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Hui SC, Zöllner HJ, Oeltzschner G, Edden RAE, Saleh MG. In vivo spectral editing of phosphorylethanolamine. Magn Reson Med 2022; 87:50-56. [PMID: 34411324 PMCID: PMC8616810 DOI: 10.1002/mrm.28976] [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: 02/15/2021] [Revised: 07/07/2021] [Accepted: 07/29/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To demonstrate J-difference editing of phosphorylethanolamine (PE) with chemical shifts at 3.22 (PE3.22 ) and 3.98 (PE3.98 ) ppm, and compare the merits of two editing strategies. METHODS Density-matrix simulations of MEGA-PRESS (Mescher-Garwood PRESS) for PE were performed at TEs ranging from 80 to 200 ms in steps of 2 ms, applying 20-ms editing pulses (ON/OFF) at (1) 3.98/7.5 ppm to detect PE3.22 and (2) 3.22/7.5 ppm to detect PE3.98 . Phantom experiments were performed using a PE phantom to validate simulation results. Ten subjects were scanned using a Philips 3T MRI scanner at TEs of 90 ms and 110 ms to edit PE3.22 and PE3.98 . Osprey was used for data processing, modeling, and quantification. RESULTS Simulations show substantial TE modulation of the intensity and shape of the edited signals due to coupling evolution. Simulated and phantom integrals suggest that TEs of 110 ms and 90 ms were optimal for the edited detection of PE3.22 and PE3.98 , respectively. Phantom results indicated strong agreement with the simulated spectra and integrals. In vivo quantification of the PE3.22 /total creatine and PE3.98 /total creatine concentration ratio yielded values of 0.26 ± 0.04 (between-subject coefficient of variation [CV]: 15.4%) and 0.18 ± 0.04 (CV: 22.8%), respectively, at TE = 90 ms, and 0.24 ± 0.02 (CV: 8.2%) and 0.23 ± 0.04 (CV: 18.0%), respectively, at TE = 110 ms. CONCLUSION Simulations and in vivo MEGA-PRESS of PE demonstrate that both PE3.22 and PE3.98 are potential candidates for editing, but PE3.22 at TE = 110 ms yields lower variation across TEs.
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Affiliation(s)
- Steve C.N. Hui
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Muhammad G. Saleh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, USA
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Zöllner HJ, Tapper S, Hui SCN, Barker PB, Edden RAE, Oeltzschner G. Comparison of linear combination modeling strategies for edited magnetic resonance spectroscopy at 3 T. NMR IN BIOMEDICINE 2022; 35:e4618. [PMID: 34558129 PMCID: PMC8935346 DOI: 10.1002/nbm.4618] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/24/2021] [Accepted: 08/29/2021] [Indexed: 06/01/2023]
Abstract
J-difference-edited spectroscopy is a valuable approach for the in vivo detection of γ-aminobutyric-acid (GABA) with magnetic resonance spectroscopy (MRS). A recent expert consensus article recommends linear combination modeling (LCM) of edited MRS but does not give specific details regarding implementation. This study explores different modeling strategies to adapt LCM for GABA-edited MRS. Sixty-one medial parietal lobe GABA-edited MEGA-PRESS spectra from a recent 3-T multisite study were modeled using 102 different strategies combining six different approaches to account for co-edited macromolecules (MMs), three modeling ranges, three baseline knot spacings, and the use of basis sets with or without homocarnosine. The resulting GABA and GABA+ estimates (quantified relative to total creatine), the residuals at different ranges, standard deviations and coefficients of variation (CVs), and Akaike information criteria, were used to evaluate the models' performance. Significantly different GABA+ and GABA estimates were found when a well-parameterized MM3co basis function was included in the model. The mean GABA estimates were significantly lower when modeling MM3co , while the CVs were similar. A sparser spline knot spacing led to lower variation in the GABA and GABA+ estimates, and a narrower modeling range-only including the signals of interest-did not substantially improve or degrade modeling performance. Additionally, the results suggest that LCM can separate GABA and the underlying co-edited MM3co . Incorporating homocarnosine into the modeling did not significantly improve variance in GABA+ estimates. In conclusion, GABA-edited MRS is most appropriately quantified by LCM with a well-parameterized co-edited MM3co basis function with a constraint to the nonoverlapped MM0.93 , in combination with a sparse spline knot spacing (0.55 ppm) and a modeling range of 0.5-4 ppm.
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Affiliation(s)
- Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Sofie Tapper
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Steve C. N. Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Peter B. Barker
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Hui SCN, Gong T, Zöllner HJ, Song Y, Murali-Manohar S, Oeltzschner G, Mikkelsen M, Tapper S, Chen Y, Saleh MG, Porges EC, Chen W, Wang G, Edden RAE. The macromolecular MR spectrum does not change with healthy aging. Magn Reson Med 2021; 87:1711-1719. [PMID: 34841564 DOI: 10.1002/mrm.29093] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/11/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. METHODS 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm3 , pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. RESULTS MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions. CONCLUSION Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.
