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Kebiri H, Gholipour A, Lin R, Vasung L, Calixto C, Krsnik Ž, Karimi D, Bach Cuadra M. Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study. Med Image Anal 2024; 95:103186. [PMID: 38701657 DOI: 10.1016/j.media.2024.103186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/06/2024] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rizhong Lin
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Kebiri H, Gholipour A, Vasung L, Krsnik Ž, Karimi D, Cuadra MB. Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.01.547351. [PMID: 37425859 PMCID: PMC10327173 DOI: 10.1101/2023.07.01.547351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation of FODs requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results to standard methods such as Constrained Spherical Deconvolution. We demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical datasets of newborns and fetuses. Additionally, we compute agreement metrics within the HARDI newborn dataset, and validate fetal FODs with post-mortem histological data. The results of this study show the advantage of deep learning in inferring the microstructure of the developing brain from in-vivo dMRI measurements that are often very limited due to subject motion and limited acquisition times, but also highlight the intrinsic limitations of dMRI in the analysis of the developing brain microstructure. These findings, therefore, advocate for the need for improved methods that are tailored to studying the early development of human brain.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Koay CG, Yeh PH, Ollinger JM, İrfanoğlu MO, Pierpaoli C, Basser PJ, Oakes TR, Riedy G. Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging. Neuroimage 2015; 126:151-63. [PMID: 26638985 DOI: 10.1016/j.neuroimage.2015.11.046] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Revised: 11/05/2015] [Accepted: 11/18/2015] [Indexed: 11/19/2022] Open
Abstract
The purpose of this work is to develop a framework for single-subject analysis of diffusion tensor imaging (DTI) data. This framework is termed Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI) because it is capable of testing whether an individual tract as represented by the major eigenvector of the diffusion tensor and its corresponding angular dispersion are significantly different from a group of tracts on a voxel-by-voxel basis. This work develops two complementary statistical tests based on the elliptical cone of uncertainty, which is a model of uncertainty or dispersion of the major eigenvector of the diffusion tensor. The orientation deviation test examines whether the major eigenvector from a single subject is within the average elliptical cone of uncertainty formed by a collection of elliptical cones of uncertainty. The shape deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test between the normalized shape measures (area and circumference) of the elliptical cones of uncertainty of the single subject against a group of controls. The False Discovery Rate (FDR) and False Non-discovery Rate (FNR) were incorporated in the orientation deviation test. The shape deviation test uses FDR only. TOADDI was found to be numerically accurate and statistically effective. Clinical data from two Traumatic Brain Injury (TBI) patients and one non-TBI subject were tested against the data obtained from a group of 45 non-TBI controls to illustrate the application of the proposed framework in single-subject analysis. The frontal portion of the superior longitudinal fasciculus seemed to be implicated in both tests (orientation and shape) as significantly different from that of the control group. The TBI patients and the single non-TBI subject were well separated under the shape deviation test at the chosen FDR level of 0.0005. TOADDI is a simple but novel geometrically based statistical framework for analyzing DTI data. TOADDI may be found useful in single-subject, graph-theoretic and group analyses of DTI data or DTI-based tractography techniques.
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Affiliation(s)
- Cheng Guan Koay
- National Intrepid Center of Excellence (NICoE), Bethesda, MD, USA; Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD, USA; NorthTide Group, LLC, USA.
| | - Ping-Hong Yeh
- National Intrepid Center of Excellence (NICoE), Bethesda, MD, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - John M Ollinger
- National Intrepid Center of Excellence (NICoE), Bethesda, MD, USA
| | - M Okan İrfanoğlu
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA; Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD, USA
| | - Carlo Pierpaoli
- Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Basser
- Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD, USA
| | - Terrence R Oakes
- National Intrepid Center of Excellence (NICoE), Bethesda, MD, USA
| | - Gerard Riedy
- National Intrepid Center of Excellence (NICoE), Bethesda, MD, USA; National Capital Neuroimaging Consortium, Bethesda, MD, USA
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Lohezic M, Bollensdorff C, Korn M, Lanz T, Grau V, Kohl P, Schneider JE. Optimized radiofrequency coil setup for MR examination of living isolated rat hearts in a horizontal 9.4T magnet. Magn Reson Med 2014; 73:2398-405. [PMID: 25045897 DOI: 10.1002/mrm.25369] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2014] [Revised: 06/12/2014] [Accepted: 06/24/2014] [Indexed: 12/12/2022]
Abstract
PURPOSE (i) To optimize an MR-compatible organ perfusion setup for the nondestructive investigation of isolated rat hearts by placing the radiofrequency (RF) coil inside the perfusion chamber; (ii) to characterize the benefit of this system for diffusion tensor imaging and proton ((1) H-) MR spectroscopy. METHODS Coil quality assessment was conducted both on the bench, and in the magnet. The benefit of the new RF-coil was quantified by measuring signal-to-noise ratio (SNR), accuracy, and precision of diffusion tensor imaging/error in metabolite amplitude estimation, and compared to an RF-coil placed externally to the perfusion chamber. RESULTS The new design provided a 59% gain in signal-to-noise ratio on a fixed rat heart compared to using an external resonator, which found reflection in an improvement of living heart data quality, compared to previous external resonator studies. This resulted in 14-29% improvement in accuracy and precision of diffusion tensor imaging. The Cramer-Rao lower bounds for metabolite amplitude estimations were up to 5-fold smaller. CONCLUSION Optimization of MR-compatible perfusion equipment advances the study of rat hearts with improved signal-to-noise ratio performance, and thus improved accuracy/precision.
