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Bagnato F, Sati P, Hemond CC, Elliott C, Gauthier SA, Harrison DM, Mainero C, Oh J, Pitt D, Shinohara RT, Smith SA, Trapp B, Azevedo CJ, Calabresi PA, Henry RG, Laule C, Ontaneda D, Rooney WD, Sicotte NL, Reich DS, Absinta M. Imaging chronic active lesions in multiple sclerosis: a consensus statement. Brain 2024; 147:2913-2933. [PMID: 38226694 PMCID: PMC11370808 DOI: 10.1093/brain/awae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 11/21/2023] [Accepted: 12/08/2023] [Indexed: 01/17/2024] Open
Abstract
Chronic active lesions (CAL) are an important manifestation of chronic inflammation in multiple sclerosis and have implications for non-relapsing biological progression. In recent years, the discovery of innovative MRI and PET-derived biomarkers has made it possible to detect CAL, and to some extent quantify them, in the brain of persons with multiple sclerosis, in vivo. Paramagnetic rim lesions on susceptibility-sensitive MRI sequences, MRI-defined slowly expanding lesions on T1-weighted and T2-weighted scans, and 18-kDa translocator protein-positive lesions on PET are promising candidate biomarkers of CAL. While partially overlapping, these biomarkers do not have equivalent sensitivity and specificity to histopathological CAL. Standardization in the use of available imaging measures for CAL identification, quantification and monitoring is lacking. To fast-forward clinical translation of CAL, the North American Imaging in Multiple Sclerosis Cooperative developed a consensus statement, which provides guidance for the radiological definition and measurement of CAL. The proposed manuscript presents this consensus statement, summarizes the multistep process leading to it, and identifies the remaining major gaps in knowledge.
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Affiliation(s)
- Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Neurology, Nashville VA Medical Center, Tennessee Valley Healthcare System, Nashville, TN 37212, USA
| | - Pascal Sati
- Neuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Christopher C Hemond
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | | | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, NY 10021, USA
| | - Daniel M Harrison
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Department of Neurology, Baltimore VA Medical Center, VA Maryland Healthcare System, Baltimore, MD 21201, USA
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jiwon Oh
- Division of Neurology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5S, Canada
| | - David Pitt
- Department of Neurology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37235, USA
| | - Bruce Trapp
- Department on Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Christina J Azevedo
- Department of Neurology, Keck School of Medicine of the University of Southern California, Los Angeles, CA 90007, USA
| | - Peter A Calabresi
- Departments of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA 94158, USA
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, OH 44195, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR 97239, USA
| | - Nancy L Sicotte
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Martina Absinta
- Departments of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Translational Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
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Milovic C, Tejos C, Silva J, Shmueli K, Irarrazaval P. XSIM: A structural similarity index measure optimized for MRI QSM. Magn Reson Med 2024. [PMID: 39176438 DOI: 10.1002/mrm.30271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 07/03/2024] [Accepted: 08/06/2024] [Indexed: 08/24/2024]
Abstract
PURPOSE The structural similarity index measure (SSIM) has become a popular quality metric to evaluate QSM in a way that is closer to human perception than RMS error (RMSE). However, SSIM may overpenalize errors in diamagnetic tissues and underpenalize them in paramagnetic tissues, resulting in biasing. In addition, extreme artifacts may compress the dynamic range, resulting in unrealistically high SSIM scores (hacking). To overcome biasing and hacking, we propose XSIM: SSIM implemented in the native QSM range, and with internal parameters optimized for QSM. METHODS We used forward simulations from a COSMOS ground-truth brain susceptibility map included in the 2016 QSM Reconstruction Challenge to investigate the effect of QSM reconstruction errors on the SSIM, XSIM, and RMSE metrics. We also used these metrics to optimize QSM reconstructions of the in vivo challenge data set. We repeated this experiment with the QSM abdominal phantom. To validate the use of XSIM instead of SSIM for QSM quality assessment across a range of different reconstruction techniques/algorithms, we analyzed the reconstructions submitted to the 2019 QSM Reconstruction Challenge 2.0. RESULTS Our experiments confirmed the biasing and hacking effects on the SSIM metric applied to QSM. The XSIM metric was robust to those effects, penalizing the presence of streaking artifacts and reconstruction errors. Using XSIM to optimize QSM reconstruction regularization weights returned less overregularization than SSIM and RMSE. CONCLUSION XSIM is recommended over traditional SSIM to evaluate QSM reconstructions against a known ground truth, as it avoids biasing and hacking effects and provides a larger dynamic range of scores.
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Affiliation(s)
- Carlos Milovic
- School of Electrical Engineering, Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Cristian Tejos
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile
| | - Javier Silva
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pablo Irarrazaval
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile
- Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
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Stirnberg R, Deistung A, Reichenbach JR, Breteler MMB, Stöcker T. Rapid submillimeter QSM and R 2* mapping using interleaved multishot 3D-EPI at 7 and 3 Tesla. Magn Reson Med 2024. [PMID: 38988040 DOI: 10.1002/mrm.30216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 06/05/2024] [Accepted: 06/24/2024] [Indexed: 07/12/2024]
Abstract
PURPOSE To explore the high signal-to-noise ratio (SNR) efficiency of interleaved multishot 3D-EPI with standard image reconstruction for fast and robust high-resolution whole-brain quantitative susceptibility (QSM) andR 2 ∗ $$ {R}_2^{\ast } $$ mapping at 7 and 3T. METHODS Single- and multi-TE segmented 3D-EPI is combined with conventional CAIPIRINHA undersampling for up to 72-fold effective gradient echo (GRE) imaging acceleration. Across multiple averages, scan parameters are varied (e.g., dual-polarity frequency-encoding) to additionally correct forB 0 $$ {\mathrm{B}}_0 $$ -induced artifacts, geometric distortions and motion retrospectively. A comparison to established GRE protocols is made. Resolutions range from 1.4 mm isotropic (1 multi-TE average in 36 s) up to 0.4 mm isotropic (2 single-TE averages in approximately 6 min) with whole-head coverage. RESULTS Only 1-4 averages are needed for sufficient SNR with 3D-EPI, depending on resolution and field strength. Fast scanning and small voxels together with retrospective corrections result in substantially reduced image artifacts, which improves susceptibility andR 2 ∗ $$ {R}_2^{\ast } $$ mapping. Additionally, much finer details are obtained in susceptibility-weighted image projections through significantly reduced partial voluming. CONCLUSION Using interleaved multishot 3D-EPI, single-TE and multi-TE data can readily be acquired 10 times faster than with conventional, accelerated GRE imaging. Even 0.4 mm isotropic whole-head QSM within 6 min becomes feasible at 7T. At 3T, motion-robust 0.8 mm isotropic whole-brain QSM andR 2 ∗ $$ {R}_2^{\ast } $$ mapping with no apparent distortion in less than 7 min becomes clinically feasible. Stronger gradient systems may allow for even higher effective acceleration rates through larger EPI factors while maintaining optimal contrast.
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Affiliation(s)
- Rüdiger Stirnberg
- MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Andreas Deistung
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), University Medicine Halle, Halle (Saale), Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Faculty of Medicine, Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University of Bonn, Bonn, Germany
| | - Tony Stöcker
- MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Physics and Astronomy, University of Bonn, Bonn, Germany
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Gkotsoulias DG, Jäger C, Müller R, Gräßle T, Olofsson KM, Møller T, Unwin S, Crockford C, Wittig RM, Bilgic B, Möller HE. Chaos and COSMOS-Considerations on QSM methods with multiple and single orientations and effects from local anisotropy. Magn Reson Imaging 2024; 110:104-111. [PMID: 38631534 DOI: 10.1016/j.mri.2024.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/07/2024] [Accepted: 04/14/2024] [Indexed: 04/19/2024]
Abstract
PURPOSE Field-to-susceptibility inversion in quantitative susceptibility mapping (QSM) is ill-posed and needs numerical stabilization through either regularization or oversampling by acquiring data at three or more object orientations. Calculation Of Susceptibility through Multiple Orientations Sampling (COSMOS) is an established oversampling approach and regarded as QSM gold standard. It achieves a well-conditioned inverse problem, requiring rotations by 0°, 60° and 120° in the yz-plane. However, this is impractical in vivo, where head rotations are typically restricted to a range of ±25°. Non-ideal sampling degrades the conditioning with residual streaking artifacts whose mitigation needs further regularization. Moreover, susceptibility anisotropy in white matter is not considered in the COSMOS model, which may introduce additional bias. The current work presents a thorough investigation of these effects in primate brain. METHODS Gradient-recalled echo (GRE) data of an entire fixed chimpanzee brain were acquired at 7 T (350 μm resolution, 10 orientations) including ideal COSMOS sampling and realistic rotations in vivo. Comparisons of the results included ideal COSMOS, in-vivo feasible acquisitions with 3-8 orientations and single-orientation iLSQR QSM. RESULTS In-vivo feasible and optimal COSMOS yielded high-quality susceptibility maps with increased SNR resulting from averaging multiple acquisitions. COSMOS reconstructions from non-ideal rotations about a single axis required additional L2-regularization to mitigate residual streaking artifacts. CONCLUSION In view of unconsidered anisotropy effects, added complexity of the reconstruction, and the general challenge of multi-orientation acquisitions, advantages of sub-optimal COSMOS schemes over regularized single-orientation QSM appear limited in in-vivo settings.
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Affiliation(s)
- Dimitrios G Gkotsoulias
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Carsten Jäger
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland Müller
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tobias Gräßle
- Epidemiology of Highly Pathogenic Microorganisms, Robert Koch-Institute, Berlin, Germany
| | | | | | - Steve Unwin
- Wildlife Health Australia, Canberra, Australia
| | - Catherine Crockford
- Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany; The Ape Social Mind Lab, Institut des Sciences Cognitives Marc Jeannerod, Bron, France; Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
| | - Roman M Wittig
- Department of Human Behavior, Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany; The Ape Social Mind Lab, Institut des Sciences Cognitives Marc Jeannerod, Bron, France; Taï Chimpanzee Project, Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
| | - Harald E Möller
- Nuclear Magnetic Resonance Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Taleb S, Varela-Mattatall G, Allen A, Haast R, Khan AR, Kalia V, Howard JL, MacDonald SJ, Menon RS, Lanting BA, Teeter MG. Assessing brain integrity in patients with long-term and well-functioning metal-based hip implants. J Orthop Res 2024; 42:1292-1302. [PMID: 38235918 DOI: 10.1002/jor.25785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/08/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024]
Abstract
Production of metal debris from implant wear and corrosion processes is now a well understood occurrence following hip arthroplasty. Evidence has shown that metal ions can enter the bloodstream and travel to distant organs including the brain, and in extreme cases, can induce sensorial and neurological diseases. Our objective was tosimultaneously analyze brain anatomy and physiology in patients with long-term and well-functioning implants. Included were subjects who had received total hip or hip resurfacing arthroplastywith an implantation time of a minimum of 7 years (n = 28) and age- and sex-matched controls (n = 32). Blood samples were obtained to measure ion concentrations of cobalt and chromium, and the Montreal Cognitive Assessment was performed. 3T MRI brain scans were completed with an MPRAGE sequence for ROI segmentation and multiecho gradient echo sequences to generate QSM and R2* maps. Mean QSM and R2* values were recorded for five deep brain and four middle and cortical brain structures on both hemispheres: pallidum, putamen, caudate, amygdala, hippocampus, anterior cingulate, inferior temporal, and cerebellum. No differences in QSM or R2* or cognition scores were found between both groups (p > 0.6654). No correlation was found between susceptibility and blood ion levels for cobalt or chromium in any region of the brain. No correlation was found between blood ion levels and cognition scores. Clinical significance: Results suggest that metal ions released by long-term and well-functioning implants do not affect brain integrity.