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Affiliation(s)
- Steve C N Hui
- The 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
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Helge J Zöllner
- The 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
| | - Yulu Song
- The 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
| | - Saipavitra Murali-Manohar
- The 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
| | - Georg Oeltzschner
- The 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
| | - Mark Mikkelsen
- The 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.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sofie Tapper
- The 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
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Muhammad G Saleh
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Eric C Porges
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, Florida, USA.,McKnight Brain Research Foundation, University of Florida, Gainesville, Florida, USA.,Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | | | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Richard A E Edden
- The 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
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Riemann LT, Aigner CS, Ellison SLR, Brühl R, Mekle R, Schmitter S, Speck O, Rose G, Ittermann B, Fillmer A. Assessment of measurement precision in single-voxel spectroscopy at 7 T: Toward minimal detectable changes of metabolite concentrations in the human brain in vivo. Magn Reson Med 2021; 87:1119-1135. [PMID: 34783376 DOI: 10.1002/mrm.29034] [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: 05/01/2021] [Revised: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce a study design and statistical analysis framework to assess the repeatability, reproducibility, and minimal detectable changes (MDCs) of metabolite concentrations determined by in vivo MRS. METHODS An unbalanced nested study design was chosen to acquire in vivo MRS data within different repeatability and reproducibility scenarios. A spin-echo, full-intensity acquired localized (SPECIAL) sequence was employed at 7 T utlizing three different inversion pulses: a hyperbolic secant (HS), a gradient offset independent adiabaticity (GOIA), and a wideband, uniform rate, smooth truncation (WURST) pulse. Metabolite concentrations, Cramér-Rao lower bounds (CRLBs) and coefficients of variation (CVs) were calculated. Both Bland-Altman analysis and a restricted maximum-likelihood estimation (REML) analysis were performed to estimate the different variance contributions of the repeatability and reproducibility of the measured concentration. A Bland-Altmann analysis of the spectral shape was performed to assess the variance of the spectral shape, independent of quantification model influences. RESULTS For the used setup, minimal detectable changes of brain metabolite concentrations were found to be between 0.40 µmol/g and 2.23 µmol/g. CRLBs account for only 16 % to 74 % of the total variance of the metabolite concentrations. The application of gradient-modulated inversion pulses in SPECIAL led to slightly improved repeatability, but overall reproducibility appeared to be limited by differences in positioning, calibration, and other day-to-day variations throughout different sessions. CONCLUSION A framework is introduced to estimate the precision of metabolite concentrations obtained by MRS in vivo, and the minimal detectable changes for 13 metabolite concentrations measured at 7 T using SPECIAL are obtained.
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Affiliation(s)
| | | | | | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt, Braunschweig und Berlin, Germany
| | - Ralf Mekle
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Oliver Speck
- Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Germany.,Research Campus STIMULATE, Magdeburg, Germany
| | - Georg Rose
- Research Campus STIMULATE, Magdeburg, Germany.,Institut für Medizintechnik, Otto-von-Guericke University, Magdeburg, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt, Braunschweig und Berlin, Germany
| | - Ariane Fillmer
- Physikalisch-Technische Bundesanstalt, Braunschweig und Berlin, Germany
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55
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Shams Z, van der Kemp WJM, Emir U, Dankbaar JW, Snijders TJ, de Vos FYF, Klomp DWJ, Wijnen JP, Wiegers EC. Comparison of 2-Hydroxyglutarate Detection With sLASER and MEGA-sLASER at 7T. Front Neurol 2021; 12:718423. [PMID: 34557149 PMCID: PMC8452903 DOI: 10.3389/fneur.2021.718423] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/05/2021] [Indexed: 12/15/2022] Open
Abstract
The onco-metabolite 2-hydroxyglutarate (2HG), a biomarker of IDH-mutant gliomas, can be detected with 1H MR spectroscopy (1H-MRS). Recent studies showed measurements of 2HG at 7T with substantial gain in signal to noise ratio (SNR) and spectral resolution, offering higher specificity and sensitivity for 2HG detection. In this study, we assessed the sensitivity of semi-localized by adiabatic selective refocusing (sLASER) and J-difference MEsher-GArwood-semi-LASER (MEGA-sLASER) for 2HG detection at 7T. We performed spectral editing at long TE using a TE-optimized sLASER sequence (110 ms) and J-difference spectroscopy using MEGA-sLASER (TE = 74ms) in phantoms with different 2HG concentrations to assess the sensitivity of 2HG detection. The robustness of the methods against B0 inhomogeneity was investigated. Moreover, the performance of these two techniques was evaluated in four patients with IDH1-mutated glioma. In contrary to MEGA-sLASER, sLASER was able to detect 2HG concentration as low as 0.5 mM. In case of a composite phantom containing 2HG with overlapping metabolites, MEGA-sLASER provided a clean 2HG signal with higher fitting reliability (lower %CRLB). The results demonstrate that sLASER is more robust against field inhomogeneities and experimental or motion-related artifacts which promotes to adopt sLASER in clinical implementations.
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Affiliation(s)
- Zahra Shams
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Uzay Emir
- School of Health Sciences, Purdue University, West Lafayette, IN, United States
| | - Jan Willem Dankbaar
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tom J Snijders
- Department of Neurology & Neurosurgery, University Medical Center Utrecht/UMC Utrecht Brain Center, Utrecht, Netherlands
| | - Filip Y F de Vos
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Dennis W J Klomp
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jannie P Wijnen
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Evita C Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
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56
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Caldwell S, Rothman DL. 1H Magnetic Resonance Spectroscopy to Understand the Biological Basis of ALS, Diagnose Patients Earlier, and Monitor Disease Progression. Front Neurol 2021; 12:701170. [PMID: 34512519 PMCID: PMC8429815 DOI: 10.3389/fneur.2021.701170] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/03/2021] [Indexed: 11/13/2022] Open
Abstract
At present, limited biomarkers exist to reliably understand, diagnose, and monitor the progression of amyotrophic lateral sclerosis (ALS), a fatal neurological disease characterized by motor neuron death. Standard MRI technology can only be used to exclude a diagnosis of ALS, but 1H-MRS technology, which measures neurochemical composition, may provide the unique ability to reveal biomarkers that are specific to ALS and sensitive enough to diagnose patients at early stages in disease progression. In this review, we present a summary of current theories of how mitochondrial energetics and an altered glutamate/GABA neurotransmitter flux balance play a role in the pathogenesis of ALS. The theories are synthesized into a model that predicts how pathogenesis impacts glutamate and GABA concentrations. When compared with the results of all MRS studies published to date that measure the absolute concentrations of these neurochemicals in ALS patients, results were variable. However, when normalized for neuronal volume using the MRS biomarker N-acetyl aspartate (NAA), there is clear evidence for an elevation of neuronal glutamate in nine out of thirteen studies reviewed, an observation consistent with the predictions of the model of increased activity of glutamatergic neurons and excitotoxicity. We propose that this increase in neuronal glutamate concentration, in combination with decreased neuronal volume, is specific to the pathology of ALS. In addition, when normalized to glutamate levels, there is clear evidence for a decrease in neuronal GABA in three out of four possible studies reviewed, a finding consistent with a loss of inhibitory regulation contributing to excessive neuronal excitability. The combination of a decreased GABA/Glx ratio with an elevated Glx/NAA ratio may enhance the specificity for 1H-MRS detection of ALS and ability to monitor glutamatergic and GABAergic targeted therapeutics. Additional longitudinal studies calculating the exact value of these ratios are needed to test these hypotheses and understand how ratios may change over the course of disease progression. Proposed modifications to the experimental design of the reviewed 1H MRS studies may also increase the sensitivity of the technology to changes in these neurochemicals, particularly in early stages of disease progression.