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Affiliation(s)
- Maelene Lohezic
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Christian Bollensdorff
- National Heart and Lung Institute, Imperial College London, London, UK.,Qatar Cardiovascular Research Center, Qatar Foundation, Doha, Qatar
| | | | | | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Peter Kohl
- National Heart and Lung Institute, Imperial College London, London, UK.,Department of Computer Science, University of Oxford, Oxford, UK
| | - Jürgen E Schneider
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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Beltrachini L, von Ellenrieder N, Muravchik CH. Error bounds in diffusion tensor estimation using multiple-coil acquisition systems. Magn Reson Imaging 2013; 31:1372-83. [DOI: 10.1016/j.mri.2013.04.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2012] [Revised: 04/23/2013] [Accepted: 04/26/2013] [Indexed: 11/25/2022]
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Choe AS, Stepniewska I, Colvin DC, Ding Z, Anderson AW. Validation of diffusion tensor MRI in the central nervous system using light microscopy: quantitative comparison of fiber properties. NMR IN BIOMEDICINE 2012; 25:900-908. [PMID: 22246940 DOI: 10.1002/nbm.v25.7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2010] [Revised: 09/05/2011] [Accepted: 10/18/2011] [Indexed: 05/22/2023]
Abstract
Diffusion tensor imaging (DTI) provides an indirect measure of tissue structure on a microscopic scale. To date, DTI is the only imaging method that provides such information in vivo, and has proven to be a valuable tool in both research and clinical settings. In this study, we investigated the relationship between white matter structure and diffusion parameters measured by DTI. We used micrographs from light microscopy of fixed, myelin-stained brain sections as a gold standard for direct comparison with data from DTI. Relationships between microscopic tissue properties observed with light microscopy (fiber orientation, density and coherence) and fiber properties observed by DTI (tensor orientation, diffusivities and fractional anisotropy) were investigated. Agreement between the major eigenvector of the tensor and myelinated fibers was excellent in voxels with high fiber coherence. In addition, increased fiber spread was strongly associated with increased radial diffusivity (p = 6 × 10(-6)) and decreased fractional anisotropy (p = 5 × 10(-8)), and was weakly associated with decreased axial diffusivity (p = 0.07). Increased fiber density was associated with increased fractional anisotropy (p = 0.03), and weakly associated with decreased radial diffusivity (p < 0.06), but not with axial diffusivity (p = 0.97). The mean diffusivity was largely independent of fiber spread (p = 0.24) and fiber density (p = 0.34).
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Affiliation(s)
- A S Choe
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA.
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Choe AS, Stepniewska I, Colvin DC, Ding Z, Anderson AW. Validation of diffusion tensor MRI in the central nervous system using light microscopy: quantitative comparison of fiber properties. NMR IN BIOMEDICINE 2012; 25:900-8. [PMID: 22246940 PMCID: PMC4818098 DOI: 10.1002/nbm.1810] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2010] [Revised: 09/05/2011] [Accepted: 10/18/2011] [Indexed: 05/11/2023]
Abstract
Diffusion tensor imaging (DTI) provides an indirect measure of tissue structure on a microscopic scale. To date, DTI is the only imaging method that provides such information in vivo, and has proven to be a valuable tool in both research and clinical settings. In this study, we investigated the relationship between white matter structure and diffusion parameters measured by DTI. We used micrographs from light microscopy of fixed, myelin-stained brain sections as a gold standard for direct comparison with data from DTI. Relationships between microscopic tissue properties observed with light microscopy (fiber orientation, density and coherence) and fiber properties observed by DTI (tensor orientation, diffusivities and fractional anisotropy) were investigated. Agreement between the major eigenvector of the tensor and myelinated fibers was excellent in voxels with high fiber coherence. In addition, increased fiber spread was strongly associated with increased radial diffusivity (p = 6 × 10(-6)) and decreased fractional anisotropy (p = 5 × 10(-8)), and was weakly associated with decreased axial diffusivity (p = 0.07). Increased fiber density was associated with increased fractional anisotropy (p = 0.03), and weakly associated with decreased radial diffusivity (p < 0.06), but not with axial diffusivity (p = 0.97). The mean diffusivity was largely independent of fiber spread (p = 0.24) and fiber density (p = 0.34).