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Affiliation(s)
- Shahnaz Taleb
- Schulich School of Medicine & Dentistry, Imaging Group, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Gabriel Varela-Mattatall
- Schulich School of Medicine & Dentistry, Imaging Group, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Abbigail Allen
- Department of Surgery, London Health Sciences Centre, Division of Orthopaedic Surgery, London, Ontario, Canada
| | - Roy Haast
- Schulich School of Medicine & Dentistry, Imaging Group, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Ali R Khan
- Schulich School of Medicine & Dentistry, Imaging Group, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Vishal Kalia
- Department of Medical Imaging, Schulich School of Medicine & Dentistry, Division of Musculoskeletal Imaging, Western University, London, Ontario, Canada
| | - James L Howard
- Department of Surgery, London Health Sciences Centre, Division of Orthopaedic Surgery, London, Ontario, Canada
| | - Steven J MacDonald
- Department of Surgery, London Health Sciences Centre, Division of Orthopaedic Surgery, London, Ontario, Canada
| | - Ravi S Menon
- Schulich School of Medicine & Dentistry, Imaging Group, Robarts Research Institute, Western University, London, Ontario, Canada
| | - Brent A Lanting
- Department of Surgery, London Health Sciences Centre, Division of Orthopaedic Surgery, London, Ontario, Canada
| | - Matthew G Teeter
- Schulich School of Medicine & Dentistry, Imaging Group, Robarts Research Institute, Western University, London, Ontario, Canada
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson SD, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91:1834-1862. [PMID: 38247051 PMCID: PMC10950544 DOI: 10.1002/mrm.30006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, New York, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, New York, USA
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Ishimaru H, Ikebe Y, Izumo T, Imai H, Morikawa M, Ideguchi R, Ishiyama A, Koike H, Uetani M, Toya R. Assessment for Carotid Atherosclerotic Plaque Using Vessel Wall Magnetic Resonance Imaging: A Multireader ROC Study to Determine Optimal Sequence for Detecting Vessel Wall Calcification. J Vasc Res 2024; 61:122-128. [PMID: 38547846 DOI: 10.1159/000538175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/27/2024] [Indexed: 06/05/2024] Open
Abstract
INTRODUCTION We aimed to compare conventional vessel wall MR imaging techniques and quantitative susceptibility mapping (QSM) to determine the optimal sequence for detecting carotid artery calcification. METHODS Twenty-two patients who underwent carotid vessel wall MR imaging and neck CT were enrolled. Four slices of 6-mm sections from the bilateral internal carotid bifurcation were subdivided into 4 segments according to clock position (0-3, 3-6, 6-9, and 9-12) and assessed for calcification. Two blinded radiologists independently reviewed a total of 704 segments and scored the likelihood of calcification using a 5-point scale on spin-echo imaging, FLASH, and QSM. The observer performance for detecting calcification was evaluated by a multireader, multiple-case receiver operating characteristic study. Weighted κ statistics were calculated to assess interobserver agreement. RESULTS QSM had a mean area under the receiver operating characteristic curve of 0.85, which was significantly higher than that of any other sequence (p < 0.01) and showed substantial interreader agreement (κ = 0.68). A segment with a score of 3-5 was defined as positive, and a segment with a score of 1-2 was defined as negative; the sensitivity and specificity of QSM were 0.75 and 0.87, respectively. CONCLUSION QSM was the most reliable MR sequence for the detection of plaque calcification.
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Affiliation(s)
- Hideki Ishimaru
- Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Yohei Ikebe
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Tsuyoshi Izumo
- Department of Neurolosurgery, Nagasaki University Hospital, Nagasaki, Japan
| | - Hiroshi Imai
- MR Research and Collaboration, Siemens Healthcare K.K, Osaki, Shinagawa, Tokyo, Japan
| | - Minoru Morikawa
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Reiko Ideguchi
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Ayano Ishiyama
- Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Hirofumi Koike
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Masataka Uetani
- Department of Radiology, Nagasaki University Hospital, Nagasaki, Japan
| | - Ryo Toya
- Department of Radiological Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
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Graf S, Wohlgemuth WA, Deistung A. Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution. Front Neurosci 2024; 18:1366165. [PMID: 38529264 PMCID: PMC10962327 DOI: 10.3389/fnins.2024.1366165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/20/2024] [Indexed: 03/27/2024] Open
Abstract
Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-to-susceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural networks with our approach, adaptive convolution, that learns the mapping between the additional presented information (acquisition parameters) and the changes in the phase images associated with these varying acquisition parameters. By associating a-priori information with the network parameters itself, the optimal set of convolution weights is selected based on data-specific attributes, leading to generalizability towards changes in acquisition parameters. Moreover, we demonstrate the feasibility of pre-training on synthetic data and transfer learning to clinical brain data to achieve substantial improvements in the computation of susceptibility maps. The adaptive convolution 3D U-Net demonstrated generalizability in acquisition parameters on synthetic and in-vivo data and outperformed models lacking adaptive convolution or transfer learning. Further experiments demonstrate the impact of the side information on the adaptive model and assessed susceptibility map computation on simulated pathologic data sets and measured phase data.
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Affiliation(s)
- Simon Graf
- University Clinic and Polyclinic for Radiology, University Hospital Halle (Saale), Halle, Germany
- Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Walter A. Wohlgemuth
- University Clinic and Polyclinic for Radiology, University Hospital Halle (Saale), Halle, Germany
- Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Andreas Deistung
- University Clinic and Polyclinic for Radiology, University Hospital Halle (Saale), Halle, Germany
- Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Halle, Germany
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Khormi I, Al-Iedani O, Alshehri A, Ramadan S, Lechner-Scott J. MR myelin imaging in multiple sclerosis: A scoping review. J Neurol Sci 2023; 455:122807. [PMID: 38035651 DOI: 10.1016/j.jns.2023.122807] [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: 07/24/2023] [Revised: 10/20/2023] [Accepted: 11/19/2023] [Indexed: 12/02/2023]
Abstract
The inability of disease-modifying therapies to stop the progression of multiple sclerosis (MS), has led to the development of a new therapeutic strategy focussing on myelin repair. While conventional MRI lacks sensitivity for quantifying myelin damage, advanced MRI techniques are proving effective. The development of targeted therapeutics requires histological validation of myelin imaging results, alongside the crucial task of establishing correlations between myelin imaging results and clinical assessments, so that the effectiveness of therapeutic interventions can be evaluated. The aims of this scoping review were to identify myelin imaging methods - some of which have been histologically validated, and to determine how these approaches correlate with clinical assessments of people with MS (pwMS), thus allowing for effective therapeutic evaluation. A search of two databases was undertaken for publications relating to studies on adults MS using either MRI/MR-histology of the MS brain in the range 1990-to-2022. The myelin imaging methods specified were relaxometry, magnetization transfer, and quantitative susceptibility. Relaxometry was used most frequently, with myelin water fraction (MWF) being the primary metric. Studies conducted on tissue from various regions of the brain showed that MWF was significantly lower in pwMS than in healthy controls. Magnetization transfer ratio indicated that the macromolecular content of lesions was lower than that of normal-appearing tissue. Higher magnetic susceptibility of lesions were indicative of myelin breakdown and iron accumulation. Several myelin imaging metrics were correlated with disability, disease severity and duration. Many studies showed a good correlation between myelin measured histologically and by MR myelin imaging techniques.
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Affiliation(s)
- Ibrahim Khormi
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia; College of Applied Medical Sciences, University of Jeddah, Jeddah, Saudi Arabia
| | - Oun Al-Iedani
- Hunter Medical Research Institute, New Lambton Heights, Australia; School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Abdulaziz Alshehri
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia; Department of Radiology, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Saadallah Ramadan
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia.
| | - Jeannette Lechner-Scott
- Hunter Medical Research Institute, New Lambton Heights, Australia; Department of Neurology, John Hunter Hospital, New Lambton Heights, Australia; School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
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10
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Huang S, Lah JJ, Allen JW, Qiu D. Robust quantitative susceptibility mapping via approximate message passing with parameter estimation. Magn Reson Med 2023; 90:1414-1430. [PMID: 37249040 PMCID: PMC10664815 DOI: 10.1002/mrm.29722] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 04/25/2023] [Accepted: 05/14/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps. THEORY From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modeled by the Laplace distribution. The measurement noise is modeled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP). METHODS We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI, and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at: https://github.com/EmoryCN2L/QSM_AMP_PE. RESULTS On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, deviation from calcification moment and the highest SSIM, while MEDI achieved the lowest high-frequency error norm. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters. CONCLUSION AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.
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Affiliation(s)
- Shuai Huang
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, USA
| | - James J. Lah
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - Jason W. Allen
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, USA
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, USA
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11
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Yu JJ, Li C, Qian ZM, Liu Y. Brain iron deposition is positively correlated with cognitive impairment in patients with chronic cerebral hypoperfusion: a MRI susceptibility mapping study. Clin Radiol 2023; 78:601-607. [PMID: 37003892 DOI: 10.1016/j.crad.2023.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 02/14/2023] [Accepted: 02/27/2023] [Indexed: 04/03/2023]
Abstract
AIM To investigate the relationship of brain iron deposition with cognitive impairment in patients with chronic cerebral hypoperfusion (CHP). MATERIALS AND METHODS Brain iron deposition was detected using quantitative susceptibility mapping (QSM), and cognitive function by neuropsychological tests including the Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADLs), and verbal fluency tests in a total of 40 participants, 23 with CHP and 17 age- and sex-matched healthy participants without CHP (controls). RESULTS The neuropsychological tests revealed that cognitive impairment (p<0.05) and susceptibility values (p<0.05) of the bilateral hippocampus (HP) and substantia nigra (SN) in CHP patients were significantly higher than those of the controls. The susceptibility values of bilateral HP and left putamen correlated closely with the scores of neuropsychological tests in the CHP patients (p<0.05, r2>0.1). The susceptibility values in the left putamen and bilateral HP were significantly higher in CHP patients with mild cognitive impairment (MCI; n=8) than those of CHP patients without MCI (n=15; p<0.05). CONCLUSIONS The present findings indicated that brain iron deposition in specific areas may be responsible for the cognitive impairment in CHP patients, and that QSM is a useful tool to determine brain iron, predicting cognitive impairment in CHP patients.
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Affiliation(s)
- J-J Yu
- Department of Pain and Rehabilitation, The Second Affiliated (Xinqiao) Hospital, The Army (Third Military) Medical University, Chongqing 400038, China
| | - C Li
- Department of Medical Imaging, Chongqing University Central Hospital, Chongqing, China
| | - Z-M Qian
- Institute of Translational & Precision Medicine, Nantong University, 19 Qi Xiu Road, Nantong, JS 226019, China.
| | - Y Liu
- Department of Pain and Rehabilitation, The Second Affiliated (Xinqiao) Hospital, The Army (Third Military) Medical University, Chongqing 400038, China.
