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Yihao Guo
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Ria D Sharma
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Qihao Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Jiahao Li
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Shimin Lu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Liang Xu
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | | | - Tetsuya Yoneda
- Department of Medical Imaging Sciences, Kumamoto University, Kumamoto, Japan
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Honglei Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Chengxin Yu
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
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18
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Kumar VJ, Scheffler K, Hagberg GE, Grodd W. Quantitative Susceptibility Mapping of the Basal Ganglia and Thalamus at 9.4 Tesla. Front Neuroanat 2021; 15:725731. [PMID: 34602986 PMCID: PMC8483181 DOI: 10.3389/fnana.2021.725731] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/23/2021] [Indexed: 12/15/2022] Open
Abstract
The thalamus (Th) and basal ganglia (BG) are central subcortical connectivity hubs of the human brain, whose functional anatomy is still under intense investigation. Nevertheless, both substructures contain a robust and reproducible functional anatomy. The quantitative susceptibility mapping (QSM) at ultra-high field may facilitate an improved characterization of the underlying functional anatomy in vivo. We acquired high-resolution QSM data at 9.4 Tesla in 21 subjects, and analyzed the thalamic and BG by using a prior defined functional parcellation. We found a more substantial contribution of paramagnetic susceptibility sources such as iron in the pallidum in contrast to the caudate, putamen, and Th in descending order. The diamagnetic susceptibility sources such as myelin and calcium revealed significant contributions in the Th parcels compared with the BG. This study presents a detailed nuclei-specific delineation of QSM-provided diamagnetic and paramagnetic susceptibility sources pronounced in the BG and the Th. We also found a reasonable interindividual variability as well as slight hemispheric differences. The results presented here contribute to the microstructural knowledge of the Th and the BG. In specific, the study illustrates QSM values (myelin, calcium, and iron) in functionally similar subregions of the Th and the BG.
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Affiliation(s)
| | - Klaus Scheffler
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Biomedical Magnetic Resonance, University Hospital and Eberhard-Karl's University, Tübingen, Germany
| | - Gisela E Hagberg
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Biomedical Magnetic Resonance, University Hospital and Eberhard-Karl's University, Tübingen, Germany
| | - Wolfgang Grodd
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
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19
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Bilgic B, Langkammer C, Marques JP, Meineke J, Milovic C, Schweser F. QSM reconstruction challenge 2.0: Design and report of results. Magn Reson Med 2021; 86:1241-1255. [PMID: 33783037 DOI: 10.1002/mrm.28754] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/25/2021] [Accepted: 02/08/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data. METHODS A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground truth data available to mimic the clinical setting. At the second stage, ground truth data were provided for parameter optimization. Submissions were evaluated using eight numerical metrics and visual ratings. RESULTS A total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and total variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art. CONCLUSION The synthetic data provide a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.
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Affiliation(s)
- Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | | | - José P Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands
| | | | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, New York, USA
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20
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Qu Z, Yang S, Xing F, Tong R, Yang C, Guo R, Huang J, Lu F, Fu C, Yan X, Hectors S, Gillen K, Wang Y, Liu C, Zhan S, Li J. Magnetic resonance quantitative susceptibility mapping in the evaluation of hepatic fibrosis in chronic liver disease: a feasibility study. Quant Imaging Med Surg 2021; 11:1170-1183. [PMID: 33816158 DOI: 10.21037/qims-20-720] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Noninvasive methods for the early diagnosis and staging of hepatic fibrosis are needed. The present study aimed to investigate the alteration of magnetic susceptibility in the liver of patients with various fibrosis stages and to evaluate the feasibility of using susceptibility to stage hepatic fibrosis. Methods A total of 30 consecutive patients with chronic liver diseases (CLDs) underwent magnetic resonance imaging (MRI) and liver biopsy evaluation of hepatic fibrosis, necroinflammatory activity, iron load, and steatosis. Quantitative susceptibility mapping (QSM), R2* and proton density fat fraction (PDFF) images were postprocessed from the same gradient-echo data for quantitative tissue characterization using region of interest (ROI) analysis. The differences for MRI measurements between cohorts of non-significant (Ishak-F <3) and significant fibrosis (Ishak-F ≥3) and the correlation of MRI measurements with fibrosis stages and necroinflammatory activity grades were tested. Receiver operating characteristic (ROC) analysis was also performed. Results There was a significant difference in liver susceptibility between the cohorts of significant and non-significant fibrosis (Z=-2.880, P=0.004). A moderate negative correlation between the stages of liver fibrosis and liver susceptibility was observed (r=-0.471, P=0.015). Liver magnetic susceptibility differentiated non-significant from significant hepatic fibrosis with an area under the receiver operating curve (AUC) of 0.836 (P=0.004). A highly sensitive diagnostic performance with an AUC of 0.933 was obtained using magnetic susceptibility and PDFF together (P<0.001). Conclusions A noninvasive liver QSM-based evaluation promises an accurate assessment of significant fibrosis in patients with CLDs.
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Affiliation(s)
- Zheng Qu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Shuohui Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Feng Xing
- Department of Liver Diseases, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Chenyao Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rongfang Guo
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiling Huang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Caixia Fu
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Xu Yan
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Stefanie Hectors
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Kelly Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Chenghai Liu
- Department of Liver Diseases, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Shanghai Key Laboratory of Traditional Chinese Clinical Medicine, Shanghai, China
| | - Songhua Zhan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
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21
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Voelker MN, Kraff O, Goerke S, Laun FB, Hanspach J, Pine KJ, Ehses P, Zaiss M, Liebert A, Straub S, Eckstein K, Robinson S, Nagel AN, Stefanescu MR, Wollrab A, Klix S, Felder J, Hock M, Bosch D, Weiskopf N, Speck O, Ladd ME, Quick HH. The traveling heads 2.0: Multicenter reproducibility of quantitative imaging methods at 7 Tesla. Neuroimage 2021; 232:117910. [PMID: 33647497 DOI: 10.1016/j.neuroimage.2021.117910] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/25/2021] [Accepted: 02/20/2021] [Indexed: 12/14/2022] Open
Abstract
OBJECT This study evaluates inter-site and intra-site reproducibility at ten different 7 T sites for quantitative brain imaging. MATERIAL AND METHODS Two subjects - termed the "traveling heads" - were imaged at ten different 7 T sites with a harmonized quantitative brain MR imaging protocol. In conjunction with the system calibration, MP2RAGE, QSM, CEST and multi-parametric mapping/relaxometry were examined. RESULTS Quantitative measurements with MP2RAGE showed very high reproducibility across sites and subjects, and errors were in concordance with previous results and other field strengths. QSM had high inter-site reproducibility for relevant subcortical volumes. CEST imaging revealed systematic differences between the sites, but reproducibility was comparable to results in the literature. Relaxometry had also very high agreement between sites, but due to the high sensitivity, differences caused by different applications of the B1 calibration of the two RF coil types used were observed. CONCLUSION Our results show that quantitative brain imaging can be performed with high reproducibility at 7 T and with similar reliability as found at 3 T for multicenter studies of the supratentorial brain.
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Affiliation(s)
- Maximilian N Voelker
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany; High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
| | - Oliver Kraff
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| | - Steffen Goerke
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jannis Hanspach
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Philipp Ehses
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Moritz Zaiss
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Andrzej Liebert
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sina Straub
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Korbinian Eckstein
- High Field MR Center, Department for Biomedical Imaging and Image guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Simon Robinson
- High Field MR Center, Department for Biomedical Imaging and Image guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Armin N Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Maria R Stefanescu
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Astrid Wollrab
- Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Sabrina Klix
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrueck-Center for Molecular Medicine, Berlin-Buch, Germany
| | - Jörg Felder
- Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich, Jülich, Germany
| | - Michael Hock
- Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Dario Bosch
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Oliver Speck
- Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Mark E Ladd
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany; Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Harald H Quick
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany; High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany
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22
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Li Y, Sethi SK, Zhang C, Miao Y, Yerramsetty KK, Palutla VK, Gharabaghi S, Wang C, He N, Cheng J, Yan F, Haacke EM. Iron Content in Deep Gray Matter as a Function of Age Using Quantitative Susceptibility Mapping: A Multicenter Study. Front Neurosci 2021; 14:607705. [PMID: 33488350 PMCID: PMC7815653 DOI: 10.3389/fnins.2020.607705] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/07/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To evaluate the effect of resolution on iron content using quantitative susceptibility mapping (QSM); to verify the consistency of QSM across field strengths and manufacturers in evaluating the iron content of deep gray matter (DGM) of the human brain using subjects from multiple sites; and to establish a susceptibility baseline as a function of age for each DGM structure using both a global and regional iron analysis. METHODS Data from 623 healthy adults, ranging from 20 to 90 years old, were collected across 3 sites using gradient echo imaging on one 1.5 Tesla and two 3.0 Tesla MR scanners. Eight subcortical gray matter nuclei were semi-automatically segmented using a full-width half maximum threshold-based analysis of the QSM data. Mean susceptibility, volume and total iron content with age correlations were evaluated for each measured structure for both the whole-region and RII (high iron content regions) analysis. For the purpose of studying the effect of resolution on QSM, a digitized model of the brain was applied. RESULTS The mean susceptibilities of the caudate nucleus (CN), globus pallidus (GP) and putamen (PUT) were not significantly affected by changing the slice thickness from 0.5 to 3 mm. But for small structures, the susceptibility was reduced by 10% for 2 mm thick slices. For global analysis, the mean susceptibility correlated positively with age for the CN, PUT, red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN). There was a negative correlation with age in the thalamus (THA). The volumes of most nuclei were negatively correlated with age. Apart from the GP, THA, and pulvinar thalamus (PT), all the other structures showed an increasing total iron content despite the reductions in volume with age. For the RII regional high iron content analysis, mean susceptibility in most of the structures was moderately to strongly correlated with age. Similar to the global analysis, apart from the GP, THA, and PT, all structures showed an increasing total iron content. CONCLUSION A reasonable estimate for age-related iron behavior can be obtained from a large cross site, cross manufacturer set of data when high enough resolutions are used. These estimates can be used for correcting for age related iron changes when studying diseases like Parkinson's disease, Alzheimer's disease, and other iron related neurodegenerative diseases.
