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Saucedo A, Sayre J, Thomas MA. Single-shot diffusion trace-weighted MR spectroscopy: Comparison with unipolar and bipolar diffusion-weighted point-resolved spectroscopy. NMR IN BIOMEDICINE 2024; 37:e5090. [PMID: 38148181 PMCID: PMC10957108 DOI: 10.1002/nbm.5090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 11/17/2023] [Accepted: 11/19/2023] [Indexed: 12/28/2023]
Abstract
This study demonstrates the feasibility and performance of the point-resolved spectroscopy (PRESS)-based, single-shot diffusion trace-weighted sequence in quantifying the trace apparent diffusion coefficient (ADC) in phantom and in vivo using a 3-T MRI/MRS scanner. The single-shot diffusion trace-weighted PRESS sequence was implemented and compared with conventional diffusion-weighted (DW)-PRESS variants using bipolar and unipolar diffusion-sensitizing gradients. Nine phantom datasets were acquired using each sequence, and seven volunteers were scanned in three different brain regions to determine the range and variability of trace ADC values, and to allow a comparison of trace ADCs among the sequences. This sequence results in a comparatively stable range of trace ADC values that are statistically significantly higher than those produced from unipolar and bipolar DW-PRESS sequences. Only total n-acetylaspartate, total creatine, and total choline were reliably estimated in all sequences with Cramér-Rao lower bounds of, at most, 20%. The larger trace ADCs from the single-shot sequences are probably attributable to the shorter diffusion time relative to the other sequences. Overall, this study presents the first demonstration of the single-shot diffusion trace-weighted sequence in a clinical scanner at 3 T. The results show excellent agreement of phantom trace ADCs computed with all sequences, and in vivo ADCs agree well with the expected differences between gray and white matter. The diffusion trace-weighted sequence could provide an estimate of the trace ADC in a shorter scan time (by nearly a factor of 3) compared with conventional DW-PRESS approaches that require three separate orthogonal directions.
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Affiliation(s)
- Andres Saucedo
- Radiological Sciences, University of California at Los
Angeles, Los Angeles, CA, United States
- Physics and Biology in Medicine, University of California
at Los Angeles, Los Angeles, CA, United States
| | - James Sayre
- Radiological Sciences, University of California at Los
Angeles, Los Angeles, CA, United States
| | - M. Albert Thomas
- Radiological Sciences, University of California at Los
Angeles, Los Angeles, CA, United States
- Physics and Biology in Medicine, University of California
at Los Angeles, Los Angeles, CA, United States
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2
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Ligneul C, Najac C, Döring A, Beaulieu C, Branzoli F, Clarke WT, Cudalbu C, Genovese G, Jbabdi S, Jelescu I, Karampinos D, Kreis R, Lundell H, Marjańska M, Möller HE, Mosso J, Mougel E, Posse S, Ruschke S, Simsek K, Szczepankiewicz F, Tal A, Tax C, Oeltzschner G, Palombo M, Ronen I, Valette J. Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling. Magn Reson Med 2024; 91:860-885. [PMID: 37946584 DOI: 10.1002/mrm.29877] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/18/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.
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Affiliation(s)
- Clémence Ligneul
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chloé Najac
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Christian Beaulieu
- Departments of Biomedical Engineering and Radiology, University of Alberta, Alberta, Edmonton, Canada
| | - Francesca Branzoli
- Paris Brain Institute-ICM, Sorbonne University, UMR S 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - William T Clarke
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guglielmo Genovese
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ileana Jelescu
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dimitrios Karampinos
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Roland Kreis
- MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager anf Hvidovre, Hvidovre, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minnesota, Minneapolis, USA
| | - Harald E Möller
- NMR Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Jessie Mosso
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- LIFMET, EPFL, Lausanne, Switzerland
| | - Eloïse Mougel
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Stefan Posse
- Department of Neurology, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
- Department of Physics and Astronomy, University of New Mexico School of Medicine, New Mexico, Albuquerque, USA
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Kadir Simsek
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, The Weizmann Institute of Science, Rehovot, Israel
| | - Chantal Tax
- University Medical Center Utrecht, Utrecht, The Netherlands
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Maryland, Baltimore, USA
- F. M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Maryland, Baltimore, USA
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Itamar Ronen
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, UK
| | - Julien Valette
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoires des Maladies Neurodégénératives, Fontenay-aux-Roses, France
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3
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Şimşek K, Döring A, Pampel A, Möller HE, Kreis R. Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting. Magn Reson Med 2022; 88:1962-1977. [PMID: 35803740 PMCID: PMC9545875 DOI: 10.1002/mrm.29367] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/08/2022] [Accepted: 05/31/2022] [Indexed: 12/14/2022]
Abstract