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12
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Reeves JA, Weinstock Z, Zivadinov R, Dwyer MG, Bergsland N, Salman F, Schweser F, Weinstock-Guttman B, Benedict RH, Jakimovski D. Paramagnetic rim lesions are associated with greater incidence of relapse and worse cognitive recovery following relapse. Mult Scler 2023; 29:1033-1038. [PMID: 37161349 PMCID: PMC10523891 DOI: 10.1177/13524585231169466] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
BACKGROUND Paramagnetic rim lesions (PRL) may be linked to relapse risk of people with relapsing-remitting multiple sclerosis (pwRRMS). OBJECTIVE To determine the relationship between presence of PRL lesions and cognitive recovery after relapse. METHODS PRL load was compared between acutely relapsing pwRRMS and matched stable pwRRMS controls (each group n = 21). In addition, cognitive recovery was compared between acutely relapsing pwRRMS with at least one PRL (PRL+) and those without any PRL (PRL-). RESULTS Acutely relapsing pwRRMS had significantly greater prevalence and number of PRL (p = 0.004 and p = 0.003) compared with stable controls. These findings remained significant after adjusting for global neuroinflammatory burden (enhancing and non-enhancing lesions). In addition, acutely relapsing PRL + pwRRMS (n = 10) had worse recovery of verbal memory following relapse compared with acutely relapsing PRL - pwRRMS (n = 7; p = 0.027). CONCLUSION These findings may partially explain previously suggested associations between presence of PRL with more severe disease course.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Zachary Weinstock
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 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, State University of New York, Buffalo, NY, USA/Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, 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, State University of New York, Buffalo, NY, USA/Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, 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, State University of New York, Buffalo, NY, USA/IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA/Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Ralph Hb Benedict
- Department of Neurology, Jacobs Comprehensive MS Treatment and Research Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
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13
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Li Z, Feng R, Liu Q, Feng J, Lao G, Zhang M, Li J, Zhang Y, Wei H. APART-QSM: an improved sub-voxel quantitative susceptibility mapping for susceptibility source separation using an iterative data fitting method. Neuroimage 2023; 274:120148. [PMID: 37127191 DOI: 10.1016/j.neuroimage.2023.120148] [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: 11/02/2022] [Revised: 02/06/2023] [Accepted: 04/28/2023] [Indexed: 05/03/2023] Open
Abstract
The brain tissue phase contrast in MRI sequences reflects the spatial distributions of multiple substances, such as iron, myelin, calcium, and proteins. These substances with paramagnetic and diamagnetic susceptibilities often colocalize in one voxel in brain regions. Both opposing susceptibilities play vital roles in brain development and neurodegenerative diseases. Conventional QSM methods only provide voxel-averaged susceptibility value and cannot disentangle intravoxel susceptibilities with opposite signs. Advanced susceptibility imaging methods have been recently developed to distinguish the contributions of opposing susceptibility sources for QSM. The basic concept of separating paramagnetic and diamagnetic susceptibility proportions is to include the relaxation rate R2* with R2' in QSM. The magnitude decay kernel, describing the proportionality coefficient between R2' and susceptibility, is an essential reconstruction coefficient for QSM separation methods. In this study, we proposed a more comprehensive complex signal model that describes the relationship between 3D GRE signal and the contributions of paramagnetic and diamagnetic susceptibility to the frequency shift and R2* relaxation. The algorithm is implemented as a constrained minimization problem in which the voxel-wise magnitude decay kernel and sub-voxel susceptibilities are determined alternately in each iteration until convergence. The calculated voxel-wise magnitude decay kernel could realistically model the relationship between the R2' relaxation and the volume susceptibility. Thus, the proposed method effectively prevents the errors of the magnitude decay kernel from propagating to the final susceptibility separation reconstruction. Phantom studies, ex vivo macaque brain experiments, and in vivo human brain imaging studies were conducted to evaluate the ability of the proposed method to distinguish paramagnetic and diamagnetic susceptibility sources. The results demonstrate that the proposed method provides state-of-the-art performances for quantifying brain iron and myelin compared to previous QSM separation methods. Our results show that the proposed method has the potential to simultaneously quantify whole brain iron and myelin during brain development and aging. The proposed model was also deployed with multiple-orientation complex GRE data input measurements, resulting in high-quality QSM separation maps with more faithful tissue delineation between brain structures compared to those reconstructed by single-orientation QSM separation methods.
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Affiliation(s)
- Zhenghao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiangqiang Liu
- Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Guoyan Lao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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14
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Meng Y, Li CX, Zhang X. Quantitative Evaluation of Oxygen Extraction Fraction Changes in the Monkey Brain during Acute Stroke by Using Quantitative Susceptibility Mapping. Life (Basel) 2023; 13:1008. [PMID: 37109537 PMCID: PMC10146121 DOI: 10.3390/life13041008] [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: 03/02/2023] [Revised: 04/05/2023] [Accepted: 04/10/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND The oxygen extraction fraction (OEF) indicates the brain's oxygen consumption and can be estimated by using the quantitative susceptibility mapping (QSM) MRI technique. Recent studies have suggested that OEF alteration following stroke is associated with the viability of at-risk tissue. In the present study, the temporal evolution of OEF in the monkey brain during acute stroke was investigated using QSM. METHODS Ischemic stroke was induced in adult rhesus monkeys (n = 8) with permanent middle cerebral artery occlusion (pMCAO) by using an interventional approach. Diffusion-, T2-, and T2*-weighted images were conducted on day 0, day 2, and day 4 post-stroke using a clinical 3T scanner. Progressive changes in magnetic susceptibility and OEF, along with their correlations with the transverse relaxation rates and diffusion indices, were examined. RESULTS The magnetic susceptibility and OEF in injured gray matter of the brain significantly increased during the hyperacute phase, and then decreased significantly on day 2 and day 4. Moreover, the temporal changes of OEF in gray matter were moderately correlated with mean diffusivity (MD) (r = 0.52; p = 0.046) from day 0 to day 4. Magnetic susceptibility in white matter progressively increased (from negative values to near zero) during acute stroke, and significant increases were seen on day 2 (p = 0.08) and day 4 (p = 0.003) when white matter was significantly degenerated. However, significant reduction of OEF in white matter was not seen until day 4 post-stroke. CONCLUSION The preliminary results demonstrate that QSM-derived OEF is a robust approach to examine the progressive changes of gray matter in the ischemic brain from the hyperacute phase to the subacute phase of stroke. The changes of OEF in gray matter were more prominent than those in white matter following stroke insult. The findings suggest that QSM-derived OEF may provide complementary information for understanding the neuropathology of the brain tissue following stroke and predicting stroke outcomes.
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Affiliation(s)
- Yuguang Meng
- EPC Imaging Center, Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - Chun-Xia Li
- EPC Imaging Center, Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - Xiaodong Zhang
- EPC Imaging Center, Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
- Division of Neuropharmacology and Neurologic Diseases, Emory National Primate Research Center, Emory University, Atlanta, GA 30329, USA
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15
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Chen Z, Zhai X, Chen Z. Tilted quantitative susceptibility mapping at oblique MRI (tiltQSM). Comput Biol Med 2023; 157:106802. [PMID: 36965324 DOI: 10.1016/j.compbiomed.2023.106802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 03/05/2023] [Accepted: 03/20/2023] [Indexed: 03/27/2023]
Abstract
OBJECTIVE If the phase image matrix was acquired from oblique MRI, it is needed to deal with the oblique effect for quantitative susceptibility mapping (QSM), as addressed in this paper. METHODS We proposed two methods for QSM reconstruction from slice-tilted MRI phase image (tiltQSM): 1) rotData per anti-tilting phase image rotation back into the B0-upright system, and 2) rotKernel per pro-tilting dipole kernel rotation into the same oblique setting as defined by the tilted phase image. Both matrix methods were implemented in an additional preprocessing subroutine to ensure that the phase image and the dipole kernel were represented in the same coordinate system (either in B0-upright system or in B0-tilted system); thereafter tiltQSM could be completed through a regular QSM procedure. Besides the oblique effect, tiltQSM also suffers from MRI anisotropy. We provided numeric simulations, phantom tests and in vivo brain experiments on tiltQSM with oblique MRI (axial slice tilting at 3T). RESULTS The tiltQSM reconstruction could attain a performance corr > 0.90 (spatial correlation conformance) for small tilting angles <10°. The tiltQSM performance could be further degraded by voxel anisotropy due to image matrix rotation (digital geometry error). CONCLUSIONS To seek inverse solutions of MRI phase images acquired at oblique MRI (e.g. in axial slice tilting), we proposed tiltQSM to deal with the oblique effect per matrix rotation (either rotData or rotKernel) in a preprocessing subroutine prior to a regular QSM procedure. In practice, it is always recommended to acquire MRI phase images in isotropic matrix at zero obliqueness (or limited to small tilting angles <10°) for maximal (optimal) QSM reconstruction.
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Affiliation(s)
- Zeyuan Chen
- Department of Computer Sciences, University of California-Davis, Davis, CA, USA; Microsoft Corporation, Seattle, WA, USA
| | | | - Zikuan Chen
- Zinv LLC, Albuquerque, NM, USA; Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA.
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16
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Kames C, Doucette J, Rauscher A. Multi-echo dipole inversion for magnetic susceptibility mapping. Magn Reson Med 2023; 89:2391-2401. [PMID: 36695283 DOI: 10.1002/mrm.29588] [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: 10/04/2022] [Revised: 12/08/2022] [Accepted: 12/31/2022] [Indexed: 01/26/2023]
Abstract
PURPOSE Reconstructing tissue magnetic susceptibility (QSM) from MRI phase data involves solving multiple consecutive ill-posed inverse problems such as phase unwrapping, background field removal, and field-to-source inversion. Multi-echo acquisitions present an additional challenge, as the magnetization field is typically computed from the multiple phase data prior to reconstructing the susceptibility map. Processing the multiple phase data introduces errors during the field estimation, violating assumptions of the subsequent inverse problems, manifesting as streaking artifacts in the susceptibility map. To address this challenge, we propose a multi-echo field-to-source forward model that forgoes the field estimation step. Moreover, we propose a fully general underestimation correction step to recover susceptibility sources that were regularized away during the field-to-source inversion. METHODS The multi-echo forward model and correction step were validated on the QSM Challenge 2.0 datasets and compared to the standard single field-to-source model in in vivo human brains using different types of deconvolution algorithms. RESULTS On the QSM Challenge 2.0 datasets the multi-echo forward model and correction step attain state-of-the-art results on all metrics by a wide margin. Experiments in in vivo brains show that the multi-echo model is in agreement with the single field-to-source model and that the proposed forward model and correction step can be used with any available dipole inversion method. CONCLUSION A multi-echo field-to-source forward model forgoes the need to fit multi-echo phase data and achieves state-of-the-art results on the QSM Challenge 2.0 data. Underestimated low-frequency susceptibility distributions can be partially recovered using a correction step.
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Affiliation(s)
- Christian Kames
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jonathan Doucette
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander Rauscher
- UBC MRI Research Centre, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pediatrics, The University of British Columbia, Vancouver, British Columbia, Canada
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17
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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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18
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Kamesh Iyer S, Moon BF, Josselyn N, Kurtz RM, Song JW, Ware JB, Nabavizadeh SA, Witschey WR. Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers. Sci Rep 2022; 12:21679. [PMID: 36522372 PMCID: PMC9755132 DOI: 10.1038/s41598-022-22778-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 10/19/2022] [Indexed: 12/23/2022] Open
Abstract
Quantitative susceptibility mapping employs regularization to reduce artifacts, yet many recent denoisers are unavailable for reconstruction. We developed a plug-and-play approach to QSM reconstruction (PnP QSM) and show its flexibility using several patch-based denoisers. We developed PnP QSM using alternating direction method of multiplier framework and applied collaborative filtering denoisers. We apply the technique to the 2016 QSM Challenge and in 10 glioblastoma multiforme datasets. We compared its performance with four published QSM techniques and a multi-orientation QSM method. We analyzed magnetic susceptibility accuracy using brain region-of-interest measurements, and image quality using global error metrics. Reconstructions on glioblastoma data were analyzed using ranked and semiquantitative image grading by three neuroradiologist observers to assess image quality (IQ) and sharpness (IS). PnP-BM4D QSM showed good correlation (β = 0.84, R2 = 0.98, p < 0.05) with COSMOS and no significant bias (bias = 0.007 ± 0.012). PnP-BM4D QSM achieved excellent quality when assessed using structural similarity index metric (SSIM = 0.860), high frequency error norm (HFEN = 58.5), cross correlation (CC = 0.804), and mutual information (MI = 0.475) and also maintained good conspicuity of fine features. In glioblastoma datasets, PnP-BM4D QSM showed higher performance (IQGrade = 2.4 ± 0.4, ISGrade = 2.7 ± 0.3, IQRank = 3.7 ± 0.3, ISRank = 3.9 ± 0.3) compared to MEDI (IQGrade = 2.1 ± 0.5, ISGrade = 2.1 ± 0.6, IQRank = 2.4 ± 0.6, ISRank = 2.9 ± 0.2) and FANSI-TGV (IQGrade = 2.2 ± 0.6, ISGrade = 2.1 ± 0.6, IQRank = 2.7 ± 0.3, ISRank = 2.2 ± 0.2). We illustrated the modularity of PnP QSM by interchanging two additional patch-based denoisers. PnP QSM reconstruction was feasible, and its flexibility was shown using several patch-based denoisers. This technique may allow rapid prototyping and validation of new denoisers for QSM reconstruction for an array of useful clinical applications.
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Affiliation(s)
- Srikant Kamesh Iyer
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
- Perelman Center for Advanced Medicine, South Pavilion, Rm 11-155, Philadelphia, PA, USA.
| | - Brianna F Moon
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas Josselyn
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert M Kurtz
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jae W Song
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey B Ware
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - S Ali Nabavizadeh
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Walter R Witschey
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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19
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Reeves JA, Bergsland N, Dwyer MG, Wilding GE, Jakimovski D, Salman F, Sule B, Meineke N, Weinstock-Guttman B, Zivadinov R, Schweser F. Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis. Neuroimage 2022; 261:119503. [PMID: 35878723 PMCID: PMC10097440 DOI: 10.1016/j.neuroimage.2022.119503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/06/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open
Abstract
Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels. We applied ICA to quantitative susceptibility maps for 170 individuals aged 9-81 years without neurological disease ("Healthy Aging" (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the "MS/HC" cohort). Two DGM-associated "susceptibility networks" identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. "robust") across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects. These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.