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Affiliation(s)
- Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sean K. Sethi
- Department of Radiology, Wayne State University, Detroit, MI, United States
- MR Innovations, Inc., Bingham Farms, MI, United States
- SpinTech, Inc., Bingham Farms, MI, United States
| | - Chunyan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanwei Miao
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | | | | | | | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ewart Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Radiology, Wayne State University, Detroit, MI, United States
- MR Innovations, Inc., Bingham Farms, MI, United States
- SpinTech, Inc., Bingham Farms, MI, United States
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23
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Karsa A, Punwani S, Shmueli K. An optimized and highly repeatable MRI acquisition and processing pipeline for quantitative susceptibility mapping in the head-and-neck region. Magn Reson Med 2020; 84:3206-3222. [PMID: 32621302 DOI: 10.1002/mrm.28377] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/06/2020] [Accepted: 05/23/2020] [Indexed: 02/11/2024]
Abstract
PURPOSE Quantitative Susceptibility Mapping (QSM) is an emerging technique sensitive to disease-related changes including oxygenation. It is extensively used in brain studies and has increasing clinical applications outside the brain. Here we present the first MRI acquisition protocol and QSM pipeline optimized for the head-and-neck region together with a repeatability analysis performed in healthy volunteers. METHODS We investigated both the intrasession and the intersession repeatability of the optimized method in 10 subjects. We also implemented two, Tikhonov-regularisation-based susceptibility calculation techniques that were found to have higher contrast-to-noise than existing methods in the head-and-neck region. Repeatability was evaluated by calculating the distributions of susceptibility differences between repeated scans and the corresponding minimum detectable effect sizes (MDEs). RESULTS Deep brain regions had higher QSM repeatability than neck regions. As expected, intrasession repeatability was generally better than intersession repeatability. Susceptibility maps calculated using projection onto dipole fields for background field removal were more repeatable than using the Laplacian boundary value method in the head-and-neck region. Small (short-axis diameter <5 mm) lymph nodes had the lowest repeatability (MDE = 0.27 ppm) as imperfect segmentation included some of the surrounding paramagnetic fatty fascia, highlighting the importance of accurate region delineation. MDEs in the larger lymph nodes (0.16 ppm), submandibular glands (0.10 ppm), and especially the parotid glands (0.06 ppm) were much lower, comparable to those of the brain regions. CONCLUSIONS The high repeatability of the acquisition and pipeline optimized for QSM will facilitate clinical studies in the head-and-neck region.
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Affiliation(s)
- Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Centre for Medical Imaging, University College London, London, United Kingdom
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24
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Iv M, Ng NN, Nair S, Zhang Y, Lavezo J, Cheshier SH, Holdsworth SJ, Moseley ME, Rosenberg J, Grant GA, Yeom KW. Brain Iron Assessment after Ferumoxytol-enhanced MRI in Children and Young Adults with Arteriovenous Malformations: A Case-Control Study. Radiology 2020; 297:438-446. [PMID: 32930651 DOI: 10.1148/radiol.2020200378] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Iron oxide nanoparticles are an alternative contrast agent for MRI. Gadolinium deposition has raised safety concerns, but it is unknown whether ferumoxytol administration also deposits in the brain. Purpose To investigate whether there are signal intensity changes in the brain at multiecho gradient imaging following ferumoxytol exposure in children and young adults. Materials and Methods This retrospective case-control study included children and young adults, matched for age and sex, with brain arteriovenous malformations who received at least one dose of ferumoxytol from January 2014 to January 2018. In participants who underwent at least two brain MRI examinations (subgroup), the first and last available examinations were analyzed. Regions of interests were placed around deep gray structures on quantitative susceptibility mapping and R2* images. Mean susceptibility and R2* values of regions of interests were recorded. Measurements were assessed by linear regression analyses: a between-group comparison of ferumoxytol-exposed and unexposed participants and a within-group (subgroup) comparison before and after exposure. Results Seventeen participants (mean age ± standard deviation, 13 years ± 5; nine male) were in the ferumoxytol-exposed (case) group, 21 (mean age, 14 years ± 5; 11 male) were in the control group, and nine (mean age, 12 years ± 6; four male) were in the subgroup. The mean number of ferumoxytol administrations was 2 ± 1 (range, one to four). Mean susceptibility (in parts per million [ppm]) and R2* (in inverse seconds [sec-1]) values of the dentate (case participants: 0.06 ppm ± 0.04 and 23.87 sec-1 ± 4.13; control participants: 0.02 ppm ± 0.03 and 21.7 sec-1 ± 5.26), substantia nigrae (case participants: 0.08 ppm ± 0.06 and 27.46 sec-1 ± 5.58; control participants: 0.04 ppm ± 0.05 and 24.96 sec-1 ± 5.3), globus pallidi (case participants: 0.14 ppm ± 0.05 and 30.75 sec-1 ± 5.14; control participants: 0.08 ppm ± 0.07 and 28.82 sec-1 ± 6.62), putamina (case participants: 0.03 ppm ± 0.02 and 20.63 sec-1 ± 2.44; control participants: 0.02 ppm ± 0.02 and 19.65 sec-1 ± 3.6), caudate (case participants: -0.1 ppm ± 0.04 and 18.21 sec-1 ± 3.1; control participants: -0.06 ppm ± 0.05 and 18.83 sec-1 ± 3.32), and thalami (case participants: 0 ppm ± 0.03 and 16.49 sec-1 ± 3.6; control participants: 0.02 ppm ± 0.02 and 18.38 sec-1 ± 2.09) did not differ between groups (susceptibility, P = .21; R2*, P = .24). For the subgroup, the mean interval between the first and last ferumoxytol administration was 14 months ± 8 (range, 1-25 months). Mean susceptibility and R2* values of the dentate (first MRI: 0.06 ppm ± 0.05 and 25.78 sec-1 ± 5.9; last MRI: 0.06 ppm ± 0.02 and 25.55 sec-1 ± 4.71), substantia nigrae (first MRI: 0.06 ppm ± 0.06 and 28.26 sec-1 ± 9.56; last MRI: 0.07 ppm ± 0.06 and 25.65 sec-1 ± 6.37), globus pallidi (first MRI: 0.13 ppm ± 0.07 and 27.53 sec-1 ± 8.88; last MRI: 0.14 ppm ± 0.06 and 29.78 sec-1 ± 6.54), putamina (first MRI: 0.03 ppm ± 0.03 and 19.78 sec-1 ± 3.51; last MRI: 0.03 ppm ± 0.02 and 19.73 sec-1 ± 3.01), caudate (first MRI: -0.09 ppm ± 0.05 and 21.38 sec-1 ± 4.72; last MRI: -0.1 ppm ± 0.05 and 18.75 sec-1 ± 2.68), and thalami (first MRI: 0.01 ppm ± 0.02 and 17.65 sec-1 ± 5.16; last MRI: 0 ppm ± 0.02 and 15.32 sec-1 ± 2.49) did not differ between the first and last MRI examinations (susceptibility, P = .95; R2*, P = .54). Conclusion No overall significant differences were found in susceptibility and R2* values of deep gray structures to suggest retained iron in the brain between ferumoxytol-exposed and unexposed children and young adults with arteriovenous malformations and in those exposed to ferumoxytol over time. © RSNA, 2020.