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Affiliation(s)
- Sarah Caldwell
- Departments of Radiology and Biomedical Engineering, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, United States
| | - Douglas L Rothman
- Departments of Radiology and Biomedical Engineering, Magnetic Resonance Research Center, Yale University School of Medicine, New Haven, CT, United States
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Flatt E, McLin VA, Braissant O, Pierzchala K, Mastromarino P, Mitrea SO, Sessa D, Gruetter R, Cudalbu C. Probiotics combined with rifaximin influence the neurometabolic changes in a rat model of type C HE. Sci Rep 2021; 11:17988. [PMID: 34504135 PMCID: PMC8429411 DOI: 10.1038/s41598-021-97018-8] [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: 05/05/2021] [Accepted: 08/12/2021] [Indexed: 02/07/2023] Open
Abstract
Type C hepatic encephalopathy (HE) is a neuropsychiatric disease caused by chronic liver disease. Management of type C HE remains an important challenge because treatment options are limited. Both the antibiotic rifaximin and probiotics have been reported to reduce the symptoms of HE, but longitudinal studies assessing their effects on brain metabolism are lacking and the molecular mechanisms underpinning their effects are not fully understood. Therefore, we evaluated in detail the effects of these different treatments on the neurometabolic changes associated with type C HE using a multimodal approach including ultra-high field in vivo 1H MRS. We analyzed longitudinally the effect of rifaximin alone or in combination with the probiotic Vivomixx on the brain metabolic profile in the hippocampus and cerebellum of bile duct ligated (BDL) rats, an established model of type C HE. Overall, while rifaximin alone appeared to induce no significant effect on the neurometabolic profile of BDL rats, its association with the probiotic resulted in more attenuated neurometabolic alterations in BDL rats followed longitudinally (i.e. a smaller increase in Gln and milder decrease in Glu and Cr levels). Given that both rifaximin and some probiotics are used in the treatment of HE, the implications of these findings may be clinically relevant.
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Affiliation(s)
- Emmanuelle Flatt
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Valérie A McLin
- Swiss Pediatric Liver Center, Department of Pediatrics, Gynecology and Obstetrics, University Hospitals Geneva, University of Geneva, Geneva, Switzerland
| | - Olivier Braissant
- Service of Clinical Chemistry, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Katarzyna Pierzchala
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland.,Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Paola Mastromarino
- Section of Microbiology, Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | | | - Dario Sessa
- Swiss Pediatric Liver Center, Department of Pediatrics, Gynecology and Obstetrics, University Hospitals Geneva, University of Geneva, Geneva, Switzerland
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland. .,Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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Borbath T, Murali-Manohar S, Dorst J, Wright AM, Henning A. ProFit-1D-A 1D fitting software and open-source validation data sets. Magn Reson Med 2021; 86:2910-2929. [PMID: 34390031 DOI: 10.1002/mrm.28941] [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: 01/05/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/05/2022]
Abstract
PURPOSE Accurate and precise MRS fitting is crucial for metabolite concentration quantification of 1 H-MRS spectra. LCModel, a spectral fitting software, has shown to have certain limitations to perform advanced spectral fitting by previous literature. Herein, we propose an open-source spectral fitting algorithm with adaptive spectral baseline determination and more complex cost functions. THEORY The MRS spectra are characterized by several parameters, which reflect the environment of the contributing metabolites, properties of the acquisition sequence, or additional disturbances. Fitting parameters should accurately describe these parameters. Baselines are also a major contributor to MRS spectra, in which smoothness of the spline baselines used for fitting can be adjusted based on the properties of the spectra. Three different cost functions used for the minimization problem were also investigated. METHODS The newly developed ProFit-1D fitting algorithm is systematically evaluated for simulations of several types of possible in vivo parameter variations. Although accuracy and precision are tested with simulated spectra, spectra measured in vivo at 9.4 T are used for testing precision using subsets of averages. ProFit-1D fitting results are also compared with LCModel. RESULTS Both ProFit-1D and LCModel fitted the spectra well with induced parameter and baseline variations. ProFit-1D proved to be more accurate than LCModel for simulated spectra. However, LCModel showed a somewhat increased precision for some spectral simulations and for in vivo data. CONCLUSION The open-source ProFit-1D fitting algorithm demonstrated high accuracy while maintaining precise metabolite concentration quantification. Finally, through the newly proposed cost functions, new ways to improve fitting were shown.