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Affiliation(s)
- Jack A Reeves
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; MR Research Laboratory, IRCCS, Don Gnocchi Foundation ONLUS, Milan, Italy
| | - Michael G Dwyer
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Gregory E Wilding
- Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - Dejan Jakimovski
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Fahad Salman
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Balint Sule
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Nicklas Meineke
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Bianca Weinstock-Guttman
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; Jacobs Neurological Institute, Buffalo, NY, USA
| | - Robert Zivadinov
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Center for Biomedical Imaging, Clinical and Translational Science Institute, Clinical and Translational Research Center, State University of New York at Buffalo, 6045C, 875 Ellicott Street, Buffalo, NY 14203, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA.
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20
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Ni MH, Li ZY, Sun Q, Yu Y, Yang Y, Hu B, Ma T, Xie H, Li SN, Tao LQ, Yuan DX, Zhu JL, Yan LF, Cui GB. Neurovascular decoupling measured with quantitative susceptibility mapping is associated with cognitive decline in patients with type 2 diabetes. Cereb Cortex 2022; 33:5336-5346. [PMID: 36310091 DOI: 10.1093/cercor/bhac422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 01/10/2023] Open
Abstract
Abstract
Disturbance of neurovascular coupling (NVC) is suggested to be one potential mechanism in type 2 diabetes mellitus (T2DM) associated mild cognitive impairment (MCI). However, NVC evidence derived from functional magnetic resonance imaging ignores the relationship of neuronal activity with vascular injury. Twenty-seven T2DM patients without MCI and thirty healthy controls were prospectively enrolled. Brain regions with changed susceptibility detected by quantitative susceptibility mapping (QSM) were used as seeds for functional connectivity (FC) analysis. NVC coefficients were estimated using combined degree centrality (DC) with susceptibility or cerebral blood flow (CBF). Partial correlations between neuroimaging indicators and cognitive decline were investigated. In T2DM group, higher susceptibility values in right hippocampal gyrus (R.PHG) were found and were negatively correlated with Naming Ability of Montreal Cognitive Assessment. FC increased remarkably between R.PHG and right middle temporal gyrus (R.MTG), right calcarine gyrus (R.CAL). Both NVC coefficients (DC-QSM and DC-CBF) reduced in R.PHG and increased in R.MTG and R.CAL. Both NVC coefficients in R.PHG and R.MTG increased with the improvement of cognitive ability, especially for executive function. These demonstrated that QSM and DC-QSM coefficients can be promising biomarkers for early evaluation of cognitive decline in T2DM patients and help to better understand the mechanism of NVC.
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Affiliation(s)
- Min-Hua Ni
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
- Faculty of Medical Technology, Shaanxi University of Chinese Medicine , 1 Middle Section of Shiji Road, Xian yang, Shaanxi 712046 , China
| | - Ze-Yang Li
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Qian Sun
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Ying Yu
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Yang Yang
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Bo Hu
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Teng Ma
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Hao Xie
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Si-Ning Li
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
- Faculty of Medical Technology, Xi’an Medical University , 1 Xinwang Road, Xi'an, Shaanxi 710016 , China
| | - Lan-Qiu Tao
- Student Brigade, Fourth Military Medical University , 169 Changle Road, Xi'an, Shaanxi 710032 , China
| | - Ding-Xin Yuan
- Student Brigade, Fourth Military Medical University , 169 Changle Road, Xi'an, Shaanxi 710032 , China
| | - Jun-Ling Zhu
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Lin-Feng Yan
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
| | - Guang-Bin Cui
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University , 569 Xinsi Road, Xi'an 710038, Shaanxi , China
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21
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Huang S, Lah JJ, Allen JW, Qiu D. A probabilistic Bayesian approach to recover R2*$$ {R}_{2\ast } $$ map and phase images for quantitative susceptibility mapping. Magn Reson Med 2022; 88:1624-1642. [PMID: 35672899 PMCID: PMC10627109 DOI: 10.1002/mrm.29303] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/04/2022] [Accepted: 04/26/2022] [Indexed: 12/29/2022]
Abstract
PURPOSE Undersampling is used to reduce the scan time for high-resolution three-dimensional magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover R 2 ∗ $$ {R}_2^{\ast } $$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data. THEORY Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity-promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono-exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients. METHODS Undersampling takes place in the y-z plane of k-space according to the Poisson-disk pattern. Retrospective undersampling is performed to evaluate the performances of different reconstruction approaches, prospective undersampling is performed to demonstrate the feasibility of undersampling in practice. RESULTS The proposed AMP with parameter estimation (AMP-PE) approach successfully recovers R 2 ∗ $$ {R}_2^{\ast } $$ maps and phase images for QSM across various undersampling rates. It is more computationally efficient, and performs better than the state-of-the-art l 1 $$ {l}_1 $$ -norm regularization (L1) approach in general, except a few cases where the L1 approach performs as well as AMP-PE. CONCLUSION AMP-PE achieves better performance by drawing information from both the sparse prior and the mono-exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground-truth image.
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Affiliation(s)
- Shuai Huang
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, USA
| | - James J. Lah
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - Jason W. Allen
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, USA
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22
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Wood TC, Cash D, MacNicol E, Simmons C, Kim E, Lythgoe DJ, Zelaya F, Turkheimer F. Non-Invasive measurement of the cerebral metabolic rate of oxygen using MRI in rodents. Wellcome Open Res 2022; 6:109. [PMID: 36081865 PMCID: PMC9428501 DOI: 10.12688/wellcomeopenres.16734.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 11/20/2022] Open
Abstract
Malfunctions of oxygen metabolism are suspected to play a key role in a number of neurological and psychiatric disorders, but this hypothesis cannot be properly investigated without an in-vivo non-invasive measurement of brain oxygen consumption. We present a new way to measure the Cerebral Metabolic Rate of Oxygen (CMRO2) by combining two existing magnetic resonance imaging techniques, namely arterial spin-labelling and oxygen extraction fraction mapping. This method was validated by imaging rats under different anaesthetic regimes and was strongly correlated to glucose consumption measured by autoradiography.
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Affiliation(s)
- Tobias C Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Diana Cash
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Camilla Simmons
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Eugene Kim
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
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23
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Marvel CL, Chen L, Joyce MR, Morgan OP, Iannuzzelli KG, LaConte SM, Lisinski JM, Rosenthal LS, Li X. Quantitative susceptibility mapping of basal ganglia iron is associated with cognitive and motor functions that distinguish spinocerebellar ataxia type 6 and type 3. Front Neurosci 2022; 16:919765. [PMID: 36061587 PMCID: PMC9433989 DOI: 10.3389/fnins.2022.919765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background In spinocerebellar ataxia type 3 (SCA3), volume loss has been reported in the basal ganglia, an iron-rich brain region, but iron content has not been examined. Recent studies have reported that patients with SCA6 have markedly decreased iron content in the cerebellar dentate, coupled with severe volume loss. Changing brain iron levels can disrupt cognitive and motor functions, yet this has not been examined in the SCAs, a disease in which iron-rich regions are affected. Methods In the present study, we used quantitative susceptibility mapping (QSM) to measure tissue magnetic susceptibility (indicating iron concentration), structural volume, and normalized susceptibility mass (indicating iron content) in the cerebellar dentate and basal ganglia in people with SCA3 (n = 10) and SCA6 (n = 6) and healthy controls (n = 9). Data were acquired using a 7T Philips MRI scanner. Supplemental measures assessed motor, cognitive, and mood domains. Results Putamen volume was lower in both SCA groups relative to controls, replicating prior findings. Dentate susceptibility mass and volume in SCA6 was lower than in SCA3 or controls, also replicating prior findings. The novel finding was that higher basal ganglia susceptibility mass in SCA6 correlated with lower cognitive performance and greater motor impairment, an association that was not observed in SCA3. Cerebellar dentate susceptibility mass, however, had the opposite relationship with cognition and motor function in SCA6, suggesting that, as dentate iron is depleted, it relocated to the basal ganglia, which contributed to cognitive and motor decline. By contrast, basal ganglia volume loss, rather than iron content, appeared to drive changes in motor function in SCA3. Conclusion The associations of higher basal ganglia iron with lower motor and cognitive function in SCA6 but not in SCA3 suggest the potential for using brain iron deposition profiles beyond the cerebellar dentate to assess disease states within the cerebellar ataxias. Moreover, the role of the basal ganglia deserves greater attention as a contributor to pathologic and phenotypic changes associated with SCA.
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Affiliation(s)
- Cherie L. Marvel
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Michelle R. Joyce
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Owen P. Morgan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Katherine G. Iannuzzelli
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Stephen M. LaConte
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States
- Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States
| | - Jonathan M. Lisinski
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States
| | - Liana S. Rosenthal
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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24
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Aimo A, Huang L, Tyler A, Barison A, Martini N, Saccaro LF, Roujol S, Masci PG. Quantitative susceptibility mapping (QSM) of the cardiovascular system: challenges and perspectives. J Cardiovasc Magn Reson 2022; 24:48. [PMID: 35978351 PMCID: PMC9387036 DOI: 10.1186/s12968-022-00883-z] [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: 04/28/2022] [Accepted: 08/05/2022] [Indexed: 11/10/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a powerful, non-invasive, magnetic resonance imaging (MRI) technique that relies on measurement of magnetic susceptibility. So far, QSM has been employed mostly to study neurological disorders characterized by iron accumulation, such as Parkinson's and Alzheimer's diseases. Nonetheless, QSM allows mapping key indicators of cardiac disease such as blood oxygenation and myocardial iron content. For this reason, the application of QSM offers an unprecedented opportunity to gain a better understanding of the pathophysiological changes associated with cardiovascular disease and to monitor their evolution and response to treatment. Recent studies on cardiovascular QSM have shown the feasibility of a non-invasive assessment of blood oxygenation, myocardial iron content and myocardial fibre orientation, as well as carotid plaque composition. Significant technical challenges remain, the most evident of which are related to cardiac and respiratory motion, blood flow, chemical shift effects and susceptibility artefacts. Significant work is ongoing to overcome these challenges and integrate the QSM technique into clinical practice in the cardiovascular field.
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Affiliation(s)
- Alberto Aimo
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Li Huang
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Tyler
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrea Barison
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | | | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical Engineering, King's College London, St Thomas' Hospital, 4th Floor Lambeth Wing, London, SE1 7EH, UK.
| | - Pier-Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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25
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Rotkopf LT, Buschle LR, Schlemmer HP, Ziener CH. Influence of diffusion on transverse relaxation rates and phases of an ensemble of magnetic spheres. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107259. [PMID: 35779309 DOI: 10.1016/j.jmr.2022.107259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 05/30/2022] [Accepted: 06/19/2022] [Indexed: 06/15/2023]
Abstract
In quantitative susceptibility mapping, the tissue susceptibility is determined from the magnitude and phase of the gradient echo signal, which is influenced by the interplay of complex susceptibility and diffusion effect. Herein, we analytically analyze the influence of diffusion on magnitude and phase images generated by randomly arranged magnetic spheres as a model of intracerebral iron depositions. We demonstrate that both gradient and spin echo relaxation rate constants have a strong and nonlinear dependence on diffusion strength and give empirical formulas for magnitude and phase. This may be used in the future to improve QSM processing methods. In addition, we show that, in theory, combined acquisitions of gradient and spin echo can be used to determine the dimension of the magnetic spheres and the diffusion strength.
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Affiliation(s)
- L T Rotkopf
- Department of Radiology, German Cancer Research Center, Im Neuenheimer Feld 220, 69120 Heidelberg, Germany; Medical Faculty, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - L R Buschle
- Department of Radiology, German Cancer Research Center, Im Neuenheimer Feld 220, 69120 Heidelberg, Germany
| | - H-P Schlemmer
- Department of Radiology, German Cancer Research Center, Im Neuenheimer Feld 220, 69120 Heidelberg, Germany
| | - C H Ziener
- Department of Radiology, German Cancer Research Center, Im Neuenheimer Feld 220, 69120 Heidelberg, Germany.