Purpose Definition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non‐Gaussian diffusion of brain metabolites with strongly diffusion‐weighted MR spectroscopy. Methods Short echo time MRS with strong diffusion‐weighting with b‐values up to 25 ms/μm2 at two diffusion times was implemented on a Connectom system and applied in combination with simultaneous spectral and diffusion decay modeling. Motion‐compensation was performed with a combined method based on the simultaneously acquired water and a macromolecular signal. Results The motion compensation scheme prevented spurious signal decay reflected in very small apparent diffusion constants for macromolecular signal. Macromolecular background signal patterns were determined using multiple fit strategies. Signal decay corresponding to non‐Gaussian metabolite diffusion was represented by biexponential fit models yielding parameter estimates for human gray matter that are in line with published rodent data. The optimal fit strategies used constraints for the signal decay of metabolites with limited signal contributions to the overall spectrum. Conclusion The determined macromolecular spectrum based on diffusion properties deviates from the conventional one derived from longitudinal relaxation time differences calling for further investigation before use as experimental basis spectrum when fitting clinical MR spectra. The biexponential characterization of metabolite signal decay is the basis for investigations into pathologic alterations of microstructure. Click here for author‐reader discussions
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Affiliation(s)
- Kadir Şimşek
- Magnetic Resonance MethodologyInstitute of Diagnostic and Interventional Neuroradiology, University of BernBernSwitzerland
- Graduate School for Cellular and Biomedical SciencesUniversity of BernBernSwitzerland
- Translational Imaging Center (TIC)Swiss Institute for Translational and Entrepreneurial MedicineBernSwitzerland
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC)School of Psychology, Cardiff UniversityCardiffUK
| | - André Pampel
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Harald E. Möller
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Roland Kreis
- Magnetic Resonance MethodologyInstitute of Diagnostic and Interventional Neuroradiology, University of BernBernSwitzerland
- Translational Imaging Center (TIC)Swiss Institute for Translational and Entrepreneurial MedicineBernSwitzerland
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4
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Hanstock C, Beaulieu C. Rapid acquisition diffusion MR spectroscopy of metabolites in human brain. NMR IN BIOMEDICINE 2021; 34:e4270. [PMID: 32045958 DOI: 10.1002/nbm.4270] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 01/18/2020] [Accepted: 01/20/2020] [Indexed: 06/10/2023]
Abstract
Few studies have focused on metabolite diffusion in the human brain using 1 H-MRS due to significant technical challenges. Moreover, such studies have required lengthy acquisition times and are therefore impractical to implement clinically. By first characterizing and then minimizing the effects of linear and oscillating eddy currents, which arise from the diffusion gradients, and by implementing phase-cycle and slice-order strategies, as well as introducing a new phase-alignment methodology, we report a method that allows data acquisition requiring 20 seconds per spectrum. This remained feasible, even for b-values >8000 s/mm2 , with a rapid acquisition diffusion MRS methodology. It has allowed the nonlinear characterization of signal intensity with multiple b-values, and has improved the measurement of rotationally invariant diffusion parameters via six-direction, six b-value diffusion tensor spectroscopy (DTS) in 12 minutes at 4.7 T. The shorter DTS acquisition will enable its application to white matter regions not aligned with the gradients and permit clinical studies in a feasible time.
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Affiliation(s)
- Chris Hanstock
- Department of Biomedical Engineering, University of Alberta, Alberta, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, University of Alberta, Alberta, Canada
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5
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Tkáč I, Deelchand D, Dreher W, Hetherington H, Kreis R, Kumaragamage C, Považan M, Spielman DM, Strasser B, de Graaf RA. Water and lipid suppression techniques for advanced 1 H MRS and MRSI of the human brain: Experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4459. [PMID: 33327042 PMCID: PMC8569948 DOI: 10.1002/nbm.4459] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 11/23/2020] [Indexed: 05/09/2023]
Abstract
The neurochemical information provided by proton magnetic resonance spectroscopy (MRS) or MR spectroscopic imaging (MRSI) can be severely compromised if strong signals originating from brain water and extracranial lipids are not properly suppressed. The authors of this paper present an overview of advanced water/lipid-suppression techniques and describe their advantages and disadvantages. Moreover, they provide recommendations for choosing the most appropriate techniques for proper use. Methods of water signal handling are primarily focused on the VAPOR technique and on MRS without water suppression (metabolite cycling). The section on lipid-suppression methods in MRSI is divided into three parts. First, lipid-suppression techniques that can be implemented on most clinical MR scanners (volume preselection, outer-volume suppression, selective lipid suppression) are described. Second, lipid-suppression techniques utilizing the combination of k-space filtering, high spatial resolutions and lipid regularization are presented. Finally, three promising new lipid-suppression techniques, which require special hardware (a multi-channel transmit system for dynamic B1+ shimming, a dedicated second-order gradient system or an outer volume crusher coil) are introduced.