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Affiliation(s)
- Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Johanna Dorst
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
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Wright AM, Murali-Manohar S, Borbath T, Avdievich NI, Henning A. Relaxation-corrected macromolecular model enables determination of 1 H longitudinal T 1 -relaxation times and concentrations of human brain metabolites at 9.4T. Magn Reson Med 2021; 87:33-49. [PMID: 34374449 DOI: 10.1002/mrm.28958] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Ultrahigh field MRS has improved characterization of the neurochemical profile. To compare results obtained at 9.4T to those from lower field strengths, it is of interest to quantify the concentrations of metabolites measured. Thus, measuring T1 -relaxation times is necessary to correct for T1 -weighting that occurs in acquisitions for single-voxel spectroscopy and spectroscopic imaging. A macromolecule (MM) simulation model was developed to fit MM contributions to the short TE inversion series used to measure T1 -relaxation times. METHODS An inversion series with seven time points was acquired with metabolite-cycled STEAM to estimate T1 -relaxation times of metabolites. A short TE was employed in this study to retain signals from metabolites with short T2 -relaxation times and J-couplings. The underlying macromolecule spectrum was corrected by developing a sequence-specific, relaxation-corrected simulated MM model. Quantification of metabolite peaks was performed using internal water referencing and relaxation corrections. RESULTS T1 -relaxation times for metabolites range from approximately 750 to approximately 2000 ms and approximately 1000 to approximately 2400 ms in gray matter (GM)- and white matter (WM)- rich voxels, respectively. Quantification of metabolites was compared between GM and WM voxels, as well as between results that used a simulated MM spectrum against those that used an experimentally acquired MM spectrum. Metabolite concentrations are reported in mmol/kg quantities. CONCLUSION T1 -relaxation times are reported for nonsinglet resonances for the first time at 9.4T by use of a MM simulation model to account for contributions from the MM spectrum. In addition to T1 -relaxation times, quantification results of metabolites from GM- and WM-rich voxels are reported.
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Affiliation(s)
- Andrew Martin Wright
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Nikolai I Avdievich
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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Cudalbu C, Bady P, Lai M, Xin L, Gusyatiner O, Hamou MF, Lepore M, Brouland JP, Daniel RT, Hottinger AF, Hegi ME. Metabolic and transcriptomic profiles of glioblastoma invasion revealed by comparisons between patients and corresponding orthotopic xenografts in mice. Acta Neuropathol Commun 2021; 9:133. [PMID: 34348785 PMCID: PMC8336020 DOI: 10.1186/s40478-021-01232-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
The invasive behavior of glioblastoma, the most aggressive primary brain tumor, is considered highly relevant for tumor recurrence. However, the invasion zone is difficult to visualize by Magnetic Resonance Imaging (MRI) and is protected by the blood brain barrier, posing a particular challenge for treatment. We report biological features of invasive growth accompanying tumor progression and invasion based on associated metabolic and transcriptomic changes observed in patient derived orthotopic xenografts (PDOX) in the mouse and the corresponding patients’ tumors. The evolution of metabolic changes, followed in vivo longitudinally by 1H Magnetic Resonance Spectroscopy (1H MRS) at ultra-high field, reflected growth and the invasive properties of the human glioblastoma transplanted into the brains of mice (PDOX). Comparison of MRS derived metabolite signatures, reflecting temporal changes of tumor development and invasion in PDOX, revealed high similarity to spatial metabolite signatures of combined multi-voxel MRS analyses sampled across different areas of the patients’ tumors. Pathway analyses of the transcriptome associated with the metabolite profiles of the PDOX, identified molecular signatures of invasion, comprising extracellular matrix degradation and reorganization, growth factor binding, and vascular remodeling. Specific analysis of expression signatures from the invaded mouse brain, revealed extent of invasion dependent induction of immune response, recapitulating respective signatures observed in glioblastoma. Integrating metabolic profiles and gene expression of highly invasive PDOX provided insights into progression and invasion associated mechanisms of extracellular matrix remodeling that is essential for cell–cell communication and regulation of cellular processes. Structural changes and biochemical properties of the extracellular matrix are of importance for the biological behavior of tumors and may be druggable. Ultra-high field MRS reveals to be suitable for in vivo monitoring of progression in the non-enhancing infiltration zone of glioblastoma.
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Simicic D, Rackayova V, Xin L, Tkáč I, Borbath T, Starcuk Z, Starcukova J, Lanz B, Cudalbu C. In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T 2 relaxation times. Magn Reson Med 2021; 86:2384-2401. [PMID: 34268821 PMCID: PMC8596437 DOI: 10.1002/mrm.28910] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/25/2021] [Accepted: 06/11/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) 1 H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T2 relaxation times for seven MM components. METHODS A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo 1 H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel. RESULTS A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T2 ranged between 12 and 24 ms for seven MM peaks. CONCLUSION Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.
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Affiliation(s)
- Dunja Simicic
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland.,Laboratory for functional and metabolic imaging (LIFMET), EPFL, Lausanne, Switzerland
| | - Veronika Rackayova
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland
| | - Lijing Xin
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Zenon Starcuk
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic
| | - Jana Starcukova
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic
| | - Bernard Lanz
- Laboratory for functional and metabolic imaging (LIFMET), EPFL, Lausanne, Switzerland
| | - Cristina Cudalbu
- CIBM Center for Biomedical Imaging, Switzerland.,Animal Imaging and Technology, EPFL, Lausanne, Switzerland
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62
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Near J, Harris AD, Juchem C, Kreis R, Marjańska M, Öz G, Slotboom J, Wilson M, Gasparovic C. Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4257. [PMID: 32084297 PMCID: PMC7442593 DOI: 10.1002/nbm.4257] [Citation(s) in RCA: 153] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/21/2019] [Accepted: 12/22/2019] [Indexed: 05/05/2023]
Abstract
Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step.