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26
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Duan X, Xie Y, Zhu X, Chen L, Li F, Feng G, Li L. Quantitative Susceptibility Mapping of Brain Iron Deposition in Patients With Recurrent Depression. Psychiatry Investig 2022; 19:668-675. [PMID: 36059056 PMCID: PMC9441458 DOI: 10.30773/pi.2022.0110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/08/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Recurrence is the most significant feature of depression and the relationship between iron and recurrent depression is still lack of direct evidence in vivo. METHODS Twenty-one patients with depression and twenty control subjects were included. Gradient-recalled echo, T1 and T2 images were acquired using a 3.0T MRI system. After quantitative susceptibility mapping were reconstructed and standardized, a whole-brain and the regions of interest were respectively analyzed. RESULTS Significant increases in susceptibility were found in multiple recurrent depression patients, which involved several brain regions (frontal lobes, temporal lobe structures, occipital lobes hippocampal regions, putamen, thalamus, cingulum, and cerebellum). Interestingly, no susceptibility changes after treatment compared to pre-treatment (all p>0.05) and no significant correlation between susceptibility and Hamilton Depression Rating Scale were found. Besides, it was close to significance that those with a higher relapse frequency or a longer mean duration of single episode had a higher susceptibility in the putamen, thalamus, and hippocampus. Further studies showed susceptibility across the putamen (ρ2=0.27, p<0.001), thalamus (ρ2=0.21, p<0.001), and hippocampus (ρ2=0.19, p<0.001) were strongly correlated with total course of disease onset. CONCLUSION Brain iron deposition is related to the total course of disease onset, but not the severity of depression, which suggest that brain iron deposition may be a sign of brain damage in multiple recurrent depression.
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Affiliation(s)
- Xinxiu Duan
- Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang, China
| | - Yuhang Xie
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Xiufang Zhu
- Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang, China
| | - Lei Chen
- Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang, China
| | - Feng Li
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Guoquan Feng
- Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Lei Li
- Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang, China
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27
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Wood TC, Cash D, MacNicol E, Simmons C, Kim E, Lythgoe DJ, Zelaya F, Turkheimer F. Non-Invasive measurement of the cerebral metabolic rate of oxygen using MRI in rodents. Wellcome Open Res 2022; 6:109. [PMID: 36081865 PMCID: PMC9428501 DOI: 10.12688/wellcomeopenres.16734.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2022] [Indexed: 08/17/2023] Open
Abstract
Malfunctions of oxygen metabolism are suspected to play a key role in a number of neurological and psychiatric disorders, but this hypothesis cannot be properly investigated without an in-vivo non-invasive measurement of brain oxygen consumption. We present a new way to measure the Cerebral Metabolic Rate of Oxygen (CMRO 2) by combining two existing magnetic resonance imaging techniques, namely arterial spin-labelling and oxygen extraction fraction mapping. This method was validated by imaging rats under different anaesthetic regimes and was strongly correlated to glucose consumption measured by autoradiography.
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Affiliation(s)
- Tobias C Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Diana Cash
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Camilla Simmons
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Eugene Kim
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, UK
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28
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Wood TC, Cash D, MacNicol E, Simmons C, Kim E, Lythgoe DJ, Zelaya F, Turkheimer F. Non-Invasive measurement of the cerebral metabolic rate of oxygen using MRI in rodents. Wellcome Open Res 2022; 6:109. [DOI: 10.12688/wellcomeopenres.16734.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2022] [Indexed: 11/20/2022] Open
Abstract
Malfunctions of oxygen metabolism are suspected to play a key role in a number of neurological and psychiatric disorders, but this hypothesis cannot be properly investigated without an in-vivo non-invasive measurement of brain oxygen consumption. We present a new way to measure the Cerebral Metabolic Rate of Oxygen (CMRO2) by combining two existing magnetic resonance imaging techniques, namely arterial spin-labelling and oxygen extraction fraction mapping. This method was validated by imaging rats under different anaesthetic regimes and was strongly correlated to glucose consumption measured by autoradiography.
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Fouquet JP, Sikpa D, Lebel R, Sibgatulin R, Krämer M, Herrmann KH, Deistung A, Tremblay L, Reichenbach JR, Lepage M. Characterization of microparticles of iron oxide for magnetic resonance imaging. Magn Reson Imaging 2022; 92:67-81. [DOI: 10.1016/j.mri.2022.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 03/07/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022]
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Zhang Y, Yang M, Wang F, Chen Y, Liu R, Zhang Z, Jiang Z. Histogram Analysis of Quantitative Susceptibility Mapping for the Diagnosis of Parkinson's Disease. Acad Radiol 2022; 29 Suppl 3:S71-S79. [PMID: 33189552 DOI: 10.1016/j.acra.2020.10.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/14/2020] [Accepted: 10/26/2020] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the diagnostic performance of histogram analysis combined with quantitative susceptibility mapping (QSM) for differentiating Parkinson's disease (PD) patients from healthy controls. METHODS We included 35 patients with PD diagnosed by two neurologists from August 2019 to January 2020 in our hospital in this prospective study. The clinical diagnosis was based on the Movement Disorder Society Clinical Diagnostic Criteria for PD. At the same time, 23 healthy volunteers matched for age and sex were recruited as controls. The Mini Mental State Examination, the third part of the Parkinson's Disease Rating Scale, the Hoehn & Yahr stages, and disease duration (year) were used to assess the PD patients. QSM was performed using a 3T MR scanner. The regions of interest were depicted according to the head of the caudate nucleus(CN), globus pallidus(GP), putamina (PUT), thalmus(TH), substantia nigra (SN), red nucleus(RN), and dentate nucleus. Then the corresponding histogram features were extracted. The Mann-Whitney U test was used to identify significant histogram features for differentiating PD patients from healthy controls. Area under the receiver operating characteristics curve (AUC) analysis was conducted to evaluate the diagnostic performance of all significant histogram features. Multivariate logistic regression analysis was performed to identify the best combined model for all seven nuclei. Differences among the AUCs were compared pairwise. RESULTS Histogram features in all nuclei except TH showed significant differences between the groups. Among the single features, the 10th percentile of SN (SNP10) yielded the highest AUC of 0.894, with the highest specificity of 86.86% for differentiating PD patients from healthy controls. The 75th percentile of PUT (PUTP75) yielded the highest sensitivity of 97.14%. In the multivariate logistic regression analysis, SNP10 combined with PUTP75 yielded the highest diagnostic performance with the highest AUC of 0.911, the highest specificity of 91.30% and an excellent sensitivity of 92.40%. CONCLUSION QSM combined with histogram analysis successfully distinguished PD patients from healthy controls, and the result was notably superior to the mean value.
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Yu FF, Yi Huang S, Kumar A, Witzel T, Liao C, Duval T, Cohen-Adad J, Bilgic B. Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps. Magn Reson Med 2022; 87:781-790. [PMID: 34480768 PMCID: PMC8627440 DOI: 10.1002/mrm.28995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 07/13/2021] [Accepted: 08/10/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast parametric maps. We sought to reduce the acquisition time for QSM and macromolecular tissue volume by acquiring both contrasts simultaneously by leveraging their redundancies. The joint virtual coil concept with GRAPPA (JVC-GRAPPA) was applied to reduce acquisition time further. METHODS Three adult volunteers were imaged on a 3 Tesla scanner using a multi-echo 3D GRE sequence acquired at 3 head orientations. Macromolecular tissue volume, QSM, R2∗ , T1 , and proton density maps were reconstructed. The same sequence (GRAPPA R = 4) was performed in subject 1 with a single head orientation for comparison. Fully sampled data was acquired in subject 2, from which retrospective undersampling was performed (R = 6 GRAPPA and R = 9 JVC-GRAPPA). Prospective undersampling was performed in subject 3 (R = 6 GRAPPA and R = 9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. RESULTS Subject 1's multi-orientation and single-orientation macromolecular tissue volume maps were not significantly different based on RMSE. For subject 2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject 3. Using QSM, R2∗ , and macromolecular tissue volume, the contributions of myelin and iron content to susceptibility were estimated. CONCLUSION We have developed a novel strategy to simultaneously acquire data for the reconstruction of 5 intrinsically coregistered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 min using JVC-GRAPPA.
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Affiliation(s)
- Fang Frank Yu
- Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States,,Corresponding author. Fang Frank Yu, MD, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, Ph: 214-648-7813, Fax: 214-648-3904,
| | - Susie Yi Huang
- Department of Radiology, Harvard Medical School, Boston, MA, United States,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
| | - Ashwin Kumar
- Vanderbilt University, Nashville, TN, United States
| | | | - Congyu Liao
- Radiological Sciences Laboratory, Stanford Medicine, Stanford, CA, United States
| | - Tanguy Duval
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, QC, Canada
| | - Julien Cohen-Adad
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, QC, Canada
| | - Berkin Bilgic
- Department of Radiology, Harvard Medical School, Boston, MA, United States,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
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Deistung A, Jäschke D, Draganova R, Pfaffenrot V, Hulst T, Steiner KM, Thieme A, Giordano IA, Klockgether T, Tunc S, Münchau A, Minnerop M, Göricke SL, Reichenbach JR, Timmann D. Quantitative susceptibility mapping reveals alterations of dentate nuclei in common types of degenerative cerebellar ataxias. Brain Commun 2022; 4:fcab306. [PMID: 35291442 PMCID: PMC8914888 DOI: 10.1093/braincomms/fcab306] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 10/28/2021] [Accepted: 01/05/2022] [Indexed: 11/13/2022] Open
Abstract
The cerebellar nuclei are a brain region with high iron content. Surprisingly,
little is known about iron content in the cerebellar nuclei and its possible
contribution to pathology in cerebellar ataxias, with the only exception of
Friedreich’s ataxia. In the present exploratory cross-sectional study,
quantitative susceptibility mapping was used to investigate volume, iron
concentration and total iron content of the dentate nuclei in common types of
hereditary and non-hereditary degenerative ataxias. Seventy-nine patients with
spinocerebellar ataxias of types 1, 2, 3 and 6; 15 patients with
Friedreich’s ataxia; 18 patients with multiple system atrophy, cerebellar
type and 111 healthy controls were also included. All underwent 3 T MRI
and clinical assessments. For each specific ataxia subtype, voxel-based and
volumes-of-interest-based group analyses were performed in comparison with a
corresponding age- and sex-matched control group, both for volume, magnetic
susceptiblity (indicating iron concentration) and susceptibility mass
(indicating total iron content) of the dentate nuclei. Spinocerebellar ataxia of
type 1 and multiple system atrophy, cerebellar type patients showed higher
susceptibilities in large parts of the dentate nucleus but unaltered
susceptibility masses compared with controls. Friedreich’s ataxia
patients and, only on a trend level, spinocerebellar ataxia of type 2 patients
showed higher susceptibilities in more circumscribed parts of the dentate. In
contrast, spinocerebellar ataxia of type 6 patients revealed lower
susceptibilities and susceptibility masses compared with controls throughout the
dentate nucleus. Spinocerebellar ataxia of type 3 patients showed no significant
changes in susceptibility and susceptibility mass. Lower volume of the dentate
nuclei was found to varying degrees in all ataxia types. It was most pronounced
in spinocerebellar ataxia of type 6 patients and least prominent in
spinocerebellar ataxia of type 3 patients. The findings show that alterations in
susceptibility revealed by quantitative susceptibility mapping are common in the
dentate nuclei in different types of cerebellar ataxias. The most striking
changes in susceptibility were found in spinocerebellar ataxia of type 1,
multiple system atrophy, cerebellar type and spinocerebellar ataxia of type 6.
Because iron content is known to be high in glial cells but not in neurons of
the cerebellar nuclei, the higher susceptibility in spinocerebellar ataxia of
type 1 and multiple system atrophy, cerebellar type may be explained by a
reduction of neurons (increase in iron concentration) and/or an increase in
iron-rich glial cells, e.g. microgliosis. Hypomyelination also leads to higher
susceptibility and could also contribute. The lower susceptibility in SCA6
suggests a loss of iron-rich glial cells. Quantitative susceptibility maps
warrant future studies of iron content and iron-rich cells in ataxias to gain a
more comprehensive understanding of the pathogenesis of these diseases.