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Affiliation(s)
- A S Choe
- Vanderbilt University Institute of Imaging Science, Nashville, TN, USA.
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Signal-to-noise ratio of diffusion weighted magnetic resonance imaging: Estimation methods and in vivo application to spinal cord. Biomed Signal Process Control 2012. [DOI: 10.1016/j.bspc.2011.06.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Lazar M. Mapping brain anatomical connectivity using white matter tractography. NMR IN BIOMEDICINE 2010; 23:821-35. [PMID: 20886567 PMCID: PMC4503207 DOI: 10.1002/nbm.1579] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Integration of the neural processes in the human brain is realized through interconnections that exist between different neural centers. These interconnections take place through white matter pathways. White matter tractography is currently the only available technique for the reconstruction of the anatomical connectivity in the human brain noninvasively and in vivo. The trajectory and terminations of white matter pathways are estimated from local orientations of nerve bundles. These orientations are obtained using measurements of water diffusion in the brain. In this article, the techniques for estimating fiber directions from diffusion measurements in the human brain are reviewed. Methods of white matter tractography are described, together with the current limitations of the technique, including sensitivity to image noise and partial voluming. The applications of white matter tractography to the topographical characterization of the white matter connections and the segmentation of specific white matter pathways, and corresponding functional units of gray matter, are discussed. In this context, the potential impact of white matter tractography in mapping the functional systems and subsystems in the human brain, and their interrelations, is described. Finally, the applications of white matter tractography to the study of brain disorders, including fiber tract localization in brains affected by tumors and the identification of impaired connectivity routes in neurologic and neuropsychiatric diseases, are discussed.
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Affiliation(s)
- Mariana Lazar
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA.
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Chung HW, Chou MC, Chen CY. Principles and limitations of computational algorithms in clinical diffusion tensor MR tractography. AJNR Am J Neuroradiol 2010; 32:3-13. [PMID: 20299436 DOI: 10.3174/ajnr.a2041] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
There have been numerous reports documenting the graphic reconstruction of 3D white matter architecture in the human brain by means of diffusion tensor MR tractography. Different from other reviews addressing the physics and clinical applications of DTI, this article reviews the computational principles of tractography algorithms appearing in the literature. The simplest voxel-based method and 2 widely used subvoxel approaches are illustrated first, together with brief notes on parameter selection and the restrictions arising from the distinct attributes of tract estimations. Subsequently, some advanced techniques attempting to offer improvement in various aspects are briefly introduced, including the increasingly popular research tracking tool using HARDI. The article explains the inherent technical limitations in most of the algorithms reported to date and concludes by providing a reference guideline for formulating routine applications of this important tool to clinical neuroradiology in an objective and reproducible manner.
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Affiliation(s)
- H-W Chung
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, Republic of China
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Koay CG, Ozarslan E, Pierpaoli C. Probabilistic Identification and Estimation of Noise (PIESNO): a self-consistent approach and its applications in MRI. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2009; 199:94-103. [PMID: 19346143 PMCID: PMC2732005 DOI: 10.1016/j.jmr.2009.03.005] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2008] [Revised: 03/16/2009] [Accepted: 03/17/2009] [Indexed: 05/18/2023]
Abstract
Data analysis in MRI usually entails a series of processing procedures. One of these procedures is noise assessment, which in the context of this work, includes both the identification of noise-only pixels and the estimation of noise variance (standard deviation). Although noise assessment is critical to many MRI processing techniques, the identification of noise-only pixels has received less attention than has the estimation of noise variance. The main objectives of this paper are, therefore, to demonstrate (a) that the identification of noise-only pixels has an important role to play in the analysis of MRI data, (b) that the identification of noise-only pixels and the estimation of noise variance can be combined into a coherent framework, and (c) that this framework can be made self-consistent. To this end, we propose a novel iterative approach to simultaneously identify noise-only pixels and estimate the noise standard deviation from these identified pixels in a commonly used data structure in MRI. Experimental and simulated data were used to investigate the feasibility, the accuracy and the stability of the proposed technique.
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Affiliation(s)
- Cheng Guan Koay
- Section on Tissue Biophysics and Biomimetics, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 13 South Drive, MSC 5772, Bethesda, MD 20892-5772, USA.
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