1
|
Hangel G, Spurny-Dworak B, Lazen P, Cadrien C, Sharma S, Hingerl L, Hečková E, Strasser B, Motyka S, Lipka A, Gruber S, Brandner C, Lanzenberger R, Rössler K, Trattnig S, Bogner W. Inter-subject stability and regional concentration estimates of 3D-FID-MRSI in the human brain at 7 T. NMR IN BIOMEDICINE 2021; 34:e4596. [PMID: 34382280 DOI: 10.1002/nbm.4596] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 07/14/2021] [Accepted: 07/14/2021] [Indexed: 05/13/2023]
Abstract
PURPOSE Recently, a 3D-concentric ring trajectory (CRT)-based free induction decay (FID)-MRSI sequence was introduced for fast high-resolution metabolic imaging at 7 T. This technique provides metabolic ratio maps of almost the entire brain within clinically feasible scan times, but its robustness has not yet been thoroughly investigated. Therefore, we have assessed quantitative concentration estimates and their variability in healthy volunteers using this approach. METHODS We acquired whole-brain 3D-CRT-FID-MRSI at 7 T in 15 min with 3.4 mm nominal isometric resolution in 24 volunteers (12 male, 12 female, mean age 27 ± 6 years). Concentration estimate maps were calculated for 15 metabolites using internal water referencing and evaluated in 55 different regions of interest (ROIs) in the brain. Data quality, mean metabolite concentrations, and their inter-subject coefficients of variation (CVs) were compared for all ROIs. RESULTS Of 24 datasets, one was excluded due to motion artifacts. The concentrations of total choline, total creatine, glutamate, myo-inositol, and N-acetylaspartate in 44 regions were estimated within quality thresholds. Inter-subject CVs (mean over 44 ROIs/minimum/maximum) were 9%/5%/19% for total choline, 10%/6%/20% for total creatine, 11%/7%/24% for glutamate, 10%/6%/19% for myo-inositol, and 9%/6%/19% for N-acetylaspartate. DISCUSSION We defined the performance of 3D-CRT-based FID-MRSI for metabolite concentration estimate mapping, showing which metabolites could be robustly quantified in which ROIs with which inter-subject CVs expected. However, the basal brain regions and lesser-signal metabolites in particular remain as a challenge due susceptibility effects from the proximity to nasal and auditory cavities. Further improvement in quantification and the mitigation of B0 /B1 -field inhomogeneities will be necessary to achieve reliable whole-brain coverage.
Collapse
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
| | - Benjamin Spurny-Dworak
- Division of General Psychiatry, Department of Psychiatry and Psychotherapy, 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
| | - 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
| | - Sukrit Sharma
- 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
| | - Eva Hečková
- 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
| | - Stanislav Motyka
- High-field MR Center, 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
- Institute for Clinical Molecular MRI, Karl Landsteiner Society, St. Pölten, Austria
| | - Stephan Gruber
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christoph Brandner
- High-field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Division of General Psychiatry, Department of Psychiatry and Psychotherapy, 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
- Institute for Clinical Molecular MRI, Karl Landsteiner Society, St. Pölten, Austria
| | - Wolfgang Bogner
- High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
2
|
Yan F, He N, Lin H, Li R. Iron deposition quantification: Applications in the brain and liver. J Magn Reson Imaging 2018; 48:301-317. [PMID: 29897645 DOI: 10.1002/jmri.26161] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/02/2018] [Indexed: 01/01/2023] Open
Abstract
Iron has long been implicated in many neurological and other organ diseases. It is known that over and above the normal increases in iron with age, in certain diseases there is an excessive iron accumulation in the brain and liver. MRI is a noninvasive means by which to image the various structures in the brain in three dimensions and quantify iron over the volume of the object of interest. The quantification of iron can provide information about the severity of iron-related diseases as well as quantify changes in iron for patient follow-up and treatment monitoring. This article provides an overview of current MRI-based methods for iron quantification, specifically for the brain and liver, including: signal intensity ratio, R2 , R2*, R2', phase, susceptibility weighted imaging and quantitative susceptibility mapping (QSM). Although there are numerous approaches to measuring iron, R2 and R2* are currently preferred methods in imaging the liver and QSM has become the preferred approach for imaging iron in the brain. LEVEL OF EVIDENCE 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018. J. MAGN. RESON. IMAGING 2018;48:301-317.