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Affiliation(s)
- Andreas Deistung
- University Clinic and Outpatient Clinic for Radiology, Department for Radiation Medicine, University Hospital Halle (Saale), Halle (Saale), Germany
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, Essen, Germany
| | - Dominik Jäschke
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, Essen, Germany
| | - Rossitza Draganova
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, Essen, Germany
| | - Viktor Pfaffenrot
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany
| | - Thomas Hulst
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, Essen, Germany
- Erasmus University College, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Katharina M. Steiner
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, Essen, Germany
| | - Andreas Thieme
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, Essen, Germany
| | - Ilaria A. Giordano
- Department of Neurology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Thomas Klockgether
- Department of Neurology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Sinem Tunc
- Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Alexander Münchau
- Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany
| | - Martina Minnerop
- Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Duesseldorf, Germany
| | - Sophia L. Göricke
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, Essen University Hospital, Essen, Germany
| | - Jürgen R. Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Dagmar Timmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, Essen, Germany
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Du J, Ji Y, Zhu J, Mai X, Zou J, Chen Y, Gu N. Edge prior guided dictionary learning for quantitative susceptibility mapping reconstruction. Quant Imaging Med Surg 2022; 12:510-525. [PMID: 34993097 DOI: 10.21037/qims-21-243] [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: 03/09/2021] [Accepted: 07/07/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Compared with conventional magnetic resonance imaging methods, the quantitative magnetic susceptibility mapping (QSM) technique can quantitatively measure the magnetic susceptibility distribution of tissues, which has an important clinical application value in the investigations of brain micro-bleeds, Parkinson's, and liver iron deposition, etc. However, the quantitative susceptibility mapping algorithm is an ill-posed inverse problem due to the near-zero value in the dipole kernel, and high-quality QSM reconstruction with effective streaking artifact suppression remains a challenge. In recent years, the performance of sparse representation has been well validated in improving magnetic resonance image (MRI) reconstruction. METHODS In this study, by incorporating feature learning into sparse representation, we propose an edge prior guided dictionary learning-based reconstruction method for the dipole inversion in quantitative susceptibility mapping reconstruction. The structure feature dictionary relies on magnitude images for susceptibility maps have similar structures with magnitude images, and this structure feature dictionary and edge prior information are used in the dipole inversion step. RESULTS The performance of the proposed algorithm is assessed through in vivo human brain clinical data, leading to high-quality susceptibility maps with improved streaking artifact suppression, structural recovery, and quantitative metrics. CONCLUSIONS The proposed edge prior guided dictionary learning method for dipole inversion in QSM achieves improved performance in streaking artifacts suppression, structural recovery and deep gray matter reconstruction.
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Affiliation(s)
- Jiacheng Du
- The State Key Laboratory of Bioelectronics and Jiangsu Key Laboratory of Biomaterials and Devices, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - Yuxin Ji
- The Laboratory of Image Science and Technology, Key Laboratory of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Jiali Zhu
- The Laboratory of Image Science and Technology, Key Laboratory of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Xiaoli Mai
- The Radiology, Nanjing Drum Tower Hospital, Nanjing, China
| | - Junting Zou
- The Radiology, Nanjing Drum Tower Hospital, Nanjing, China
| | - Yang Chen
- The Laboratory of Image Science and Technology, Key Laboratory of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Ning Gu
- The State Key Laboratory of Bioelectronics and Jiangsu Key Laboratory of Biomaterials and Devices, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
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Dimov AV, Nguyen TD, Spincemaille P, Sweeney EM, Zinger N, Kovanlikaya I, Kopell BH, Gauthier SA, Wang Y. Global cerebrospinal fluid as a zero-reference regularization for brain quantitative susceptibility mapping. J Neuroimaging 2022; 32:141-147. [PMID: 34480496 PMCID: PMC8752493 DOI: 10.1111/jon.12923] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 07/11/2021] [Accepted: 08/09/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND AND PURPOSE The objective ofthis study was to demonstrate a global cerebrospinal fluid (CSF) method for a consistent and automated zero referencing of brain quantitative susceptibility mapping (QSM). METHODS Whole brain CSF mask was automatically segmented by thresholding the gradient echo transverse relaxation ( R2∗) map, and regularization was employed to enforce uniform susceptibility distribution within the CSF volume in the field-to-susceptibility inversion. This global CSF regularization method was compared with a prior ventricular CSF regularization. Both reconstruction methods were compared in a repeatability study of 12 healthy subjects using t-test on susceptibility measurements, and in patient studies of 17 multiple sclerosis (MS) and 10 Parkinson's disease (PD) patients using Wilcoxon rank-sum test on radiological scores. RESULTS In scan-rescan experiments, global CSF regularization provided more consistent CSF volume as well as higher repeatability of QSM measurements than ventricular CSF regularization with a smaller bias: -2.7 parts per billion (ppb) versus -0.13 ppb (t-test p<0.05) and a narrower 95% limits of agreement: [-7.25, 6.99] ppb versus [-16.60, 11.19 ppb] (f-test p<0.05). In PD and MS patients, global CSF regularization reduced smoothly varying shadow artifacts and significantly improved the QSM quality score (p<0.001). CONCLUSIONS The proposed whole brain CSF method for QSM zero referencing improves repeatability and image quality of brain QSM compared to the ventricular CSF method.
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Affiliation(s)
- Alexey V. Dimov
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | | | | | - Nicole Zinger
- Department of Neurology, Weill Cornell Medicine, New York, USA
| | | | - Brian H. Kopell
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, USA
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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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Probst J, Rohner M, Zahn M, Piccirelli M, Pangalu A, Luft A, Deistung A, Klohs J, Wegener S. Quantitative susceptibility mapping in ischemic stroke patients after successful recanalization. Sci Rep 2021; 11:16038. [PMID: 34362957 PMCID: PMC8346586 DOI: 10.1038/s41598-021-95265-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 07/22/2021] [Indexed: 11/09/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a novel processing method for gradient-echo magnetic resonance imaging (MRI). Higher magnetic susceptibility in cortical veins have been observed on susceptibility maps in the ischemic hemisphere of stroke patients, indicating an increased oxygen extraction fraction (OEF). Our goal was to investigate susceptibility in veins of stroke patients after successful recanalization in order to analyze the value of QSM in predicting tissue prognosis and clinical outcome. We analyzed MR images of 23 patients with stroke due to unilateral middle cerebral artery (MCA)-M1/M2 occlusion acquired 24–72 h after successful thrombectomy. The susceptibilities of veins were obtained from QSM and compared between the stroke territory, the ipsilateral non-ischemic MCA territory and the contralateral MCA territory. As outcome variables, early infarct size and functional disability (modified Rankin Scale, mRS) after 3–5 months was used. The median susceptibility value of cortical veins in the ischemic core was 41% lower compared to the ipsilateral non-ischemic MCA territory and 38% lower than on the contralateral MCA territory. Strikingly, in none of the patients prominent vessels with high susceptibility signal were found after recanalization. Venous susceptibility values within the infarct did not correlate with infarct volume or functional disability after 3–5 months. Low venous susceptibility within the infarct core after successful recanalization of the occluded vessel likely indicates poor oxygen extraction arising from tissue damage. We did not identify peri-infarct tissue with increased susceptibility values as potential surrogate of former penumbral areas. We found no correlation of QSM parameters with infarct size or outcome.
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Affiliation(s)
- Jasmin Probst
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Marco Rohner
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Malin Zahn
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Marco Piccirelli
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
| | - Athina Pangalu
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, Switzerland
| | - Andreas Luft
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland.,Cereneo Center for Neurology and Rehabilitation, Vitznau, Switzerland
| | - Andreas Deistung
- University Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), Halle, Germany
| | - Jan Klohs
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Susanne Wegener
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland.
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Zhu X, Gao Y, Liu F, Crozier S, Sun H. Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning. Z Med Phys 2021; 32:188-198. [PMID: 34312047 PMCID: PMC9948866 DOI: 10.1016/j.zemedi.2021.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 06/23/2021] [Accepted: 06/26/2021] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can substantially shorten the scan time or enhance the spatial resolution without increasing scan time; however, this may lead to significant DGM susceptibility underestimation. METHOD A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages. The xQSM method is compared with two conventional dipole inversion methods using simulated and in vivo experiments from 4 healthy subjects at 3T. Pre-processed magnetic field maps are extended symmetrically from the centre of globus pallidus in the coronal plane to simulate QSM acquisitions of difference spatial coverages, ranging from 100% (∼32mm) to 400% (∼128mm) of the actual DGM physical size. RESULTS The proposed xQSM network led to the lowest DGM contrast loss in both simulated and in vivo subjects, with the smallest susceptibility variation range across all spatial coverages. For the digital brain phantom simulation, xQSM improved the DGM susceptibility underestimation more than 20% in small spatial coverages, as compared to conventional methods. For the in vivo acquisition, less than 5% DGM susceptibility error was achieved in 48mm axial slabs using the xQSM network, while a minimum of 112mm coverage was required for conventional methods. It is also shown that the background field removal process performed worse in reduced brain coverages, which further deteriorated the subsequent dipole inversion. CONCLUSION The recently proposed deep learning-based xQSM method significantly improves the accuracy of DGM QSM from small spatial coverages as compared with conventional QSM algorithms, which can shorten DGM QSM acquisition time substantially.
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Affiliation(s)
| | | | | | | | - Hongfu Sun
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.
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Marques JP, Meineke J, Milovic C, Bilgic B, Chan K, Hedouin R, van der Zwaag W, Langkammer C, Schweser F. QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures. Magn Reson Med 2021; 86:526-542. [PMID: 33638241 PMCID: PMC8048665 DOI: 10.1002/mrm.28716] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM. METHODS We created a digital whole-head tissue property phantom by segmenting and postprocessing high-resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer. We simulated the steady-state magnetization at 7 T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: TR and TE, voxel size, background fields, and RF phase biases. Diffusion-weighted imaging phantom data are provided, allowing future investigations of tissue-microstructure effects in phase and QSM algorithms. RESULTS The brain part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantom's properties, including the possibility of generating phase data with nonlinear evolution over TE due to partial-volume effects or complex distributions of frequency shifts within the voxel. CONCLUSION The presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.
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Affiliation(s)
- José P. Marques
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
| | | | - Carlos Milovic
- Department of Electrical EngineeringPontificia Universidad Catolica de ChileSantiagoChile
- Biomedical Imaging CenterPontificia Universidad Catolica de ChileSantiagoChile
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUnited Kingdom
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical ImagingCharlestownMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Harvard‐MIT Health Sciences and TechnologyMITCambridgeMassachusettsUSA
| | - Kwok‐Shing Chan
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
| | - Renaud Hedouin
- Donders Institute for Brain, Cognition and BehaviorRadboud UniversityNijmegenthe Netherlands
- Centre Inria Rennes ‐ Bretagne AtlantiqueRennesFrance
| | | | | | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis CenterDepartment of NeurologyJacobs School of Medicine and Biomedical SciencesUniversity at BuffaloThe State University of New YorkBuffaloNew YorkUSA
- Center for Biomedical Imaging, Clinical and Translational Science InstituteUniversity at BuffaloThe State University of New YorkBuffaloNew YorkUSA
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Chen L, Cai S, van Zijl PC, Li X. Single-step calculation of susceptibility through multiple orientation sampling. NMR IN BIOMEDICINE 2021; 34:e4517. [PMID: 33822416 PMCID: PMC8184590 DOI: 10.1002/nbm.4517] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 03/06/2021] [Accepted: 03/14/2021] [Indexed: 06/12/2023]
Abstract
Quantitative susceptibility mapping (QSM) was developed to estimate the spatial distribution of magnetic susceptibility from MR signal phase acquired using a gradient echo (GRE) sequence. The field-to-susceptibility inversion in QSM is known to be ill-posed and needs numerical stabilization through either regularization or data oversampling. The calculation of susceptibility through the multiple orientation sampling (COSMOS) method uses phase data acquired at three or more head orientations to achieve a well-conditioned field-to-susceptibility inversion and is often considered the gold standard for in vivo QSM. However, the conventional COSMOS approach, here named multistep COSMOS (MSCOSMOS), solves the dipole inversion from the local field derived from raw GRE phase through multiple steps of phase preprocessing. Error propagations between these consecutive phase processing steps can thus affect the final susceptibility quantification. On the other hand, recently proposed single-step QSM (SSQSM) methods aim to solve an integrated inversion from unprocessed or total phase to mitigate such error propagations but have been limited to single orientation QSM. This study therefore aimed to test the feasibility of using single-step COSMOS (SSCOSMOS) to jointly perform background field removal and dipole inversion with multiple orientation sampling, which could serve as a better standard for gauging SSQSM methods. We incorporated multiple spherical mean value (SMV) kernels of various radii with the dipole inversion in SSCOSMOS. QSM reconstructions with SSCOSMOS and MSCOSMOS were compared using both simulations with a numerical head phantom and in vivo human brain data. SSCOSMOS permitted integrated background removal and dipole inversion without the need to adjust any regularization parameters. In addition, with sufficiently large SMV kernels, SSCOSMOS performed consistently better than MSCOSMOS in all the tested error metrics in our simulations, giving better susceptibility quantification and smaller reconstruction error. Consistent tissue susceptibility values were obtained between SSCOSMOS and MSCOSMOS.