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Affiliation(s)
- Ivan Tkáč
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Dinesh Deelchand
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Wolfgang Dreher
- Department of Chemistry, In vivo-MR Group, University Bremen, Bremen, Germany
| | - Hoby Hetherington
- Department of Radiology Magnetic Resonance Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Roland Kreis
- Departments of Radiology and Biomedical Research, University Bern, Bern, Switzerland
| | - Chathura Kumaragamage
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
| | - Michal Považan
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel M. Spielman
- Department of Radiology, Stanford University, Stanford, California, CA, USA
| | - Bernhard Strasser
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
| | - Robin A. de Graaf
- Department of Radiology and Biomedical Imaging, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, USA
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6
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Henriques RN, Palombo M, Jespersen SN, Shemesh N, Lundell H, Ianuş A. Double diffusion encoding and applications for biomedical imaging. J Neurosci Methods 2020; 348:108989. [PMID: 33144100 DOI: 10.1016/j.jneumeth.2020.108989] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/25/2020] [Accepted: 10/20/2020] [Indexed: 12/11/2022]
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.
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Affiliation(s)
- Rafael N Henriques
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Marco Palombo
- Centre for Medical Image Computing and Dept. of Computer Science, University College London, London, UK
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark
| | - Andrada Ianuş
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
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7
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Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
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8
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Wu D, Liu D, Hsu YC, Li H, Sun Y, Qin Q, Zhang Y. Diffusion-prepared 3D gradient spin-echo sequence for improved oscillating gradient diffusion MRI. Magn Reson Med 2020; 85:78-88. [PMID: 32643240 DOI: 10.1002/mrm.28401] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/03/2020] [Accepted: 06/07/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE Oscillating gradient (OG) enables the access of short diffusion times for time-dependent diffusion MRI (dMRI); however, it poses several technical challenges for clinical use. This study proposes a 3D oscillating gradient-prepared gradient spin-echo (OGprep-GRASE) sequence to improve SNR and shorten acquisition time for OG dMRI on clinical scanners. METHODS The 3D OGprep-GRASE sequence consisted of global saturation, diffusion encoding, fat saturation, and GRASE readout modules. Multiplexed sensitivity-encoding reconstruction was used to correct the phase errors between multiple shots. We compared the scan time and SNR of the proposed sequence and the conventional 2D-EPI sequence for OG dMRI at 30-90-mm slice coverage. We also examined the time-dependent diffusivity changes with OG dMRI acquired at frequencies of 50 Hz and 25 Hz and pulsed-gradient dMRI at diffusion times of 30 ms and 60 ms. RESULTS The OGprep-GRASE sequence reduced the scan time by a factor of 1.38, and increased the SNR by 1.74-2.27 times compared with 2D EPI for relatively thick slice coverage (60-90 mm). The SNR gain led to improved diffusion-tensor reconstruction in the multishot protocols. Image distortion in 2D-EPI images was also reduced in GRASE images. Diffusivity measurements from the pulsed-gradient dMRI and OG dMRI showed clear diffusion-time dependency in the white matter and gray matter of the human brain, using both the GRASE and EPI sequences. CONCLUSION The 3D OGprep-GRASE sequence improved scan time and SNR and reduced image distortion compared with the 2D multislice acquisition for OG dMRI on a 3T clinical system, which may facilitate the clinical translation of time-dependent dMRI.
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Affiliation(s)
- Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dapeng Liu
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Csenter for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Sun
- MR Collaboration, Siemens Healthcare China, Shanghai, China
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Csenter for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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9
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Dziadosz M, Bogner W, Kreis R. Non-water-excitation MR spectroscopy techniques to explore exchanging protons in human brain at 3 T. Magn Reson Med 2020; 84:2352-2363. [PMID: 32602971 DOI: 10.1002/mrm.28322] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/23/2020] [Accepted: 04/23/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop localization sequences for in vivo MR spectroscopy (MRS) on clinical scanners of 3 T to record spectra that are not influenced by magnetization transfer from water. METHODS Image-selected in vivo spectroscopy (ISIS) localization and chemical-shift-selective excitation (termed I-CSE) was combined in two ways: first, full ISIS localization plus a frequency-selective spin-echo and second, two-dimensional (2D) ISIS plus a frequency-selective excitation and slice-selective refocusing. The techniques were evaluated at 3 T in phantoms and human subjects in comparison to standard techniques with water presaturation or metabolite-cycling. ISIS included gradient-modulated offset-independent adiabatic (GOIA)-type adiabatic inversion pulses; echo times were 8-10 ms. RESULTS The novel 2D and 3D I-CSE methods yield upfield spectra that are comparable to those from standard MRS, except for shorter echo times and a limited frequency range. On the downfield/high-frequency side, they yield much more signal for exchangeable protons when compared to MRS with water presaturation or metabolite-cycling and longer echo times. CONCLUSION Novel non-water-excitation MRS sequences offer substantial benefits for the detection of metabolite signals that are otherwise suppressed by saturation transfer from water. Avoiding water saturation and using very short echo times allows direct observation of faster exchanging moieties than was previously possible at 3 T and additionally makes the methods less susceptible to fast T2 relaxation.
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Affiliation(s)
- Martyna Dziadosz
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Roland Kreis
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
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