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Affiliation(s)
- Jamie Near
- Douglas Mental Health University Institute and Department of Psychiatry, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada
| | - Ashley D. Harris
- Department of Radiology, University of Calgary, Calgary, Canada
- Alberta Children’s Hospital Research Institute, Calgary, Canada
- Hotchkiss Brain Institute, Calgary, Canada
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University, New York NY, USA
| | - Roland Kreis
- Departments of Radiology and Biomedical Research, University Bern, Switzerland
| | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
| | - Gülin Öz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis MN, USA
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging (SCAN), Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Martin Wilson
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, England
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63
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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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Lin A, Andronesi O, Bogner W, Choi I, Coello E, Cudalbu C, Juchem C, Kemp GJ, Kreis R, Krššák M, Lee P, Maudsley AA, Meyerspeer M, Mlynarik V, Near J, Öz G, Peek AL, Puts NA, Ratai E, Tkáč I, Mullins PG. Minimum Reporting Standards for in vivo Magnetic Resonance Spectroscopy (MRSinMRS): Experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4484. [PMID: 33559967 PMCID: PMC8647919 DOI: 10.1002/nbm.4484] [Citation(s) in RCA: 115] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 11/24/2020] [Accepted: 01/12/2021] [Indexed: 05/08/2023]
Abstract
The translation of MRS to clinical practice has been impeded by the lack of technical standardization. There are multiple methods of acquisition, post-processing, and analysis whose details greatly impact the interpretation of the results. These details are often not fully reported, making it difficult to assess MRS studies on a standardized basis. This hampers the reviewing of manuscripts, limits the reproducibility of study results, and complicates meta-analysis of the literature. In this paper a consensus group of MRS experts provides minimum guidelines for the reporting of MRS methods and results, including the standardized description of MRS hardware, data acquisition, analysis, and quality assessment. This consensus statement describes each of these requirements in detail and includes a checklist to assist authors and journal reviewers and to provide a practical way for journal editors to ensure that MRS studies are reported in full.
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Affiliation(s)
- Alexander Lin
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Ovidiu Andronesi
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
| | - In‐Young Choi
- Department of Neurology, Hoglund Biomedical Imaging CenterUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Eduardo Coello
- Center for Clinical Spectroscopy, Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Cristina Cudalbu
- Center for Biomedical Imaging (CIBM), Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Christoph Juchem
- Departments of Biomedical Engineering and RadiologyColumbia UniversityNew YorkNew YorkUSA
| | - Graham J. Kemp
- Department of Musculoskeletal and Ageing Science and Liverpool Magnetic Resonance Imaging Centre (LiMRIC)University of LiverpoolLiverpoolUK
| | - Roland Kreis
- Departments of Radiology and Biomedical ResearchUniversity of BernBernSwitzerland
| | - Martin Krššák
- Department of Medicine III and Department of Biomedical Imaging and Image guided TherapyMedical University of ViennaViennaAustria
| | - Phil Lee
- Department of Radiology, Hoglund Biomedical Imaging CenterUniversity of Kansas Medical CenterKansas CityKansasUSA
| | | | - Martin Meyerspeer
- High Field MR Center, Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
| | - Vladamir Mlynarik
- Magnetic Resonance Centre of Excellence. Medical University of ViennaViennaAustria
| | - Jamie Near
- Brain Imaging Centre, Douglas Research Centre, Department of PsychiatryMcGill UniversityMontrealQuebecCanada
| | - Gülin Öz
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Aimie L. Peek
- Faculty of Health SciencesUniversity of SydneySydneyAustralia
| | - Nicolaas A. Puts
- Department of Forensic and Neurodevelopmental SciencesSackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College LondonLondonUK
| | - Eva‐Maria Ratai
- A.A. Martinos Center for Biomedical Imaging, Neuroradiology Division, Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Ivan Tkáč
- Faculty of Health SciencesUniversity of SydneySydneyAustralia
| | - Paul G. Mullins
- Bangor Imaging Unit, School of PsychologyBangor UniversityBangorGwyneddUK
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65
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Ip IB, Bridge H. Investigating the neurochemistry of the human visual system using magnetic resonance spectroscopy. Brain Struct Funct 2021; 227:1491-1505. [PMID: 33900453 PMCID: PMC9046312 DOI: 10.1007/s00429-021-02273-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/09/2021] [Indexed: 11/29/2022]
Abstract
Biochemical processes underpin the structure and function of the visual cortex, yet our understanding of the fundamental neurochemistry of the visual brain is incomplete. Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive brain imaging tool that allows chemical quantification of living tissue by detecting minute differences in the resonant frequency of molecules. Application of MRS in the human brain in vivo has advanced our understanding of how the visual brain consumes energy to support neural function, how its neural substrates change as a result of disease or dysfunction, and how neural populations signal during perception and plasticity. The aim of this review is to provide an entry point to researchers interested in investigating the neurochemistry of the visual system using in vivo measurements. We provide a basic overview of MRS principles, and then discuss recent findings in four topics of vision science: (i) visual perception, plasticity in the (ii) healthy and (iii) dysfunctional visual system, and (iv) during visual stimulation. Taken together, evidence suggests that the neurochemistry of the visual system provides important novel insights into how we perceive the world.
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Affiliation(s)
- I Betina Ip
- Wellcome Centre for Integrative Neuroimaging, FMRIB Building, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Holly Bridge
- Wellcome Centre for Integrative Neuroimaging, FMRIB Building, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK.
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Zöllner HJ, Považan M, Hui SC, Tapper S, Edden RA, Oeltzschner G. Comparison of different linear-combination modeling algorithms for short-TE proton spectra. NMR IN BIOMEDICINE 2021; 34:e4482. [PMID: 33530131 PMCID: PMC8935349 DOI: 10.1002/nbm.4482] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/09/2021] [Indexed: 05/08/2023]
Abstract
Short-TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as a linear combination of metabolite basis spectra. This large-scale multi-site study compares the levels of the four major metabolite complexes in short-TE spectra estimated by three linear-combination modeling (LCM) algorithms. 277 medial parietal lobe short-TE PRESS spectra (TE = 35 ms) from a recent 3 T multi-site study were preprocessed with the Osprey software. The resulting spectra were modeled with Osprey, Tarquin and LCModel, using the same three vendor-specific basis sets (GE, Philips and Siemens) for each algorithm. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (mI) and glutamate + glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and coefficient of variations of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated as lower by Tarquin. The cohort mean coefficient of determination for all pairs of LCM algorithms across all datasets and metabolites was R 2 ¯ = 0.39, indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean R 2 ¯ = 0.10). While mean estimates of major metabolite complexes broadly agree between linear-combination modeling algorithms at group level, correlations between algorithms are only weak-to-moderate, despite standardized preprocessing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.