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Affiliation(s)
- Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Peter C.M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States
- Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland, United States
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40
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ZHUANG HAN, LIU XUELING, WANG HUI, QIN CHUNLI, LI YUXIN, LI WENSHENG, SHI YONGHONG. DIAGNOSIS OF EARLY STAGE PARKINSON’S DISEASE ON QUANTITATIVE SUSCEPTIBILITY MAPPING USING COMPLEX NETWORK WITH ONE-WAY ANOVA F-TEST FEATURE SELECTION. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421400261] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper presented a novel complex network with one-way ANOVA F-test feature selection to diagnose early-stage Parkinson’s disease (PD) on quantitative susceptibility mapping (QSM). Experimental results on QSM images of 30 early-stage PD patients and 27 healthy controls (HC) proved that the F-test feature selection scheme was effective and achieved good classification results. The accuracy, AUC, sensitivity and specificity of our method were 0.96, 0.97, 0.99 and 0.95, respectively, which were improved by 15%, 4%, 29% and 2%, respectively by comparison with the commonly used region of interest (ROI) based method. Meanwhile, according to the feature importance, the potential brain regions affected by PD were arranged orderly. The affected regions were distributed as follows: 61% of them are located in right hemisphere and 39% in the left hemisphere. Particularly, frontal lobe, parietal lobe, temporal lobe and occipital lobe accounted for 24%, 20%, 5% and 14%, respectively, and striatum and the dorsal thalamus accounted for 16%. It concludes that the complex network with one-way ANOVA F-test feature selection can greatly improve the diagnostic performance of early-stage PD based on QSM, as well as provide a new way to study the effect of PD on brain in the future.
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Affiliation(s)
- HAN ZHUANG
- Digital Medical Research Center, School of Basic Medicine Sciences, Fudan University, Shanghai 200032, P. R. China
| | - XUELING LIU
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, P. R. China
| | - HUI WANG
- Digital Medical Research Center, School of Basic Medicine Sciences, Fudan University, Shanghai 200032, P. R. China
| | - CHUNLI QIN
- Digital Medical Research Center, School of Basic Medicine Sciences, Fudan University, Shanghai 200032, P. R. China
| | - YUXIN LI
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, P. R. China
| | - WENSHENG LI
- Department of Anatomy, Histology and Embryology, School of Basic Medicine Sciences, Fudan University, Shanghai 200032, P. R. China
| | - YONGHONG SHI
- Digital Medical Research Center, Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention School of Basic Medicine Sciences, Fudan University, Shanghai 200032, China
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41
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Chen Z, Chen Z. Computed inverse MRI (CIMRI) for intrinsic brain magnetic susceptibility mapping. Comput Biol Med 2021; 134:104498. [PMID: 34051451 DOI: 10.1016/j.compbiomed.2021.104498] [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: 03/12/2021] [Revised: 04/30/2021] [Accepted: 05/12/2021] [Indexed: 11/24/2022]
Abstract
In magnetic resonance imaging (MRI), tissue magnetization in the main field B0 is a necessary preparation for magnetic resonance signal formation that imposes an inherent dipole effect on MRI signals, which predisposes an artifact on tissue MRI. In the MRI principle, T2*-weighted MRI can be described by a cascade of data transformations: from the source of tissue magnetic susceptibility (denoted by χ) to the output of complex-valued T2* image (in a magnitude and phase pair). Under the linear approximation of the T2* phase MRI, we can computationally reconstruct the source χ by quantitative susceptibility mapping (QSM), which is an inverse solution that is modeled by computed inverse MRI (CIMRI). For a brain function study using MRI (fMRI), we can reconstruct a timeseries of brain χ images to represent the intrinsic brain function activity called functional QSM (fQSM). This intrinsic depiction is defined as the removal of the artifactual dipole effect and other MRI-introduced distortions from phase data through inverse mapping. With one high-resolution QSM experiment and one group (20 subjects) low-resolution fQSM experiment, we show that the dipole effect manifests as ripples around vessels and a spatial split at a local activation blob and that the dipole effect could be removed by CIMRI. In the context of inverse imaging or undoing MRI transformations (including dipole convolution), we computationally achieve brain intrinsic structural depiction by QSM and intrinsic functional depiction by fQSM.
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Affiliation(s)
- Zeyuan Chen
- Department of Computer Sciences, University of California-Davis, Davis, CA, 95616, USA.
| | - Zikuan Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, 91010, USA; Zinv LLC, Albuquerque, NM, 87108, USA.
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42
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Ozturk K, Nascene D. Susceptibility-Weighted Imaging of the Pediatric Brain after Repeat Doses of Gadolinium-Based Contrast Agent. AJNR Am J Neuroradiol 2021; 42:1136-1143. [PMID: 33888459 DOI: 10.3174/ajnr.a7143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 12/17/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Gadolinium complexes have paramagnetic properties; thus, we aimed to determine the susceptibility changes in the globus pallidus and dentate nucleus following administration of linear or macrocyclic gadolinium-based contrast agents in children. MATERIALS AND METHODS Thirty-three patients with linear gadolinium-based contrast agent gadopentetate dimeglumine administration, 33 age- and sex-matched patients with macrocyclic gadolinium-based contrast agent gadobutrol administration, and 33 age- and sex-matched control subjects without gadolinium exposure were enrolled in this retrospective study. The signal intensity on SWI and T1WI was determined in the dentate nucleus, middle cerebellar peduncle, globus pallidus, and pulvinar of the thalamus in an ROI-based analysis to calculate dentate nucleus-to-middle cerebellar peduncle and globus pallidus-to-thalamus ratios. A repeated measures ANOVA was performed to compare SWIminimum, SWImean, and T1WI signal intensity ratios between gadolinium-based contrast agent groups and control subjects. Pearson correlation analysis was performed to determine any correlation between signal intensity ratios and variables. RESULTS Dentate nucleus-to-middle cerebellar peduncle and globus pallidus-to-thalamus ratios for both SWImean and SWIminimum were lower for the linear gadolinium-based contrast agent group compared with macrocyclic gadolinium-based contrast agent and control groups (P < .05). No significant difference of the SWImean and SWIminimum ratios were noted between the macrocyclic gadolinium-based contrast agent group and the control group (P > .05). Both dentate nucleus-to-middle cerebellar peduncle and globus pallidus-to-thalamus ratios on T1WI in the linear gadolinium-based contrast agent group were higher than in the control group and the macrocyclic gadolinium-based contrast agent group (P < .05). A negative correlation was identified between SWImean and SWIminimum ratios and the number of linear gadolinium-based contrast agent administrations (dentate nucleus-to-middle cerebellar peduncle ratio: SWImean, r = -0.43, P = .005; SWIminimum, r = -0.38, P = .011; globus pallidus-to-thalamus ratio: SWImean, r = -0.39, P = .009; SWIminimum, r = -0.33, P = .017). CONCLUSIONS SWI analysis of the pediatric brain demonstrated a statistically significant decrease in SWIminimum and SWImean values for the dentate nucleus and globus pallidus after administration of linear gadolinium-based contrast agents but not macrocyclic gadolinium-based contrast agents.
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Affiliation(s)
- K Ozturk
- From the Department of Radiology, University of Minnesota, Minneapolis, Minnesota
| | - D Nascene
- From the Department of Radiology, University of Minnesota, Minneapolis, Minnesota
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43
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Knopp T, Grosser M. MRIReco.jl: An MRI reconstruction framework written in Julia. Magn Reson Med 2021; 86:1633-1646. [PMID: 33817833 DOI: 10.1002/mrm.28792] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/11/2021] [Accepted: 03/12/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE The aim of this work is to develop a high-performance, flexible, and easy-to-use MRI reconstruction framework using the scientific programming language Julia. METHODS Julia is a modern, general purpose programming language with strong features in the area of signal/image processing and numerical computing. It has a high-level syntax but still generates efficient machine code that is usually as fast as comparable C/C++ applications. In addition to the language features itself, Julia has a sophisticated package management system that makes proper modularization of functionality across different packages feasible. Our developed MRI reconstruction framework MRIReco.jl can therefore reuse existing functionality from other Julia packages and concentrate on the MRI-related parts. This includes common imaging operators and support for MRI raw data formats. RESULTS MRIReco.jl is a simple to use framework with a high degree of accessibility. While providing a simple-to-use interface, many of its components can easily be extended and customized. The performance of MRIReco.jl is compared to the Berkeley Advanced Reconstruction Toolbox (BART) and we show that the Julia framework achieves comparable reconstruction speed as the popular C/C++ library. CONCLUSIONS Modern programming languages can bridge the gap between high performance and accessible implementations. MRIReco.jl leverages this fact and contributes a promising environment for future algorithmic development in MRI reconstruction.
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Affiliation(s)
- Tobias Knopp
- Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany.,Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mirco Grosser
- Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany.,Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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44
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Liu X, Du L, Zhang B, Zhao Z, Gao W, Liu B, Liu J, Chen Y, Wang Y, Yu H, Ma G. Alterations and Associations Between Magnetic Susceptibility of the Basal Ganglia and Diffusion Properties in Alzheimer's Disease. Front Neurosci 2021; 15:616163. [PMID: 33664645 PMCID: PMC7921325 DOI: 10.3389/fnins.2021.616163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 01/12/2021] [Indexed: 11/28/2022] Open
Abstract
This study adopted diffusion tensor imaging to detect alterations in the diffusion parameters of the white matter fiber in Alzheimer’s disease (AD) and used quantitative susceptibility mapping to detect changes in magnetic susceptibility. However, whether the changes of susceptibility values due to excessive iron in the basal ganglia have correlations with the alterations of the diffusion properties of the white matter in patients with AD are still unknown. We aim to investigate the correlations among magnetic susceptibility values of the basal ganglia, diffusion indexes of the white matter, and cognitive function in patients with AD. Thirty patients with AD and nineteen healthy controls (HCs) were recruited. Diffusion indexes of the whole brain were detected using tract-based spatial statistics. The caudate nucleus, putamen, and globus pallidus were selected as regions of interest, and their magnetic susceptibility values were measured. Compared with HCs, patients with AD showed that there were significantly increased axial diffusivity (AxD) in the internal capsule, superior corona radiata (SCR), and right anterior corona radiata (ACR); increased radial diffusivity (RD) in the right anterior limb of the internal capsule, ACR, and genu of the corpus callosum (GCC); and decreased fractional anisotropy (FA) in the right ACR and GCC. The alterations of RD values, FA values, and susceptibility values of the right caudate nucleus in patients with AD were correlated with cognitive scores. Besides, AxD values in the right internal capsule, ACR, and SCR were positively correlated with the magnetic susceptibility values of the right caudate nucleus in patients with AD. Our findings revealed that the magnetic susceptibility of the caudate nucleus may be an MRI-based biomarker of the cognitive dysfunction of AD and abnormal excessive iron distribution in the basal ganglia had adverse effects on the diffusion properties of the white matter.