Collapse
Affiliation(s)
- Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
3
|
Lin H, Fu C, Kannengiesser S, Cheng S, Shen J, Dong H, Yan F. Quantitative analysis of hepatic iron in patients suspected of coexisting iron overload and steatosis using multi-echo single-voxel magnetic resonance spectroscopy: Comparison with fat-saturated multi-echo gradient echo sequence. J Magn Reson Imaging 2018. [PMID: 29513377 DOI: 10.1002/jmri.25967] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The coexistence of hepatic iron and fat is common in patients with hyperferritinemia, which plays an interactive and aggressive role in the progression of diseases (fibrosis, cirrhosis, and hepatocellular carcinomas). PURPOSE To evaluate a modified high-speed T2 -corrected multi-echo, single voxel spectroscopy sequence (HISTOV) for liver iron concentration (LIC) quantification in patients with hyperferritinemia, with simultaneous fat fraction (FF) estimation. STUDY TYPE Retrospective cohort study. POPULATION Thirty-eight patients with hyperferritinemia were enrolled. FIELD STRENGTH/SEQUENCE HISTOV, a fat-saturated multi-echo gradient echo (GRE) sequence, and a spin echo sequence (FerriScan) were performed at 1.5T. ASSESSMENT R2 of the water signal and FF were calculated with HISTOV, and R2* values were derived from the GRE sequence, with R2 and LIC from FerriScan serving as the references. STATISTICAL TESTS Linear regression, correlation analyses, receiver operating characteristic analyses, and Bland-Altman analyses were conducted. RESULTS Abnormal hepatic iron load was detected in 32/38 patients, of whom 10/32 had coexisting steatosis. Strong correlation was found between R2* and FerriScan-LIC (R2 = 0.861), and between HISTOV-R2_ water and FerriScan-R2 (R2 = 0.889). Furthermore, HISTOV-R2_ water was not correlated with HISTOV-FF. The area under the curve (AUC) for HISTOV-R2_ water was 0.974, 0.971, and 1, corresponding to clinical FerriScan-LIC thresholds of 1.8, 3.2, and 7.0 mg/g dw, respectively. No significant difference in the AUC was found between HISTOV-R2_ water and R2* at any of the LIC thresholds, with P-values of 0.42, 0.37, and 1, respectively. HISTOV-LIC showed excellent agreement with FerriScan-LIC, with a mean bias of 0.00 ± 1.18 mg/g dw, whereas the mean bias between GRE-LIC and FerriScan-LIC was 0.53 ± 1.49 mg/g dw. DATA CONCLUSION HISTOV is useful for the quantification and grading of liver iron overload in patients with hyperferritinemia, particularly in cases with coexisting steatosis. HISTOV-LIC showed no systematic bias compared with FerriScan-LIC, making it a promising alternative for iron quantification. LEVEL OF EVIDENCE 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2018.