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Affiliation(s)
- Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Michal Považan
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Steve C.N. Hui
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Sofie Tapper
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Richard A.E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Gajdošík M, Landheer K, Swanberg KM, Juchem C. INSPECTOR: free software for magnetic resonance spectroscopy data inspection, processing, simulation and analysis. Sci Rep 2021; 11:2094. [PMID: 33483543 PMCID: PMC7822873 DOI: 10.1038/s41598-021-81193-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 12/28/2020] [Indexed: 12/03/2022] Open
Abstract
In vivo magnetic resonance spectroscopy (MRS) is a powerful tool for biomedical research and clinical diagnostics, allowing for non-invasive measurement and analysis of small molecules from living tissues. However, currently available MRS processing and analytical software tools are limited in their potential for in-depth quality management, access to details of the processing stream, and user friendliness. Moreover, available MRS software focuses on selected aspects of MRS such as simulation, signal processing or analysis, necessitating the use of multiple packages and interfacing among them for biomedical applications. The freeware INSPECTOR comprises enhanced MRS data processing, simulation and analytical capabilities in a one-stop-shop solution for a wide range of biomedical research and diagnostic applications. Extensive data handling, quality management and visualization options are built in, enabling the assessment of every step of the processing chain with maximum transparency. The parameters of the processing can be flexibly chosen and tailored for the specific research problem, and extended confidence information is provided with the analysis. The INSPECTOR software stands out in its user-friendly workflow and potential for automation. In addition to convenience, the functionalities of INSPECTOR ensure rigorous and consistent data processing throughout multi-experiment and multi-center studies.
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Affiliation(s)
- Martin Gajdošík
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 3227 Broadway, New York, NY, 10027, USA.
| | - Karl Landheer
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 3227 Broadway, New York, NY, 10027, USA
| | - Kelley M Swanberg
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 3227 Broadway, New York, NY, 10027, USA
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 3227 Broadway, New York, NY, 10027, USA
- Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
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68
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Hoefemann M, Döring A, Fichtner ND, Kreis R. Combining chemical exchange saturation transfer and 1 H magnetic resonance spectroscopy for simultaneous determination of metabolite concentrations and effects of magnetization exchange. Magn Reson Med 2020; 85:1766-1782. [PMID: 33151011 PMCID: PMC7821128 DOI: 10.1002/mrm.28574] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 10/06/2020] [Accepted: 10/07/2020] [Indexed: 12/21/2022]
Abstract
Purpose A new sequence combining chemical‐exchange saturation‐transfer (CEST) with traditional MRS is used to simultaneously determine metabolite content and effects of magnetization exchange. Methods A CEST saturation block consisting of a train of RF‐pulses is placed before a metabolite‐cycled semi‐LASER single‐voxel spectroscopy sequence. The saturation parameters are adjustable to allow optimization of the saturation for a specific target. Data were collected in brain from 20 subjects in experiments with different B1‐settings (0.4‐2.0 µT) on a 3T MR scanner. CEST Z‐spectra were calculated from water intensities and fitted with a multi‐pool Lorentzian model. Interrelated metabolite spectra were fitted in fitting tool for arrays of interrelated datasets (FiTAID). Results Evaluation of traditional Z‐spectra from water revealed exchange effects from amides, amines, and hydroxyls as well as an upfield nuclear Overhauser effect. The magnetization transfer effect was evaluated on metabolites and macromolecules for the whole spectral range and for the different B1 levels. A correction scheme for direct saturation on metabolites is proposed. Both magnetization‐transfer and direct saturation proved to differ for individual metabolites. Conclusion Using non‐water‐suppressed spectroscopy offers time‐saving simultaneous recording of the traditional CEST Z‐spectrum from water and the metabolite spectrum under frequency‐selective saturation. In addition, exchange and magnetization‐transfer effects on metabolites and macromolecules can be detected, which might offer additional possibilities for quantification or give further insight into the composition of the traditional CEST Z‐spectrum. Apparent magnetization‐transfer effects on macromolecular signals in the 1H‐MR spectrum have been found. Detailed knowledge of magnetization‐transfer effects is also relevant for judging the influence of water‐suppression on the quantification of metabolite signals.