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Affiliation(s)
- Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
| | - Zifang Zhao
- Department of Anesthesiology, Peking University First Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China
| | - Jian Liu
- Department of Ultrasound Diagnosis, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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45
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Li J, Zhang Q, Zhang N, Guo L. Increased Brain Iron Detection by Voxel-Based Quantitative Susceptibility Mapping in Type 2 Diabetes Mellitus Patients With an Executive Function Decline. Front Neurosci 2021; 14:606182. [PMID: 33519360 PMCID: PMC7843466 DOI: 10.3389/fnins.2020.606182] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/07/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose Brain iron accumulation has been suggested as a pathomechanism in patients with type 2 diabetes mellitus (T2DM) with cognitive impairment. This research aims to examine the total-brain pattern of iron accumulation in relation to executive function decline in patients with T2DM by voxel-based quantitative susceptibility mapping (QSM) analysis. Materials and Methods A total of 32 patients with T2DM and 34 age- and sex-matched healthy controls (HCs) were enrolled in this study. All participants underwent brain magnetic resonance examination, and 48 individuals underwent cognitive function assessments. Imaging data were collected with three-dimensional fast low-angle shot sequences to achieve magnitude as well as phase images. Using voxel-based QSM analysis, we compared the voxel-wise susceptibility values of the whole brain among groups and explored whether the susceptibility values had correlations with cognitive data. Results Among the 66 participants, cognitive function was estimated in 23 patients with T2DM (11 males and 12 females; average age, 64.65 ± 8.44 years) and 25 HCs (13 males and 12 females; average age, 61.20 ± 7.62 years). T2DM patients exhibited significantly (t = 4.288, P < 0.001) lower Montreal Cognitive Assessment (MoCA) scores [T2DM, 27 (27, 28); HCs, 29 (28, 29); normal standard ≥ 26)] and higher Trail-making Test (TMT)-A/TMT-B scores [71 (51, 100)/185 (149, 260)] than HCs [53 (36.5, 63.5)/150 (103, 172.5)] (Z = 2.612, P = 0.009; Z = 2.797, P = 0.005). Subjects with T2DM showed significantly higher susceptibility values than HCs in the caudate/putamen/pallidum, frontal inferior triangular gyrus, and precentral gyrus on the right hemisphere. In contrast (HC > T2DM), no region showed a significant difference in susceptibility values between the groups. The correlation analysis between susceptibility values and cognitive function scores was tested by voxel-based susceptibility value with sex and age as covariates. After multiple comparison correction, in T2DM patients, the left thalamus showed a significant relationship with TMT-A (R 2 = 0.53, P = 0.001). The right thalamus and left thalamus showed a significant relationship with TMT-B (R 2 = 0.35, P = 0.019; and R 2 = 0.38, P = 0.017, respectively). In HCs, the cluster of right precentral/middle frontal gyrus/inferior frontal gyrus/inferior triangular gyrus showed a significant relationship with TMT-B (R 2 = 0.59, P = 0.010). No relationship was found between the susceptibility values with MoCA in the brain region in both two groups. Conclusion Patients with T2DM presented declined cognitive assessments and elevated iron deposition in the striatum and frontal lobe, suggesting that executive function decline in T2DM might be associated with the cerebral iron burden and that changes in susceptibility values may represent a latent quantitative imaging marker for early assessment of cognitive decline in patients with T2DM.
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Affiliation(s)
- Jing Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Qihao Zhang
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States
| | - Nan Zhang
- Shandong Medical Imaging Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lingfei Guo
- Shandong Medical Imaging Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, China
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Polak D, Chatnuntawech I, Yoon J, Iyer SS, Milovic C, Lee J, Bachert P, Adalsteinsson E, Setsompop K, Bilgic B. Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM). NMR IN BIOMEDICINE 2020; 33:e4271. [PMID: 32078756 PMCID: PMC7528217 DOI: 10.1002/nbm.4271] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 05/04/2023]
Abstract
High-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.
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Affiliation(s)
- Daniel Polak
- Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Siemens Healthcare GmbH, Erlangen, Germany
| | - Itthi Chatnuntawech
- National Science and Technology Development Agency, National Nanotechnology Center, Pathum Thani, Thailand
| | - Jaeyeon Yoon
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Siddharth Srinivasan Iyer
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Electronical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Peter Bachert
- Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elfar Adalsteinsson
- Department of Electronical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Berkin Bilgic
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Isaacs BR, Mulder MJ, Groot JM, van Berendonk N, Lute N, Bazin PL, Forstmann BU, Alkemade A. 3 versus 7 Tesla magnetic resonance imaging for parcellations of subcortical brain structures in clinical settings. PLoS One 2020; 15:e0236208. [PMID: 33232325 PMCID: PMC7685480 DOI: 10.1371/journal.pone.0236208] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 11/06/2020] [Indexed: 12/14/2022] Open
Abstract
7 Tesla (7T) magnetic resonance imaging holds great promise for improved visualization of the human brain for clinical purposes. To assess whether 7T is superior regarding localization procedures of small brain structures, we compared manual parcellations of the red nucleus, subthalamic nucleus, substantia nigra, globus pallidus interna and externa. These parcellations were created on a commonly used clinical anisotropic clinical 3T with an optimized isotropic (o)3T and standard 7T scan. The clinical 3T MRI scans did not allow delineation of an anatomically plausible structure due to its limited spatial resolution. o3T and 7T parcellations were directly compared. We found that 7T outperformed the o3T MRI as reflected by higher Dice scores, which were used as a measurement of interrater agreement for manual parcellations on quantitative susceptibility maps. This increase in agreement was associated with higher contrast to noise ratios for smaller structures, but not for the larger globus pallidus segments. Additionally, control-analyses were performed to account for potential biases in manual parcellations by assessing semi-automatic parcellations. These results showed a higher consistency for structure volumes for 7T compared to optimized 3T which illustrates the importance of the use of isotropic voxels for 3D visualization of the surgical target area. Together these results indicate that 7T outperforms c3T as well as o3T given the constraints of a clinical setting.
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Affiliation(s)
- Bethany R. Isaacs
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
- Department of Experimental Neurosurgery, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Martijn J. Mulder
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
- Psychology and Social Sciences, University of Utrecht, Utrecht, The Netherlands
| | - Josephine M. Groot
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
| | - Nikita van Berendonk
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
| | - Nicky Lute
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
- Clinical Neuropsychology, Vrije University, Amsterdam, The Netherlands
| | - Pierre-Louis Bazin
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
- Max Planck Institute for Human, Cognitive and Brain Sciences, Leipzig, Germany
| | - Birte U. Forstmann
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
| | - Anneke Alkemade
- University of Amsterdam, Integrative Model-Based Cognitive Neuroscience Research Unit, Amsterdam, The Netherlands
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48
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Gharabaghi S, Liu S, Wang Y, Chen Y, Buch S, Jokar M, Wischgoll T, Kashou NH, Zhang C, Wu B, Cheng J, Haacke EM. Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging. Front Neurosci 2020; 14:581474. [PMID: 33192267 PMCID: PMC7645168 DOI: 10.3389/fnins.2020.581474] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/16/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. Methods The proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects. Results The unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI’s performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(−1%); MEDI + 2%(−11%) and then iSWIM −5%(−10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature. Conclusion This study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts.
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Affiliation(s)
- Sara Gharabaghi
- Department of Computer Science and Engineering, Wright State University, Dayton, OH, United States.,Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States
| | - Saifeng Liu
- The MRI Institute for Biomedical Research, Bingham Farms, MI, United States
| | - Ying Wang
- The MRI Institute for Biomedical Research, Bingham Farms, MI, United States.,Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, Detroit, MI, United States
| | - Sagar Buch
- Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Mojtaba Jokar
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States
| | - Thomas Wischgoll
- Department of Computer Science and Engineering, Wright State University, Dayton, OH, United States
| | - Nasser H Kashou
- Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH, United States
| | - Chunyan Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Bo Wu
- Shanghai Zhu Yan Medical Technology Ltd., Shanghai, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - E Mark Haacke
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States.,The MRI Institute for Biomedical Research, Bingham Farms, MI, United States.,Department of Radiology, Wayne State University, Detroit, MI, United States.,Department of Neurology, Wayne State University, Detroit, MI, United States.,Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States
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49
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Nael K, Dagher JC, Downs ME, Fine MS, Brokaw E, Millward D. Maximum AmbiGuity Distance for Phase Imaging in Detection of Traumatic Cerebral Microbleeds: An Improvement over Current Imaging Practice. AJNR Am J Neuroradiol 2020; 41:2027-2033. [PMID: 33033046 DOI: 10.3174/ajnr.a6774] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 07/07/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND PURPOSE Developed using a rigorous mathematic framework, Maximum AmbiGuity distance for Phase Imaging (MAGPI) is a promising phase-imaging technique that provides optimal phase SNR and reduced susceptibility artifacts. We aimed to test the potential of MAGPI over routinely used SWI in the detection of traumatic cerebral microbleeds in athletes diagnosed with mild traumatic brain injury. MATERIALS AND METHODS In this prospective study, 10 athletes (18-22 years of age, 3 women/7 men) diagnosed with mild traumatic brain injury were enrolled. Brain MRIs were performed using 3T MR imaging at 2 days, 2 weeks, and 2 months after head trauma. The imaging protocol included whole-brain T1 MPRAGE, T2 FLAIR, conventional SWI, and the MAGPI multiecho sequence. Phase images from MAGPI were put through a previously described SWI process to generate MAGPI-SWI. Conventional and MAGPI-SWI were assessed independently by a board-certified neuroradiologist for the presence of contusions and cerebral microbleeds. All participants had routine neuropsychological assessment and Visuo-Motor Tests. RESULTS At initial assessment, 4 of the participants had visuo-motor performance indicative of mild traumatic brain injury, and 4 participants had a Post-Concussion Symptom Scale score of >21, a threshold that has been used to define moderate impairment. Cerebral microbleeds were identified in 6 participants on MAGPI-SWI, 4 of whom had evidence of concurrent contusions on FLAIR imaging. None of these cerebral microbleeds were identified confidently on conventional SWI due to substantial distortion and susceptibility artifacts. CONCLUSIONS Optimal phase unwrapping with reduced susceptibility in MAGPI-SWI can clarify small microbleeds that can go undetected with routinely used conventional SWI.
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Affiliation(s)
- K Nael
- From the David Geffen School of Medicine (K.N.), University of California, Los Angeles, Los Angeles, California
| | - J C Dagher
- Emerging Technologies (J.C.D., M.E.D., M.S.F., E.B.), MITRE Corporation, McLean, Virginia
| | - M E Downs
- Emerging Technologies (J.C.D., M.E.D., M.S.F., E.B.), MITRE Corporation, McLean, Virginia
| | - M S Fine
- Emerging Technologies (J.C.D., M.E.D., M.S.F., E.B.), MITRE Corporation, McLean, Virginia
| | - E Brokaw
- Emerging Technologies (J.C.D., M.E.D., M.S.F., E.B.), MITRE Corporation, McLean, Virginia
| | - D Millward
- Athletics Department (D.M.), University of Arizona, Tucson, Arizona
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50
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Doucette J, Kames C, Rauscher A. DECAES - DEcomposition and Component Analysis of Exponential Signals. Z Med Phys 2020; 30:271-278. [PMID: 32451148 DOI: 10.1016/j.zemedi.2020.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/17/2020] [Accepted: 04/14/2020] [Indexed: 12/12/2022]
Abstract
Combinations of multiple exponentially decaying signals are found across many disciplines of science. Decomposition of these multi-exponential signals into their individual components provides insight into the various contributors to the signal. Magnetic resonance images, for instance, can be acquired with multiple gradient or spin echoes to provide voxel by voxel multi-exponential T2* or T2 decays, respectively. With their millions of voxels, these images make the task of decomposition into individual exponentials computationally challenging. Current implementations take several hours, which is prohibitively long in many settings, such as on-scanner calculation for clinical applications. Here, we present a fast approach for the decomposition of multi-exponential signals. The method is applied to multi echo spin echo MRI scans and computes myelin water maps of the whole brain in under 2min, and luminal water maps of the prostate in under 1min.
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Affiliation(s)
- Jonathan Doucette
- Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, V6T 1Z1 Vancouver, BC, Canada; UBC MRI Research Centre, 2221 Wesbrook Mall, V6T 2B5 Vancouver, BC, Canada.
| | - Christian Kames
- Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, V6T 1Z1 Vancouver, BC, Canada; UBC MRI Research Centre, 2221 Wesbrook Mall, V6T 2B5 Vancouver, BC, Canada
| | - Alexander Rauscher
- Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, V6T 1Z1 Vancouver, BC, Canada; UBC MRI Research Centre, 2221 Wesbrook Mall, V6T 2B5 Vancouver, BC, Canada; Department of Radiology, University of British Columbia, 2775 Laurel Street, V5Z 1M9 Vancouver, BC, Canada; Department of Pediatrics, Faculty of Medicine, University of British Columbia, 4480 Oak Street, BC Children's Hospital, V6H 3V4 Vancouver, BC, Canada
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