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Affiliation(s)
- Michael Iv
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Nathan N Ng
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Sid Nair
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Yi Zhang
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Jonathan Lavezo
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Samuel H Cheshier
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Samantha J Holdsworth
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Michael E Moseley
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Jarrett Rosenberg
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Gerald A Grant
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
| | - Kristen W Yeom
- From the Department of Radiology, Division of Neuroimaging and Neurointervention (M.I.), Department of Pathology (J.L.), Department of Radiology, Lucas Center (S.J.H., M.E.M., J.R.), and Department of Neurosurgery, Division of Pediatric Neurosurgery (G.A.G.), Stanford University, Stanford, Calif; Department of Radiology, Pediatric MRI and CT, Division of Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University, 725 Welch Rd, Room G516, Palo Alto, CA 94304 (M.I., N.N.N., S.N., Y.Z., K.W.Y.); and Department of Neurosurgery, Division of Pediatric Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT (S.H.C.). From the 2018 RSNA Annual Meeting
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Multi-centre, multi-vendor reproducibility of 7T QSM and R 2* in the human brain: Results from the UK7T study. Neuroimage 2020; 223:117358. [PMID: 32916289 PMCID: PMC7480266 DOI: 10.1016/j.neuroimage.2020.117358] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 09/03/2020] [Accepted: 09/03/2020] [Indexed: 12/13/2022] Open
Abstract
Introduction We present the reliability of ultra-high field T2* MRI at 7T, as part of the UK7T Network's “Travelling Heads” study. T2*-weighted MRI images can be processed to produce quantitative susceptibility maps (QSM) and R2* maps. These reflect iron and myelin concentrations, which are altered in many pathophysiological processes. The relaxation parameters of human brain tissue are such that R2* mapping and QSM show particularly strong gains in contrast-to-noise ratio at ultra-high field (7T) vs clinical field strengths (1.5–3T). We aimed to determine the inter-subject and inter-site reproducibility of QSM and R2* mapping at 7T, in readiness for future multi-site clinical studies. Methods Ten healthy volunteers were scanned with harmonised single- and multi-echo T2*-weighted gradient echo pulse sequences. Participants were scanned five times at each “home” site and once at each of four other sites. The five sites had 1× Philips, 2× Siemens Magnetom, and 2× Siemens Terra scanners. QSM and R2* maps were computed with the Multi-Scale Dipole Inversion (MSDI) algorithm (https://github.com/fil-physics/Publication-Code). Results were assessed in relevant subcortical and cortical regions of interest (ROIs) defined manually or by the MNI152 standard space. Results and Discussion Mean susceptibility (χ) and R2* values agreed broadly with literature values in all ROIs. The inter-site within-subject standard deviation was 0.001–0.005 ppm (χ) and 0.0005–0.001 ms−1 (R2*). For χ this is 2.1–4.8 fold better than 3T reports, and 1.1–3.4 fold better for R2*. The median ICC from within- and cross-site R2* data was 0.98 and 0.91, respectively. Multi-echo QSM had greater variability vs single-echo QSM especially in areas with large B0 inhomogeneity such as the inferior frontal cortex. Across sites, R2* values were more consistent than QSM in subcortical structures due to differences in B0-shimming. On a between-subject level, our measured χ and R2* cross-site variance is comparable to within-site variance in the literature, suggesting that it is reasonable to pool data across sites using our harmonised protocol. Conclusion The harmonized UK7T protocol and pipeline delivers on average a 3-fold improvement in the coefficient of reproducibility for QSM and R2* at 7T compared to previous reports of multi-site reproducibility at 3T. These protocols are ready for use in multi-site clinical studies at 7T.
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Ryman SG, Poston KL. MRI biomarkers of motor and non-motor symptoms in Parkinson's disease. Parkinsonism Relat Disord 2020; 73:85-93. [PMID: 31629653 PMCID: PMC7145760 DOI: 10.1016/j.parkreldis.2019.10.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/03/2019] [Accepted: 10/05/2019] [Indexed: 12/19/2022]
Abstract
Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.
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Affiliation(s)
- Sephira G Ryman
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, 300 Pasteur Dr. Room A343. MC-5235, Stanford, CA, 94305, USA.
| | - Kathleen L Poston
- Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, 300 Pasteur Dr. Room A343. MC-5235, Stanford, CA, 94305, USA.
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Wang C, Zhang Y, Du J, Huszár IN, Liu S, Chen Y, Buch S, Wu F, Liu Y, Jenkinson M, Hsu CC, Fan Z, Haacke EM, Yang Q. Quantitative Susceptibility Mapping for Characterization of Intraplaque Hemorrhage and Calcification in Carotid Atherosclerotic Disease. J Magn Reson Imaging 2020; 52:534-541. [DOI: 10.1002/jmri.27064] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 01/08/2020] [Accepted: 01/09/2020] [Indexed: 12/26/2022] Open
Affiliation(s)
- Chaoyue Wang
- Department of Radiology, Xuanwu HospitalCapital Medical University Beijing China
- Nuffield Department of Clinical NeurosciencesUniversity of Oxford Oxford UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of Oxford Oxford UK
| | - Yue Zhang
- Department of Radiology, Xuanwu HospitalCapital Medical University Beijing China
| | - Jingwen Du
- Department of Radiology, Xuanwu HospitalCapital Medical University Beijing China
| | - István N. Huszár
- Nuffield Department of Clinical NeurosciencesUniversity of Oxford Oxford UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of Oxford Oxford UK
| | - Saifeng Liu
- Magnetic Resonance Imaging Institute for Biomedical Research Bingham Farms Michigan USA
| | - Yongsheng Chen
- Magnetic Resonance Imaging Institute for Biomedical Research Bingham Farms Michigan USA
- Department of RadiologyWayne State University Detroit Michigan USA
| | - Sagar Buch
- Center for Functional and Metabolic Mapping, Robarts' Research InstituteWestern University London Ontario Canada
| | - Fang Wu
- Department of Radiology, Xuanwu HospitalCapital Medical University Beijing China
| | - Yuehong Liu
- Department of Radiology, Xuanwu HospitalCapital Medical University Beijing China
| | - Mark Jenkinson
- Nuffield Department of Clinical NeurosciencesUniversity of Oxford Oxford UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of Oxford Oxford UK
| | | | - Zhaoyang Fan
- Biomedical Imaging Research InstituteCedars Sinai Medical Center Los Angeles California USA
| | - E. Mark Haacke
- Magnetic Resonance Imaging Institute for Biomedical Research Bingham Farms Michigan USA
- Department of RadiologyWayne State University Detroit Michigan USA
| | - Qi Yang
- Department of Radiology, Xuanwu HospitalCapital Medical University Beijing China
- Biomedical Imaging Research InstituteCedars Sinai Medical Center Los Angeles California USA
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Spincemaille P, Anderson J, Wu G, Yang B, Fung M, Li K, Li S, Kovanlikaya I, Gupta A, Kelley D, Benhamo N, Wang Y. Quantitative Susceptibility Mapping: MRI at 7T versus 3T. J Neuroimaging 2020; 30:65-75. [PMID: 31625646 PMCID: PMC6954973 DOI: 10.1111/jon.12669] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/02/2019] [Accepted: 10/02/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND AND PURPOSE Ultrahigh-field 7T promises more than doubling the signal-to-noise ratio (SNR) of 3T for magnetic resonance imaging (MRI), particularly for MRI of magnetic susceptibility effects induced by B0 . Quantitative susceptibility mapping (QSM) is based on deconvolving the induced phase (or field) and would therefore benefit substantially from 7T. The purpose of this work was to compare QSM performance at 7T versus 3T in an intrascanner test-retest experiment with varying echo numbers (5 and 10 echoes). METHODS A prospective study in N = 10 healthy subjects was carried out at both 3T and 7T field strengths. Gradient echo data using 5 and 10 echoes were acquired twice in each subject. Test-retest reproducibility was assessed using Bland-Altman and regression analysis of region of interest measurements. Image quality was scored by an experienced neuroradiologist. RESULTS Intrascanner bias was below 3.6 parts-per-billion (ppb) with correlation R2 > .85. Interscanner bias was below 10.9 ppb with correlation R2 > .8. The image quality score for the 3T 10 echo protocol was not different from the 7T 5 echo protocol (P = .65). CONCLUSION Excellent image quality and good reproducibility was observed. 7T allows equivalent image quality of 3T in half of the scan time.
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Affiliation(s)
- Pascal Spincemaille
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
- Corresponding author: Pascal Spincemaille, Ph.D.,
Department of Radiology, 515 East 71st St, Suite S101, New York, NY, 10021,
, tel: +1 646 962 2630
| | | | - Gaohong Wu
- General Electrical Healthcare, Waukesha, WI
| | | | | | - Ke Li
- General Electrical Healthcare, Waukesha, WI
| | - Shaojun Li
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
| | - Ilhami Kovanlikaya
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
| | - Ajay Gupta
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
| | | | | | - Yi Wang
- Radiology, Weill Cornell Medical College, Cornell
University, New York, NY
- Department of Biomedical Engineering, Cornell University,
Ithaca, NY
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Multi-site harmonization of 7 tesla MRI neuroimaging protocols. Neuroimage 2019; 206:116335. [PMID: 31712167 PMCID: PMC7212005 DOI: 10.1016/j.neuroimage.2019.116335] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 10/25/2019] [Accepted: 11/04/2019] [Indexed: 12/11/2022] Open
Abstract
Increasing numbers of 7 T (7 T) magnetic resonance imaging (MRI) scanners are in research and clinical use. 7 T MRI can increase the scanning speed, spatial resolution and contrast-to-noise-ratio of many neuroimaging protocols, but technical challenges in implementation have been addressed in a variety of ways across sites. In order to facilitate multi-centre studies and ensure consistency of findings across sites, it is desirable that 7 T MRI sites implement common high-quality neuroimaging protocols that can accommodate different scanner models and software versions. With the installation of several new 7 T MRI scanners in the United Kingdom, the UK7T Network was established with an aim to create a set of harmonized structural and functional neuroimaging sequences and protocols. The Network currently includes five sites, which use three different scanner platforms, provided by two different vendors. Here we describe the harmonization of functional and anatomical imaging protocols across the three different scanner models, detailing the necessary changes to pulse sequences and reconstruction methods. The harmonized sequences are fully described, along with implementation details. Example datasets acquired from the same subject on all Network scanners are made available. Based on these data, an evaluation of the harmonization is provided. In addition, the implementation and validation of a common system calibration process is described.