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Affiliation(s)
- Maike Hoefemann
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - André Döring
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.,Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Nicole Damara Fichtner
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.,University of British Columbia, Vancouver, Canada
| | - Roland Kreis
- Departments of Radiology and Biomedical Research, University of Bern, Bern, Switzerland
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69
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Hangel G, Cadrien C, Lazen P, Furtner J, Lipka A, Hečková E, Hingerl L, Motyka S, Gruber S, Strasser B, Kiesel B, Mischkulnig M, Preusser M, Roetzer T, Wöhrer A, Widhalm G, Rössler K, Trattnig S, Bogner W. High-resolution metabolic imaging of high-grade gliomas using 7T-CRT-FID-MRSI. Neuroimage Clin 2020; 28:102433. [PMID: 32977210 PMCID: PMC7511769 DOI: 10.1016/j.nicl.2020.102433] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVES Successful neurosurgical intervention in gliomas depends on the precision of the preoperative definition of the tumor and its margins since a safe maximum resection translates into a better patient outcome. Metabolic high-resolution imaging might result in improved presurgical tumor characterization, and thus optimized glioma resection. To this end, we validated the performance of a fast high-resolution whole-brain 3D-magnetic resonance spectroscopic imaging (MRSI) method at 7T in a patient cohort of 23 high-grade gliomas (HGG). MATERIALS AND METHODS We preoperatively measured 23 patients with histologically verified HGGs (17 male, 8 female, age 53 ± 15) with an MRSI sequence based on concentric ring trajectories with a 64 × 64 × 39 measurement matrix, and a 3.4 × 3.4 × 3.4 mm3 nominal voxel volume in 15 min. Quantification used a basis-set of 17 components including N-acetyl-aspartate (NAA), total choline (tCho), total creatine (tCr), glutamate (Glu), glutamine (Gln), glycine (Gly) and 2-hydroxyglutarate (2HG). The resultant metabolic images were evaluated for their reliability as well as their quality and compared to spatially segmented tumor regions-of-interest (necrosis, contrast-enhanced, non-contrast enhanced + edema, peritumoral) based on clinical data and also compared to histopathology (e.g., grade, IDH-status). RESULTS Eighteen of the patient measurements were considered usable. In these patients, ten metabolites were quantified with acceptable quality. Gln, Gly, and tCho were increased and NAA and tCr decreased in nearly all tumor regions, with other metabolites such as serine, showing mixed trends. Overall, there was a reliable characterization of metabolic tumor areas. We also found heterogeneity in the metabolic images often continued into the peritumoral region. While 2HG could not be satisfyingly quantified, we found an increase of Glu in the contrast-enhancing region of IDH-wildtype HGGs and a decrease of Glu in IDH1-mutant HGGs. CONCLUSIONS We successfully demonstrated high-resolution 7T 3D-MRSI in HGG patients, showing metabolic differences between tumor regions and peritumoral tissue for multiple metabolites. Increases of tCho, Gln (related to tumor metabolism), Gly (related to tumor proliferation), as well as decreases in NAA, tCr, and others, corresponded very well to clinical tumor segmentation, but were more heterogeneous and often extended into the peritumoral region.
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Affiliation(s)
- Gilbert Hangel
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.
| | - Cornelius Cadrien
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Philipp Lazen
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Julia Furtner
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Alexandra Lipka
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Eva Hečková
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Hingerl
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stanislav Motyka
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Stephan Gruber
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Bernhard Strasser
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Barbara Kiesel
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Mario Mischkulnig
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Matthias Preusser
- Division of Oncology, Department of Inner Medicine I, Medical University of Vienna, Vienna, Austria
| | - Thomas Roetzer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Adelheid Wöhrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Karl Rössler
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Siegfried Trattnig
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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70
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Murali-Manohar S, Wright AM, Borbath T, Avdievich NI, Henning A. A novel method to measure T 1 -relaxation times of macromolecules and quantification of the macromolecular resonances. Magn Reson Med 2020; 85:601-614. [PMID: 32864826 DOI: 10.1002/mrm.28484] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/29/2020] [Accepted: 07/29/2020] [Indexed: 01/29/2023]
Abstract
PURPOSE Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T1 relaxation times among individual MM peaks must be considered. Characterization of T1 -relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T1 -relaxation times of individual MM peaks is reported in this work. METHODS Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR-metabolite-cycled semi-LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T1 -relaxation times. RESULTS T1 -relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray- and white-matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported. CONCLUSION A novel DIR method is reported in this work to calculate T1 -relaxation times of MMs in the human brain. T1 -relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray- and white-matter rich voxels for the first time at 9.4T.
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Affiliation(s)
- Saipavitra Murali-Manohar
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Andrew Martin Wright
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive & Systems Neuroscience, Tübingen, Germany
| | - Tamas Borbath
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Faculty of Science, University of Tübingen, Tübingen, Germany
| | - Nikolai I Avdievich
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
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71
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Lanz B, Abaei A, Braissant O, Choi IY, Cudalbu C, Henry PG, Gruetter R, Kara F, Kantarci K, Lee P, Lutz NW, Marjańska M, Mlynárik V, Rasche V, Xin L, Valette J. Magnetic resonance spectroscopy in the rodent brain: Experts' consensus recommendations. NMR IN BIOMEDICINE 2020; 34:e4325. [PMID: 33565219 PMCID: PMC9429976 DOI: 10.1002/nbm.4325] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 03/29/2020] [Accepted: 04/30/2020] [Indexed: 05/21/2023]
Abstract
In vivo MRS is a non-invasive measurement technique used not only in humans, but also in animal models using high-field magnets. MRS enables the measurement of metabolite concentrations as well as metabolic rates and their modifications in healthy animals and disease models. Such data open the way to a deeper understanding of the underlying biochemistry, related disturbances and mechanisms taking place during or prior to symptoms and tissue changes. In this work, we focus on the main preclinical 1H, 31P and 13C MRS approaches to study brain metabolism in rodent models, with the aim of providing general experts' consensus recommendations (animal models, anesthesia, data acquisition protocols). An overview of the main practical differences in preclinical compared with clinical MRS studies is presented, as well as the additional biochemical information that can be obtained in animal models in terms of metabolite concentrations and metabolic flux measurements. The properties of high-field preclinical MRS and the technical limitations are also described.