Collapse
Affiliation(s)
- Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Caixia Fu
- Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | | | - Shu Cheng
- Department of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Shen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haipeng Dong
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
4
|
Becerra L, Veggeberg R, Prescot A, Jensen JE, Renshaw P, Scrivani S, Spierings ELH, Burstein R, Borsook D. A 'complex' of brain metabolites distinguish altered chemistry in the cingulate cortex of episodic migraine patients. NEUROIMAGE-CLINICAL 2016; 11:588-594. [PMID: 27158591 PMCID: PMC4846856 DOI: 10.1016/j.nicl.2016.03.020] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 03/14/2016] [Accepted: 03/28/2016] [Indexed: 12/22/2022]
Abstract
Despite the prevalence of migraine, the pathophysiology of the disease remains unclear. Current understanding of migraine has alluded to the possibility of a hyperexcitable brain. The aim of the current study is to investigate human brain metabolite differences in the anterior cingulate cortex (ACC) during the interictal phase in migraine patients. We hypothesized that there may be differences in levels of excitatory neurotransmitters and/or their derivatives in the migraine cohort in support of the theory of hyperexcitability in migraine. 2D J-resolved proton magnetic resonance spectroscopy (1H-MRS) data were acquired on a 3 Tesla (3 T) MRI from a voxel placed over the ACC of 32 migraine patients (MP; 23 females, 9 males, age 33 ± 9.6 years) and 33 healthy controls (HC; 25 females, 8 males, age 32 ± 9.6 years). Amplitude correlation matrices were constructed for each subject to evaluate metabolite discriminability. ProFit-estimated metabolite peak areas were normalized to a water reference signal to assess subject differences. The initial analysis of variance (ANOVA) was performed to test for group differences for all metabolites/creatine (Cre) ratios between healthy controls and migraineurs but showed no statistically significant differences. In addition, we used a multivariate approach to distinguish migraineurs from healthy subjects based on the metabolite/Cre ratio. A quadratic discriminant analysis (QDA) model was used to identify 3 metabolite ratios sufficient to minimize minimum classification error (MCE). The 3 selected metabolite ratios were aspartate (Asp)/Cre, N-acetyl aspartate (NAA)/Cre, and glutamine (Gln)/Cre. These findings are in support of a ‘complex’ of metabolite alterations, which may underlie changes in neuronal chemistry in the migraine brain. Furthermore, the parallel changes in the three-metabolite ‘complex’ may confer more subtle but biological processes that are ongoing. The data also support the current theory that the migraine brain is hyperexcitable even in the interictal state. 3 T MRI was used to acquire 2D J-resolved proton magnetic resonance spectroscopy. Metabolite alterations are reported in the anterior cingulate cortex of episodic migraineurs. The complex of metabolites may reflect multiple chemical changes in migraineurs. The observed chemical changes support the theory that the brain of migraineurs is hyperexcitable.
Collapse
Affiliation(s)
- L Becerra
- Pain/Analgesia Imaging Neuroscience (P.A.I.N.) Group, Department of Anesthesia Critical Care and Pain Medicine, Boston Children's Hospital, Center for Pain and the Brain, Harvard Medical School, Waltham, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Departments of Psychiatry and Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - R Veggeberg
- Pain/Analgesia Imaging Neuroscience (P.A.I.N.) Group, Department of Anesthesia Critical Care and Pain Medicine, Boston Children's Hospital, Center for Pain and the Brain, Harvard Medical School, Waltham, MA, USA; Brain Imaging Center, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - A Prescot
- Department of Radiology, University of Utah, School of Medicine, Salt Lake City, UT, USA; VISN 19 MIRECC, Salt Lake City, UT, USA
| | - J E Jensen
- Brain Imaging Center, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - P Renshaw
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA; VISN 19 MIRECC, Salt Lake City, UT, USA
| | - S Scrivani
- Department of Oral and Maxillofacial Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - E L H Spierings
- Department of Neurology, Tufts Medical Center, Boston, MA, USA
| | - R Burstein
- Anesthesia and Critical Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - D Borsook
- Pain/Analgesia Imaging Neuroscience (P.A.I.N.) Group, Department of Anesthesia Critical Care and Pain Medicine, Boston Children's Hospital, Center for Pain and the Brain, Harvard Medical School, Waltham, MA, USA; Departments of Psychiatry and Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA; Brain Imaging Center, McLean Hospital, Harvard Medical School, Belmont, MA, USA.
| |
Collapse
|