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Vaillancourt DE, Lehericy S. Illuminating basal ganglia and beyond in Parkinson's disease. Mov Disord 2019; 33:1373-1375. [PMID: 30311976 DOI: 10.1002/mds.27483] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 08/08/2018] [Indexed: 12/14/2022] Open
Affiliation(s)
- David E Vaillancourt
- Department of Applied Physiology and Kinesiology, Biomedical Engineering, Neurology, University of Florida, Gainesville, Florida, USA
| | - Stéphane Lehericy
- Institut du Cerveau et de la Moelle - ICM, Centre de NeuroImagerie de Recherche - CENIR, Sorbonne Universités, UPMC Univ Paris 06, Paris, France
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Spincemaille P, Liu Z, Zhang S, Kovanlikaya I, Ippoliti M, Makowski M, Watts R, de Rochefort L, Venkatraman V, Desmond P, Santin MD, Lehéricy S, Kopell BH, Péran P, Wang Y. Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi-Site Reproducibility and Single-Site Robustness. J Neuroimaging 2019; 29:689-698. [PMID: 31379055 DOI: 10.1111/jon.12658] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/11/2019] [Accepted: 07/21/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND AND PURPOSE Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment. METHODS A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm3 reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients. CONCLUSION Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.
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Affiliation(s)
- Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Zhe Liu
- Department of Radiology, Weill Medical College of Cornell University, New York, NY.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
| | - Shun Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY.,Department of Radiology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Matteo Ippoliti
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Marcus Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Richard Watts
- Department of Psychology, Yale University, New Haven, CT
| | | | - Vijay Venkatraman
- Department of Medicine and Radiology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Patricia Desmond
- Department of Medicine and Radiology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Mathieu D Santin
- Inserm U 1127, CNRS UMR 7225, Centre for NeuroImaging Research, ICM (Brain & Spine Institute), Sorbonne University, Paris, France
| | - Stéphane Lehéricy
- Inserm U 1127, CNRS UMR 7225, Centre for NeuroImaging Research, ICM (Brain & Spine Institute), Sorbonne University, Paris, France.,Neuroradiology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Brian H Kopell
- Division of Movement Disorders, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patrice Péran
- Toulouse NeuroImaging Center, Université de Toulouse Inserm, Toulouse, France
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
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Hoch MJ, Bruno MT, Faustin A, Cruz N, Mogilner AY, Crandall L, Wisniewski T, Devinsky O, Shepherd TM. 3T MRI Whole-Brain Microscopy Discrimination of Subcortical Anatomy, Part 2: Basal Forebrain. AJNR Am J Neuroradiol 2019; 40:1095-1105. [PMID: 31196861 DOI: 10.3174/ajnr.a6088] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 04/22/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE The basal forebrain contains multiple structures of great interest to emerging functional neurosurgery applications, yet many neuroradiologists are unfamiliar with this neuroanatomy because it is not resolved with current clinical MR imaging. MATERIALS AND METHODS We applied an optimized TSE T2 sequence to washed whole postmortem brain samples (n = 13) to demonstrate and characterize the detailed anatomy of the basal forebrain using a clinical 3T MR imaging scanner. We measured the size of selected internal myelinated pathways and measured subthalamic nucleus size, oblique orientation, and position relative to the intercommissural point. RESULTS We identified most basal ganglia and diencephalon structures using serial axial, coronal, and sagittal planes relative to the intercommissural plane. Specific oblique image orientations demonstrated the positions and anatomic relationships for selected structures of interest to functional neurosurgery. We observed only 0.2- to 0.3-mm right-left differences in the anteroposterior and superoinferior length of the subthalamic nucleus (P = .084 and .047, respectively). Individual variability for the subthalamic nucleus was greatest for angulation within the sagittal plane (range, 15°-37°), transverse dimension (range, 2-6.7 mm), and most inferior border (range, 4-7 mm below the intercommissural plane). CONCLUSIONS Direct identification of basal forebrain structures in multiple planes using the TSE T2 sequence makes this challenging neuroanatomy more accessible to practicing neuroradiologists. This protocol can be used to better define individual variations relevant to functional neurosurgical targeting and validate/complement advanced MR imaging methods being developed for direct visualization of these structures in living patients.
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Affiliation(s)
- M J Hoch
- From the Department of Radiology and Imaging Sciences, (M.J.H.), Emory University, Atlanta, Georgia
| | - M T Bruno
- Departments of Radiology (M.T.B., N.C., T.M.S.)
| | | | - N Cruz
- Departments of Radiology (M.T.B., N.C., T.M.S.)
| | | | - L Crandall
- Neurology (L.C., T.W., O.D.).,SUDC Foundation (L.C., O.D.), New York, New York
| | - T Wisniewski
- Pathology (A.F., T.W.).,Neurology (L.C., T.W., O.D.).,Psychiatry (T.W.), New York University, New York, New York
| | - O Devinsky
- Neurology (L.C., T.W., O.D.).,SUDC Foundation (L.C., O.D.), New York, New York
| | - T M Shepherd
- Departments of Radiology (M.T.B., N.C., T.M.S.) .,Center for Advanced Imaging Innovation and Research (T.M.S.), New York, New York
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Choi JY, Lee J, Nam Y, Lee J, Oh SH. Improvement of reproducibility in quantitative susceptibility mapping (QSM) and transverse relaxation rates ( R 2 * ) after physiological noise correction. J Magn Reson Imaging 2019; 49:1769-1776. [PMID: 31062456 DOI: 10.1002/jmri.26522] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 09/06/2018] [Accepted: 09/07/2018] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Numerous studies have suggested that quantitative susceptibility mapping (QSM) and transverse relaxation rates ( R 2 * ) are useful to monitor neurological diseases. For clinical use of QSM and R 2 * , reproducibility is an important feature. However, respiration-induced local magnetic field variation makes artifacts in gradient echo-based images and reduces the reproducibility of QSM and R 2 * . PURPOSE To investigate the improvement of reproducibility of QSM and R 2 * after the correction of respiration-induced field variation, and to assess the effect of varying types of the region of interest (ROI) analysis on reproducibility. STUDY TYPE Reproducibility study. POPULATION Ten controls. FIELD STRENGTH/SEQUENCE 3T/multiecho gradient echo sequence. ASSESSMENT Intrascan reproducibility of QSM and R 2 * was investigated in ROIs before and after the respiration correction. STATISTICAL TESTS Reproducibility was obtained by the square of voxel-wise correlation coefficients between scans. A paired t-test was performed for comparison between before and after the respiration correction and between QSM and R 2 * . RESULTS Based on the ROI analysis, reproducibility increased after the respiration correction. Reproducibility in the white matter (11.89% increased in QSM and 23.38% in R 2 * , P = 0.009 and 0.024, respectively) and deep gray matter (5.50% increased in QSM and 13.96% in R 2 * , P = 0.024 and 0.019, respectively) increased significantly after the respiration correction. Reproducibility of R 2 * was higher than that of QSM in the whole brain and cortical gray matter, while QSM maps showed higher reproducibility than R 2 * in the white matter and deep gray matter. DATA CONCLUSION Respiration-induced error correction significantly improved reproducibility in QSM and R 2 * mapping. QSM and R 2 * mapping showed a different level of reproducibility depending on the types of ROI analysis. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Joon Yul Choi
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jingu Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Yoonho Nam
- Department of Radiology, Seoul Saint Mary's Hospital, College of Medicine, Catholic University of Korea, Seoul, Republic of Korea
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Se-Hong Oh
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea
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Lancione M, Donatelli G, Cecchi P, Cosottini M, Tosetti M, Costagli M. Echo-time dependency of quantitative susceptibility mapping reproducibility at different magnetic field strengths. Neuroimage 2019; 197:557-564. [PMID: 31075389 DOI: 10.1016/j.neuroimage.2019.05.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 04/10/2019] [Accepted: 05/02/2019] [Indexed: 12/13/2022] Open
Abstract
Quantitative Susceptibility Mapping (QSM) provides a way of measuring iron concentration and myelination non-invasively and has the potential of becoming a tool of paramount importance in the study of a host of different pathologies. However, several experimental factors and the physical properties of magnetic susceptibility (χ) can impair the reliability of QSM, and it is therefore essential to assess QSM reproducibility for repeated acquisitions and different field strength. In particular, it has recently been demonstrated that QSM measurements strongly depend on echo time (TE): the same tissue, measured on the same scanner, exhibits different apparent frequency shifts depending on the TE used. This study aims to assess the influence of TE on intra-scanner and inter-scanner reproducibility of QSM, by using MRI systems operating at 3T and 7T. To maximize intra-scanner reproducibility it is necessary to match the TEs of the acquisition protocol, but the application of this rule leads to inconsistent QSM values across scanners operating at different static magnetic field. This study however demonstrates that, provided a careful choice of acquisition parameters, and in particular by using TEs at 3T that are approximately 2.6 times longer than those at 7T, highly reproducible whole-brain χ maps can be achieved also across different scanners, which renders QSM a suitable technique for longitudinal follow-up in clinical settings and in multi-center studies.