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Affiliation(s)
- Bernard Lanz
- Laboratory for Functional and Metabolic Imaging (LIFMET), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Alireza Abaei
- Core Facility Small Animal Imaging, Ulm University, Ulm, Germany
| | - Olivier Braissant
- Service of Clinical Chemistry, University of Lausanne and University Hospital of Lausanne, Lausanne, Switzerland
| | - In-Young Choi
- Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, US
| | - Cristina Cudalbu
- Centre d’Imagerie Biomedicale (CIBM), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, US
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging (LIFMET), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Firat Kara
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, US
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, US
| | - Phil Lee
- Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas, US
| | | | - Małgorzata Marjańska
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, US
| | - Vladimír Mlynárik
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Volker Rasche
- Core Facility Small Animal Imaging, Ulm University, Ulm, Germany
| | - Lijing Xin
- Centre d’Imagerie Biomedicale (CIBM), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Julien Valette
- Commissariat à l’Energie Atomique et aux Energies Alternatives, MIRCen, Fontenay-aux-Roses, France
- Neurodegenerative Diseases Laboratory, Centre National de la Recherche Scientifique, Université Paris-Sud, Université Paris-Saclay, UMR 9199, Fontenay-aux-Roses, France
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72
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Kreis R, Boer V, Choi I, Cudalbu C, de Graaf RA, Gasparovic C, Heerschap A, Krššák M, Lanz B, Maudsley AA, Meyerspeer M, Near J, Öz G, Posse S, Slotboom J, Terpstra M, Tkáč I, Wilson M, Bogner W. Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts' consensus recommendations. NMR IN BIOMEDICINE 2020; 34:e4347. [PMID: 32808407 PMCID: PMC7887137 DOI: 10.1002/nbm.4347] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 05/20/2020] [Accepted: 05/21/2020] [Indexed: 05/04/2023]
Abstract
With a 40-year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.
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Affiliation(s)
- Roland Kreis
- Department of Radiology, Neuroradiology, and Nuclear Medicine and Department of Biomedical ResearchUniversity BernBernSwitzerland
| | - Vincent Boer
- Danish Research Centre for Magnetic Resonance, Funktions‐ og Billeddiagnostisk EnhedCopenhagen University Hospital HvidovreHvidovreDenmark
| | - In‐Young Choi
- Department of Neurology, Hoglund Brain Imaging CenterUniversity of Kansas Medical CenterKansas CityKansasUSA
| | - Cristina Cudalbu
- Centre d'Imagerie Biomedicale (CIBM)Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Robin A. de Graaf
- Department of Radiology and Biomedical Imaging & Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA
| | | | - Arend Heerschap
- Department of Radiology and Nuclear MedicineRadboud University Medical CenterNijmegenThe Netherlands
| | - Martin Krššák
- Division of Endocrinology and Metabolism, Department of Internal Medicine III & High Field MR Centre, Department of Biomedical Imaging and Image guided TherapyMedical University of ViennaViennaAustria
| | - Bernard Lanz
- Laboratory of Functional and Metabolic Imaging (LIFMET)Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew A. Maudsley
- Department of Radiology, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Martin Meyerspeer
- Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
- High Field MR CenterMedical University of ViennaViennaAustria
| | - Jamie Near
- Douglas Mental Health University Institute and Department of PsychiatryMcGill UniversityMontrealCanada
| | - Gülin Öz
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Stefan Posse
- Department of NeurologyUniversity of New Mexico School of MedicineAlbuquerqueNew MexicoUSA
| | - Johannes Slotboom
- Department of Radiology, Neuroradiology, and Nuclear MedicineUniversity Hospital BernBernSwitzerland
| | - Melissa Terpstra
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, Department of RadiologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Martin Wilson
- Centre for Human Brain Health and School of PsychologyUniversity of BirminghamBirminghamUK
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image‐guided TherapyMedical University of ViennaViennaAustria
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73
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Öz G, Deelchand DK, Wijnen JP, Mlynárik V, Xin L, Mekle R, Noeske R, Scheenen TWJ, Tkáč I. Advanced single voxel 1 H magnetic resonance spectroscopy techniques in humans: Experts' consensus recommendations. NMR IN BIOMEDICINE 2020; 34:e4236. [PMID: 31922301 PMCID: PMC7347431 DOI: 10.1002/nbm.4236] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 10/29/2019] [Accepted: 11/07/2019] [Indexed: 05/06/2023]
Abstract
Conventional proton MRS has been successfully utilized to noninvasively assess tissue biochemistry in conditions that result in large changes in metabolite levels. For more challenging applications, namely, in conditions which result in subtle metabolite changes, the limitations of vendor-provided MRS protocols are increasingly recognized, especially when used at high fields (≥3 T) where chemical shift displacement errors, B0 and B1 inhomogeneities and limitations in the transmit B1 field become prominent. To overcome the limitations of conventional MRS protocols at 3 and 7 T, the use of advanced MRS methodology, including pulse sequences and adjustment procedures, is recommended. Specifically, the semiadiabatic LASER sequence is recommended when TE values of 25-30 ms are acceptable, and the semiadiabatic SPECIAL sequence is suggested as an alternative when shorter TE values are critical. The magnetic field B0 homogeneity should be optimized and RF pulses should be calibrated for each voxel. Unsuppressed water signal should be acquired for eddy current correction and preferably also for metabolite quantification. Metabolite and water data should be saved in single shots to facilitate phase and frequency alignment and to exclude motion-corrupted shots. Final averaged spectra should be evaluated for SNR, linewidth, water suppression efficiency and the presence of unwanted coherences. Spectra that do not fit predefined quality criteria should be excluded from further analysis. Commercially available tools to acquire all data in consistent anatomical locations are recommended for voxel prescriptions, in particular in longitudinal studies. To enable the larger MRS community to take advantage of these advanced methods, a list of resources for these advanced protocols on the major clinical platforms is provided. Finally, a set of recommendations are provided for vendors to enable development of advanced MRS on standard platforms, including implementation of advanced localization sequences, tools for quality assurance on the scanner, and tools for prospective volume tracking and dynamic linear shim corrections.
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Affiliation(s)
- Gülin Öz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Dinesh K. Deelchand
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jannie P. Wijnen
- High field MR Research group, Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Vladimír Mlynárik
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lijing Xin
- Animal Imaging and Technology Core (AIT), Center for Biomedical Imaging (CIBM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ralf Mekle
- Center for Stroke Research Berlin (CSB), Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | - Tom W. J. Scheenen
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
- Erwin L Hahn Institute for Magnetic Resonance Imaging, UNESCO World Cultural Heritage Zollverein, Essen, Germany
| | - Ivan Tkáč
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
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