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Affiliation(s)
| | - Graziella Donatelli
- IMAGO7 Foundation, Pisa, Italy; Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Paolo Cecchi
- Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | | | - Michela Tosetti
- IMAGO7 Foundation, Pisa, Italy; IRCCS Stella Maris, Pisa, Italy.
| | - Mauro Costagli
- IMAGO7 Foundation, Pisa, Italy; IRCCS Stella Maris, Pisa, Italy
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35
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Zhang S, Liu Z, Nguyen TD, Yao Y, Gillen KM, Spincemaille P, Kovanlikaya I, Gupta A, Wang Y. Clinical feasibility of brain quantitative susceptibility mapping. Magn Reson Imaging 2019; 60:44-51. [PMID: 30954651 DOI: 10.1016/j.mri.2019.04.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 03/31/2019] [Accepted: 04/02/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To evaluate the quality of brain quantitative susceptibility mapping (QSM) that is fully automatically reconstructed in clinical MRI of various neurological diseases. METHODS 393 consecutive patients in one month were recruited for this evaluation study. QSM was reconstructed using Morphology Enabled Dipole Inversion without zero reference regularization (MEDI) and using MEDI with cerebrospinal fluid automatic zero-reference regularization to generate susceptibility values (MEDI+0). Two neuroradiologists independently assessed the image quality of MEDI+0 and MEDI and image concordance between them. Lesion susceptibility values were measured in 20 cases of glioma, 21 cases of ischemic stroke and 43 multiple sclerosis (MS) cases on both MEDI+0 and MEDI images. RESULTS The two neuroradiologists rated the MEDI+0 image qualities of the 393 cases as 351 (89.3%) and 362 (92.1%) excellent, 29 (7.4%) and 24 (6.1%) diagnostic, and 13 (3.3%) and 7 (1.8%) poor, and scored the concordances between MEDI+0 and MEDI as 364 (92.6%) and 351 (89.3%) excellent, 13 (3.3%) and 31 (7.9%) good, 14 (3.6%) and 9 (2.3%) intermediate, 2 (0.5%) and 2 (0.5%) poor, and 0 (0%) and 0 (0%) none. There was good correlation between MEDI+0 and MEDI in lesion susceptibility contrast of glioma, ischemic stroke, and MS cases (all p < 0.05). The MS lesion susceptibility time course from this patient cohort was found to be similar to the reported pattern: isointense initially for acute enhancing lesions, and hyperintense over the following years for active chronic lesions. CONCLUSION Brain QSM images of various neurological diseases have reliable diagnostic quality in clinical MRI, with MEDI+0 providing susceptibility values automatically referenced to CSF in longitudinal and cross-center studies.
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Affiliation(s)
- Shun Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhe Liu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Yihao Yao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
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Lin F, Prince MR, Spincemaille P, Wang Y. Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism. Recent Pat Biotechnol 2018; 13:90-113. [PMID: 30556508 DOI: 10.2174/1872208313666181217112745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 12/04/2018] [Accepted: 12/11/2018] [Indexed: 01/06/2023]
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice. OBJECTIVE To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community. METHOD We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility. RESULTS We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase. CONCLUSION Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.
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Affiliation(s)
- Feng Lin
- School of Law, City University of Hong Kong, Hong Kong, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States
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37
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Acosta-Cabronero J, Milovic C, Mattern H, Tejos C, Speck O, Callaghan MF. A robust multi-scale approach to quantitative susceptibility mapping. Neuroimage 2018; 183:7-24. [PMID: 30075277 PMCID: PMC6215336 DOI: 10.1016/j.neuroimage.2018.07.065] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 06/29/2018] [Accepted: 07/29/2018] [Indexed: 12/11/2022] Open
Abstract
Quantitative Susceptibility Mapping (QSM), best known as a surrogate for tissue iron content, is becoming a highly relevant MRI contrast for monitoring cellular and vascular status in aging, addiction, traumatic brain injury and, in general, a wide range of neurological disorders. In this study we present a new Bayesian QSM algorithm, named Multi-Scale Dipole Inversion (MSDI), which builds on the nonlinear Morphology-Enabled Dipole Inversion (nMEDI) framework, incorporating three additional features: (i) improved implementation of Laplace's equation to reduce the influence of background fields through variable harmonic filtering and subsequent deconvolution, (ii) improved error control through dynamic phase-reliability compensation across spatial scales, and (iii) scalewise use of the morphological prior. More generally, this new pre-conditioned QSM formalism aims to reduce the impact of dipole-incompatible fields and measurement errors such as flow effects, poor signal-to-noise ratio or other data inconsistencies that can lead to streaking and shadowing artefacts. In terms of performance, MSDI is the first algorithm to rank in the top-10 for all metrics evaluated in the 2016 QSM Reconstruction Challenge. It also demonstrated lower variance than nMEDI and more stable behaviour in scan-rescan reproducibility experiments for different MRI acquisitions at 3 and 7 Tesla. In the present work, we also explored new forms of susceptibility MRI contrast making explicit use of the differential information across spatial scales. Specifically, we show MSDI-derived examples of: (i) enhanced anatomical detail with susceptibility inversions from short-range dipole fields (hereby referred to as High-Pass Susceptibility Mapping or HPSM), (ii) high specificity to venous-blood susceptibilities for highly regularised HPSM (making a case for MSDI-based Venography or VenoMSDI), (iii) improved tissue specificity (and possibly statistical conditioning) for Macroscopic-Vessel Suppressed Susceptibility Mapping (MVSSM), and (iv) high spatial specificity and definition for HPSM-based Susceptibility-Weighted Imaging (HPSM-SWI) and related intensity projections.
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Affiliation(s)
- Julio Acosta-Cabronero
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
| | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Hendrik Mattern
- Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto von Guericke University, Magdeburg, Germany
| | - Cristian Tejos
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Oliver Speck
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto von Guericke University, Magdeburg, Germany; Center for Behavioural Brain Sciences, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
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Deh K, Kawaji K, Bulk M, Van Der Weerd L, Lind E, Spincemaille P, McCabe Gillen K, Van Auderkerke J, Wang Y, Nguyen TD. Multicenter reproducibility of quantitative susceptibility mapping in a gadolinium phantom using MEDI+0 automatic zero referencing. Magn Reson Med 2018; 81:1229-1236. [PMID: 30284727 DOI: 10.1002/mrm.27410] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 05/05/2018] [Accepted: 05/29/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To determine the reproducibility of quantitative susceptibility mapping at multiple sites on clinical and preclinical scanners (1.5 T, 3 T, 7 T, and 9.4 T) from different vendors (Siemens, GE, Philips, and Bruker) for standardization of multicenter studies. METHODS Seven phantoms distributed from the core site, each containing 5 compartments with gadolinium solutions with fixed concentrations between 0.625 mM and 10 mM. Multi-echo gradient echo scans were performed at 1.5 T, 3 T, 7 T, and 9.4 T on 12 clinical and 3 preclinical scanners. DICOM images from the scans were processed into quantitative susceptibility maps using the Laplacian boundary value (LBV) and MEDI+0 automatic uniform reference algorithm. Region of interest (ROI) analyses were performed by a physicist to determine agreement between results from all sites. Measurement reproducibility was assessed using regression, Bland-Altman plots, and the intra-class correlation coefficient (ICC). RESULTS Quantitative susceptibility mapping (QSM) from all scanners had similar, artifact-free visual appearance. Regression analysis showed a linear relationship between gadolinium concentrations and average QSM measurements for all phantoms (y = 350x - 0.0346, r2 >0.99). The SD of measurements increased almost linearly from 32 ppb to 230 ppb as the measured susceptibility increased from 0.26 ppm to 3.56 ppm. A Bland-Altman plot showed the bias, upper, and lower limits of agreement for all comparisons were -10, -210, and 200 ppb, respectively. The ICC was 0.991 with a 95% CI (0.973, 0.99). CONCLUSIONS QSM shows excellent multicenter reproducibility for a large range of susceptibility values encountered in cranial and extra-cranial applications on a diverse set of scanner platforms.
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Affiliation(s)
- Kofi Deh
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Keigo Kawaji
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois.,Department of Medicine, University of Chicago Medical Center, Chicago, Illinois
| | - Marjolein Bulk
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Louise Van Der Weerd
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Emelie Lind
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | - Kelly McCabe Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
| | | | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York.,Department of Biomedical Engineering, Cornell University, Ithaca, New York
| | - Thanh D Nguyen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York
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Longitudinal Progression Markers of Parkinson's Disease: Current View on Structural Imaging. Curr Neurol Neurosci Rep 2018; 18:83. [PMID: 30280267 DOI: 10.1007/s11910-018-0894-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
PURPOSE OF REVIEW Advances in neuroimaging techniques pave a rich avenue for in vivo progression biomarkers, which can objectively and noninvasively assess the long-term dynamic alterations in the brain of Parkinson's disease (PD) patients. This article reviews recent progress in structural magnetic resonance imaging (MRI) tools to track disease progression in PD, and discusses specific criteria a neuroimaging tool needs to meet to be a progression biomarker of PD and the potential applications of these techniques in PD based on current evidence. RECENT FINDINGS Recent longitudinal studies showed that quantitative structural MRI markers derived from T1-weighted, diffusion-weighted, neuromelanin-sensitive, and iron-sensitive imaging have the potential to track disease progression in PD. However, validation of these progression biomarkers is only beginning, and more work is required for multisite validation, the sample size for use in a clinical trial, and drug-responsiveness of most of these biomarkers. At present, the most clinical trial-ready biomarker is free-water diffusion imaging of the substantia nigra and seems well established to be used in disease-modifying studies in PD. A variety of structural imaging biomarkers are promising candidates to be progression biomarkers in PD. Further studies are needed to elucidate the sensitivity, reliability, sample size, and effect of confounding factors of these progression biomarkers.
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Foucher JR, Mainberger O, Lamy J, Santin MD, Vignaud A, Roser MM, de Sousa PL. Multi-parametric quantitative MRI reveals three different white matter subtypes. PLoS One 2018; 13:e0196297. [PMID: 29906284 PMCID: PMC6003690 DOI: 10.1371/journal.pone.0196297] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 04/10/2018] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) shows slight spatial variations in brain white matter (WM). We used quantitative multi-parametric MRI to evaluate in what respect these inhomogeneities could correspond to WM subtypes with specific characteristics and spatial distribution. MATERIALS AND METHODS Twenty-six controls (12 women, 38 ±9 Y) took part in a 60-min session on a 3T scanner measuring 7 parameters: R1 and R2, diffusion tensor imaging which allowed to measure Axial and Radial Diffusivity (AD, RD), magnetization transfer imaging which enabled to compute the Macromolecular Proton Fraction (MPF), and a susceptibility-weighted sequence which permitted to quantify R2* and magnetic susceptibility (χm). Spatial independent component analysis was used to identify WM subtypes with specific combination of quantitative parameters values. RESULTS Three subtypes could be identified. t-WM (track) mostly mapped on well-formed projection and commissural tracts and came with high AD values (all p < 10(-18)). The two other subtypes were located in subcortical WM and overlapped with association fibers: f-WM (frontal) was mostly anterior in the frontal lobe whereas c-WM (central) was underneath the central cortex. f-WM and c-WM had higher MPF values, indicating a higher myelin content (all p < 1.7 10(-6)). This was compatible with their larger χm and R2, as iron is essentially stored in oligodendrocytes (all p < 0.01). Although R1 essentially showed the same, its higher value in t-WM relative to c-WM might be related to its higher cholesterol concentration. CONCLUSIONS Thus, f- and c-WMs were less structured, but more myelinated and probably more metabolically active regarding to their iron content than WM related to fasciculi (t-WM). As known WM bundles passed though different WM subtypes, myelination might not be uniform along the axons but rather follow a spatially consistent regional variability. Future studies might examine the reproducibility of this decomposition and how development and pathology differently affect each subtype.
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Affiliation(s)
- Jack R. Foucher
- Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), CNRS UMR 7357, University of Strasbourg, Strasbourg, France
- Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
- CEntre de neuroModulation Non Invasive de Strasbourg (CEMNIS), University Hospital, Strasbourg, France
- Department of Physiology, University of Strasbourg, Strasbourg, France
| | - Olivier Mainberger
- Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), CNRS UMR 7357, University of Strasbourg, Strasbourg, France
- Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
- CEntre de neuroModulation Non Invasive de Strasbourg (CEMNIS), University Hospital, Strasbourg, France
- Department of Physiology, University of Strasbourg, Strasbourg, France
| | - Julien Lamy
- Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), CNRS UMR 7357, University of Strasbourg, Strasbourg, France
- Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
| | | | | | - Mathilde M. Roser
- Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), CNRS UMR 7357, University of Strasbourg, Strasbourg, France
- Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
- CEntre de neuroModulation Non Invasive de Strasbourg (CEMNIS), University Hospital, Strasbourg, France
- Department of Physiology, University of Strasbourg, Strasbourg, France
| | - Paulo L. de Sousa
- Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), CNRS UMR 7357, University of Strasbourg, Strasbourg, France
- Fédération de Médecine Translationnelle de Strasbourg (FMTS), University of Strasbourg, Strasbourg, France
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Li J, Lin H, Liu T, Zhang Z, Prince MR, Gillen K, Yan X, Song Q, Hua T, Zhao X, Zhang M, Zhao Y, Li G, Tang G, Yang G, Brittenham GM, Wang Y. Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration. J Magn Reson Imaging 2018; 48:1069-1079. [PMID: 29566449 DOI: 10.1002/jmri.26019] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 03/06/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A challenge for R2 and R2* methods in measuring liver iron concentration (LIC) is that fibrosis, fat, and other hepatic cellular pathology contribute to R2 and R2* and interfere with LIC estimation. PURPOSE To examine the interfering effects of fibrosis, fat, and other lesions on R2* LIC estimation and to use quantitative susceptibility mapping (QSM) to reduce these distortions. STUDY TYPE Prospective. PHANTOMS, SUBJECTS Water phantoms with various concentrations of gadolinium (Gd), collagen (Cl, modeling fibrosis), and fat; nine healthy controls with no known hepatic disease, nine patients with known or suspected hepatic iron overload, and nine patients with focal liver lesions. FIELD STRENGTH/SEQUENCE The phantoms and human subjects were imaged using a 3D multiecho gradient-echo on clinical 1.5T and 3T MRI systems. ASSESSMENT QSM and R2* images were postprocessed from the same gradient-echo data. Fat contributions to susceptibility and R2* were corrected in signal models for LIC estimation. STATISTICAL TESTS Polynomial regression analyses were performed to examine relations among susceptibility, R2* and true [Gd] and [Cl] in phantoms, and among susceptibility and R2* in patient livers. RESULTS In phantoms, R2* had a strong nonlinear dependency on [Cl], [fat], and [Gd], while susceptibility was linearly dependent (R2 > 0.98). In patients, R2* was highly sensitive to liver pathological changes, including fat, fibrosis, and tumors, while QSM was relatively insensitive to these abnormalities (P = 0.015). With moderate iron overload, liver susceptibility and R2* were not linearly correlated over a common R2* range [0, 100] sec-1 (P = 0.35). DATA CONCLUSION R2* estimation of LIC is prone to substantial nonlinear interference from fat, fibrosis, and other lesions. QSM processing of the same gradient echo MRI data can effectively minimize the effects of cellular pathology. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1069-1079.
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Affiliation(s)
- Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tian Liu
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Zhuwei Zhang
- Department of Radiology, Shanghai Tenth People's Hospital Affiliated to Tongji University, School of Medicine, Shanghai, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Kelly Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Xu Yan
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Qi Song
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Hua
- Department of Radiology, Shanghai Tenth People's Hospital Affiliated to Tongji University, School of Medicine, Shanghai, China
| | - Xiance Zhao
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Miao Zhang
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Yu Zhao
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People's Hospital Affiliated to Tongji University, School of Medicine, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China
| | - Gary M Brittenham
- Department of Pediatrics, Columbia University, New York, New York, USA
| | - Yi Wang
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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42
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Duyn JH. Studying brain microstructure with magnetic susceptibility contrast at high-field. Neuroimage 2018; 168:152-161. [PMID: 28242317 PMCID: PMC5569005 DOI: 10.1016/j.neuroimage.2017.02.046] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 02/03/2017] [Accepted: 02/16/2017] [Indexed: 12/14/2022] Open
Abstract
A rapidly developing application of high field MRI is the study of brain anatomy and function with contrast based on the magnetic susceptibility of tissues. To study the subtle variations in susceptibility contrast between tissues and with changes in brain activity, dedicated scan techniques such as susceptibility-weighted MRI and blood-oxygen level dependent functional MRI have been developed. Particularly strong susceptibility contrast has been observed with systems that operate at 7T and above, and their recent widespread use has led to an improved understanding of contributing sources and mechanisms. To interpret magnetic susceptibility contrast, analysis approaches have been developed with the goal of extracting measures that report on local tissue magnetic susceptibility, a physical quantity that, under certain conditions, allows estimation of blood oxygenation, local tissue iron content, and quantification of its changes with disease. Interestingly, high field studies have also brought to light that not only the makeup of tissues affects MRI susceptibility contrast, but that also a tissue's sub-voxel structure at scales all the way down to the molecular level plays an important role as well. In this review, various ways will be discussed by which sub-voxel structure can affect the MRI signal in general, and magnetic susceptibility in particular, sometimes in a complex fashion. In the light of this complexity, it appears likely that accurate, brain-wide quantification of iron will require the combination of multiple contrasts that may include diffusion and magnetization transfer information with susceptibility-weighted contrast. On the other hand, this complexity also offers opportunities to use magnetic susceptibility contrast to inform about specific microstructural aspects of brain tissue. Details and several examples will be presented in this review.
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Affiliation(s)
- Jeff H Duyn
- Advanced MRI Section, Laboratory of Functional and Molecular Imaging, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
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43
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Schweser F, Raffaini Duarte Martins AL, Hagemeier J, Lin F, Hanspach J, Weinstock-Guttman B, Hametner S, Bergsland N, Dwyer MG, Zivadinov R. Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: A proposed mechanistic relationship between inflammation and oligodendrocyte vitality. Neuroimage 2018; 167:438-452. [PMID: 29097315 PMCID: PMC5845810 DOI: 10.1016/j.neuroimage.2017.10.063] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/24/2017] [Accepted: 10/27/2017] [Indexed: 12/13/2022] Open
Abstract
Recent advances in susceptibility MRI have dramatically improved the visualization of deep gray matter brain regions and the quantification of their magnetic properties in vivo, providing a novel tool to study the poorly understood iron homeostasis in the human brain. In this study, we used an advanced combination of the recent quantitative susceptibility mapping technique with dedicated analysis methods to study intra-thalamic tissue alterations in patients with clinically isolated syndrome (CIS) and multiple sclerosis (MS). Thalamic pathology is one of the earliest hallmarks of MS and has been shown to correlate with cognitive dysfunction and fatigue, but the mechanisms underlying the thalamic pathology are poorly understood. We enrolled a total of 120 patients, 40 with CIS, 40 with Relapsing Remitting MS (RRMS), and 40 with Secondary Progressive MS (SPMS). For each of the three patient groups, we recruited 40 controls, group matched for age- and sex (120 total). We acquired quantitative susceptibility maps using a single-echo gradient echo MRI pulse sequence at 3 T. Group differences were studied by voxel-based analysis as well as with a custom thalamus atlas. We used threshold-free cluster enhancement (TFCE) and multiple regression analyses, respectively. We found significantly reduced magnetic susceptibility compared to controls in focal thalamic subregions of patients with RRMS (whole thalamus excluding the pulvinar nucleus) and SPMS (primarily pulvinar nucleus), but not in patients with CIS. Susceptibility reduction was significantly associated with disease duration in the pulvinar, the left lateral nuclear region, and the global thalamus. Susceptibility reduction indicates a decrease in tissue iron concentration suggesting an involvement of chronic microglia activation in the depletion of iron from oligodendrocytes in this central and integrative brain region. Not necessarily specific to MS, inflammation-mediated iron release may lead to a vicious circle that reduces the protection of axons and neuronal repair.
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Affiliation(s)
- Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | - Ana Luiza Raffaini Duarte Martins
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Fuchun Lin
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Jannis Hanspach
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Institute of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Simon Hametner
- Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA
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44
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Hagemeier J, Zivadinov R, Dwyer MG, Polak P, Bergsland N, Weinstock-Guttman B, Zalis J, Deistung A, Reichenbach JR, Schweser F. Changes of deep gray matter magnetic susceptibility over 2 years in multiple sclerosis and healthy control brain. NEUROIMAGE-CLINICAL 2017; 18:1007-1016. [PMID: 29868452 PMCID: PMC5984575 DOI: 10.1016/j.nicl.2017.04.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 04/07/2017] [Accepted: 04/09/2017] [Indexed: 01/21/2023]
Abstract
In multiple sclerosis, pathological changes of both tissue iron and myelin occur, yet these factors have not been characterized in a longitudinal fashion using the novel iron- and myelin-sensitive quantitative susceptibility mapping (QSM) MRI technique. We investigated disease-relevant tissue changes associated with myelin loss and iron accumulation in multiple sclerosis deep gray matter (DGM) over two years. One-hundred twenty (120) multiple sclerosis patients and 40 age- and sex-matched healthy controls were included in this prospective study. Written informed consent and local IRB approval were obtained from all participants. Clinical testing and QSM were performed both at baseline and at follow-up. Brain magnetic susceptibility was measured in major DGM structures. Temporal (baseline vs. follow-up) and cross-sectional (multiple sclerosis vs. controls) differences were studied using mixed factorial ANOVA analysis and appropriate t-tests. At either time-point, multiple sclerosis patients had significantly higher susceptibility in the caudate and globus pallidus and lower susceptibility in the thalamus. Over two years, susceptibility increased significantly in the caudate of both controls and multiple sclerosis patients. Inverse thalamic findings among MS patients suggest a multi-phase pathology explained by simultaneous myelin loss and/or iron accumulation followed by iron depletion and/or calcium deposition at later stages.
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Affiliation(s)
- Jesper Hagemeier
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA.
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Paul Polak
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; IRCCS Don Gnocchi Foundation, Milan, Italy
| | - Bianca Weinstock-Guttman
- Jacobs Multiple Sclerosis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Joshua Zalis
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany; Section of Experimental Neurology, Department of Neurology, Essen University Hospital, Essen, Germany; Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Germany; Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Germany
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
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45
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Eskreis-Winkler S, Zhang Y, Zhang J, Liu Z, Dimov A, Gupta A, Wang Y. The clinical utility of QSM: disease diagnosis, medical management, and surgical planning. NMR IN BIOMEDICINE 2017; 30:e3668. [PMID: 27906525 DOI: 10.1002/nbm.3668] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 09/22/2016] [Accepted: 10/11/2016] [Indexed: 06/06/2023]
Abstract
Quantitative susceptibility mapping (QSM) is an MR technique that depicts and quantifies magnetic susceptibility sources. Mapping iron, the dominant susceptibility source in the brain, has many important clinical applications. Herein, we review QSM applications in the diagnosis, medical management, and surgical treatment of disease. To assist in early disease diagnosis, QSM can identify elevated iron levels in the motor cortex of amyotrophic lateral sclerosis patients, in the substantia nigra of Parkinson's disease (PD) patients, in the globus pallidus, putamen, and caudate of Huntington's disease patients, and in the basal ganglia of Wilson's disease patients. Additionally, QSM can distinguish between hemorrhage and calcification, which could prove useful in tumor subclassification, and can measure microbleeds in traumatic brain injury patients. In guiding medical management, QSM can be used to monitor iron chelation therapy in PD patients, to monitor smoldering inflammation of multiple sclerosis (MS) lesions after the blood-brain barrier (BBB) seals, to monitor active inflammation of MS lesions before the BBB seals without using gadolinium, and to monitor hematoma volume in intracerebral hemorrhage. QSM can also guide neurosurgical treatment. Neurosurgeons require accurate depiction of the subthalamic nucleus, a tiny deep gray matter nucleus, prior to inserting deep brain stimulation electrodes into the brains of PD patients. QSM is arguably the best imaging tool for depiction of the subthalamic nucleus. Finally, we discuss future directions, including bone QSM, cardiac QSM, and using QSM to map cerebral metabolic rate of oxygen. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
| | - Yan Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Jingwei Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Zhe Liu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Alexey Dimov
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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46
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Stability of R2* and quantitative susceptibility mapping of the brain tissue in a large scale multi-center study. Sci Rep 2017; 7:45261. [PMID: 28349957 PMCID: PMC5368661 DOI: 10.1038/srep45261] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 02/27/2017] [Indexed: 12/21/2022] Open
Abstract
Multi-center studies are advantageous for enrolling participants of varying pathological and demographical conditions, and especially in neurological studies. Hence stability of the obtained quantitative R2* and susceptibility in multicenter studies is a key issue for their widespread applications. In this work, the stabilities of simultaneously obtained R2* and susceptibility are investigated and compared across 10 sites that are equipped with the same scanner and receiver coil, the same post-processing process was used to achieve consistent experiment setup. Two healthy adult volunteers (one male and female) participated in this study. High intraclass correlation coefficient was obtained for both susceptibility (0.94) and R2* (0.96). The coefficients of variance for all measurements obtained were smaller than 0.1, the largest variations of measurements in all the chosen ROIs fall within ±20% from the median value. Higher level of stability was obtained in R2* as compared to susceptibility at 1 mm resolution (P < 0.05) and at 1.5 mm (P < 0.01).
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47
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Lehericy S, Vaillancourt DE, Seppi K, Monchi O, Rektorova I, Antonini A, McKeown MJ, Masellis M, Berg D, Rowe JB, Lewis SJG, Williams-Gray CH, Tessitore A, Siebner HR. The role of high-field magnetic resonance imaging in parkinsonian disorders: Pushing the boundaries forward. Mov Disord 2017; 32:510-525. [PMID: 28370449 DOI: 10.1002/mds.26968] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 12/22/2016] [Accepted: 01/15/2017] [Indexed: 12/28/2022] Open
Abstract
Historically, magnetic resonance imaging (MRI) has contributed little to the study of Parkinson's disease (PD), but modern MRI approaches have unveiled several complementary markers that are useful for research and clinical applications. Iron- and neuromelanin-sensitive MRI detect qualitative changes in the substantia nigra. Quantitative MRI markers can be derived from diffusion weighted and iron-sensitive imaging or volumetry. Functional brain alterations at rest or during task performance have been captured with functional and arterial spin labeling perfusion MRI. These markers are useful for the diagnosis of PD and atypical parkinsonism, to track disease progression from the premotor stages of these diseases and to better understand the neurobiological basis of clinical deficits. A current research goal using MRI is to generate time-dependent models of the evolution of PD biomarkers that can help understand neurodegeneration and provide reliable markers for therapeutic trials. This article reviews recent advances in MRI biomarker research at high-field (3T) and ultra high field-imaging (7T) in PD and atypical parkinsonism. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Stéphane Lehericy
- Institut du Cerveau et de la Moelle épinière - ICM, Centre de NeuroImagerie de Recherche - CENIR, Sorbonne Universités, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - David E Vaillancourt
- Department of Applied Physiology and Kinesiology, Department of Neurology and Centre for Movement Disorders and Neurorestoration, Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Klaus Seppi
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria and Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria
| | - Oury Monchi
- Department of Clinical Neurosciences, Department of Radiology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Irena Rektorova
- First Department of Neurology, School of Medicine, St. Anne's University Hospital, Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Angelo Antonini
- Parkinson and Movement Disorders Unit, istituto di ricovero e cura a carattere scientifico (IRCCS) Hospital San Camillo, Venice and Department of Neurosciences (DNS), Padova University, Padova, Italy
| | - Martin J McKeown
- Pacific Parkinson's Research Center, Department of Medicine (Neurology), University of British Columbia Vancouver, BC, Canada
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Daniela Berg
- Department of Neurology, Christian-Albrechts-University of Kiel and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - James B Rowe
- Department of Clinical Neurosciences, Cambridge University, and Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - Simon J G Lewis
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Caroline H Williams-Gray
- John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alessandro Tessitore
- Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Department of Neurology, Copenhagen University Hospital Bispebjerg, Hvidovre, Denmark
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48
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Wang Y, Spincemaille P, Liu Z, Dimov A, Deh K, Li J, Zhang Y, Yao Y, Gillen KM, Wilman AH, Gupta A, Tsiouris AJ, Kovanlikaya I, Chiang GCY, Weinsaft JW, Tanenbaum L, Chen W, Zhu W, Chang S, Lou M, Kopell BH, Kaplitt MG, Devos D, Hirai T, Huang X, Korogi Y, Shtilbans A, Jahng GH, Pelletier D, Gauthier SA, Pitt D, Bush AI, Brittenham GM, Prince MR. Clinical quantitative susceptibility mapping (QSM): Biometal imaging and its emerging roles in patient care. J Magn Reson Imaging 2017; 46:951-971. [PMID: 28295954 DOI: 10.1002/jmri.25693] [Citation(s) in RCA: 178] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 02/10/2017] [Indexed: 12/13/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) has enabled magnetic resonance imaging (MRI) of tissue magnetic susceptibility to advance from simple qualitative detection of hypointense blooming artifacts to precise quantitative measurement of spatial biodistributions. QSM technology may be regarded to be sufficiently developed and validated to warrant wide dissemination for clinical applications of imaging isotropic susceptibility, which is dominated by metals in tissue, including iron and calcium. These biometals are highly regulated as vital participants in normal cellular biochemistry, and their dysregulations are manifested in a variety of pathologic processes. Therefore, QSM can be used to assess important tissue functions and disease. To facilitate QSM clinical translation, this review aims to organize pertinent information for implementing a robust automated QSM technique in routine MRI practice and to summarize available knowledge on diseases for which QSM can be used to improve patient care. In brief, QSM can be generated with postprocessing whenever gradient echo MRI is performed. QSM can be useful for diseases that involve neurodegeneration, inflammation, hemorrhage, abnormal oxygen consumption, substantial alterations in highly paramagnetic cellular iron, bone mineralization, or pathologic calcification; and for all disorders in which MRI diagnosis or surveillance requires contrast agent injection. Clinicians may consider integrating QSM into their routine imaging practices by including gradient echo sequences in all relevant MRI protocols. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017;46:951-971.
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Affiliation(s)
- Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Ithaca, New York, USA
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Zhe Liu
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Ithaca, New York, USA
| | - Alexey Dimov
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Biomedical Engineering, Ithaca, New York, USA
| | - Kofi Deh
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Jianqi Li
- Department of Physics, East China Normal University, Shanghai, P.R. China
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Yihao Yao
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA.,Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | | | - Ilhami Kovanlikaya
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | | | - Jonathan W Weinsaft
- Division of Cardiology, Department of Medicine, Weill Cornell Medical College, New York, New York, USA
| | | | - Weiwei Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, P.R. China
| | - Shixin Chang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese & Western Medicine, Shanghai, P.R. China
| | - Min Lou
- Department of Neurology, the Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, P.R. China
| | - Brian H Kopell
- Department of Neurosurgery, Mount Sinai Hospital, New York, New York, USA
| | - Michael G Kaplitt
- Department of Neurological Surgery, Weill Cornell Medical College, New York, New York, USA
| | - David Devos
- Department of Medical Pharmacology, University of Lille, Lille, France.,Department of Neurology and Movement Disorders, University of Lille, Lille, France.,Department of Toxicology, Public Health and Environment, University of Lille, Lille, France.,INSERM U1171, University of Lille, Lille, France
| | - Toshinori Hirai
- Department of Radiology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Xuemei Huang
- Department of Neurology, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA.,Department of Pharmacology, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA.,Department of Neurosurgery, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA.,Department of Radiology, Penn State University-Milton S. Hershey Medical Center, Hershey, Pennsylvania, USA
| | - Yukunori Korogi
- Department of Radiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Alexander Shtilbans
- Department of Neurology, Hospital for Special Surgery, New York, New York, USA.,Parkinson's Disease and Movement Disorder Institute, Weill Cornell Medical College, New York, New York, USA
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, South Korea
| | - Daniel Pelletier
- Department of Neurology, Department of Neurology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Susan A Gauthier
- Department of Neurology and Neuroscience, Weill Cornell Medical College, New York, New York, USA
| | - David Pitt
- Department of Neurology, School of Medicine, Yale University, New Haven, Connecticut, USA
| | - Ashley I Bush
- Oxidation Biology Unit, Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Gary M Brittenham
- Department of Pediatrics, Columbia University, Children's Hospital of New York, New York, New York, USA
| | - Martin R Prince
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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49
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Wang Z, Luo XG, Gao C. Utility of susceptibility-weighted imaging in Parkinson's disease and atypical Parkinsonian disorders. Transl Neurodegener 2016; 5:17. [PMID: 27761236 PMCID: PMC5054585 DOI: 10.1186/s40035-016-0064-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 09/29/2016] [Indexed: 01/14/2023] Open
Abstract
In the clinic, the diagnosis of Parkinson's disease (PD) largely depends on clinicians' experience. When the diagnosis is made, approximately 80% of dopaminergic cells in the substantia nigra (SN) have been lost. Additionally, it is rather challenging to differentiate PD from atypical parkinsonian disorders (APD). Clinially-available 3T conventional MRI contributes little to solve these problems. The pathologic alterations of parkinsonism show abnormal brain iron deposition, and therefore susceptibility-weighted imaging (SWI), which is sensitive to iron concentration, has been applied to find iron-related lesions for the diagnosis and differentiation of PD in recent decades. Until now, the majority of research has revealed that in SWI the signal intensity changes in deep brain nuclei, such as the SN, the putamen (PUT), the globus pallidus (GP), the thalamus (TH), the red nucleus (RN) and the caudate nucleus (CN), thereby raising the possibility of early diagnosis and differentiation. Furthermore, the signal changes in SN, PUT and TH sub-regions may settle the issues with higher accuracy. In this article, we review the brain iron deposition of PD, MSA-P and PSP in SWI in the hope of exhibiting a profile of SWI features in PD, MSA and PSP and its clinical values.
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Affiliation(s)
- Zhibin Wang
- Neurology Department, The First Affiliated Hospital of China Medical University, 155# Nanjing Bei Street Heping District, Shenyang, 110001 People's Republic of China
| | - Xiao-Guang Luo
- Neurology Department, The First Affiliated Hospital of China Medical University, 155# Nanjing Bei Street Heping District, Shenyang, 110001 People's Republic of China
| | - Chao Gao
- Neurology Department, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Ruijin 2nd Road 197, Shanghai, 200025 People's Republic of China
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Visser E, Keuken MC, Forstmann BU, Jenkinson M. Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7T data at young and old age. Neuroimage 2016; 139:324-336. [PMID: 27349329 PMCID: PMC4988791 DOI: 10.1016/j.neuroimage.2016.06.039] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 05/28/2016] [Accepted: 06/20/2016] [Indexed: 12/29/2022] Open
Abstract
With recent developments in MR acquisition at 7T, smaller brainstem structures such as the red nuclei, substantia nigra and subthalamic nuclei can be imaged with good contrast and resolution. These structures have important roles both in the study of the healthy brain and in diseases such as Parkinson's disease, but few methods have been described to automatically segment them. In this paper, we extend a method that we have previously proposed for segmentation of the striatum and globus pallidus to segment these deeper and smaller structures. We modify the method to allow more direct control over segmentation smoothness by using a Markov random field prior. We investigate segmentation performance in three age groups and show that the method produces consistent results that correspond well with manual segmentations. We perform a vertex-based analysis to identify changes with age in the shape of the structures and present results suggesting that the method may be at least as effective as manual delineation in capturing differences between subjects.
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Affiliation(s)
- Eelke Visser
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Max C Keuken
- Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
| | - Birte U Forstmann
- Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
| | - Mark Jenkinson
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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