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Huang YL, Lin YR, Tsai SY. Comparison of convolutional-neural-networks-based method and LCModel on the quantification of in vivo magnetic resonance spectroscopy. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01120-z. [PMID: 37713007 DOI: 10.1007/s10334-023-01120-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/09/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Quantification of metabolites concentrations in institutional unit (IU) is important for inter-subject and long-term comparisons in the applications of magnetic resonance spectroscopy (MRS). Recently, deep learning (DL) algorithms have found a variety of applications on the process of MRS data. A quantification strategy compatible to DL base MRS spectral processing method is, therefore, useful. MATERIALS AND METHODS This study aims to investigate whether metabolite concentrations quantified using a convolutional neural network (CNN) based method, coupled with a scaling procedure that normalizes spectral signals for CNN input and linear regression, can effectively reflect variations in metabolite concentrations in IU across different brain regions with varying signal-to-noise ratios (SNR) and linewidths (LW). An error index based on standard error (SE) is proposed to indicate the confidence levels associated with metabolite predictions. In vivo MRS spectra were acquired from three brain regions of 43 subjects using a 3T system. RESULTS The metabolite concentrations in IU of five major metabolites, quantified using CNN and LCModel, exhibit similar ranges with Pearson's correlation coefficients ranging from 0.24 to 0.78. The SE of the metabolites shows a positive correlation with Cramer-Rao lower bound (CRLB) (r=0.46) and absolute CRLB (r=0.81), calculated by multiplying CRLBs with the quantified metabolite content. CONCLUSION In conclusion, the CNN based method with the proposed scaling procedures can be employed to quantify in vivo MRS spectra and derive metabolites concentrations in IU. The SE can be used as error index, indicating predicted uncertainties for metabolites and sharing information similar to the absolute CRLB.
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Affiliation(s)
- Yu-Long Huang
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Yi-Ru Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Shang-Yueh Tsai
- Graduate Institute of Applied Physics, National Chengchi University, No.64, Sec.2, ZhiNan Rd., Wenshan District, Taipei, 11605, Taiwan.
- Research Center for Mind, Brain and Learning, National Chengchi University, Taipei, Taiwan.
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Klauser A, Klauser P, Grouiller F, Courvoisier S, Lazeyras F. Whole-brain high-resolution metabolite mapping with 3D compressed-sensing SENSE low-rank 1 H FID-MRSI. NMR IN BIOMEDICINE 2022; 35:e4615. [PMID: 34595791 PMCID: PMC9285075 DOI: 10.1002/nbm.4615] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 08/16/2021] [Accepted: 08/20/2021] [Indexed: 05/07/2023]
Abstract
There is a growing interest in the neuroscience community to map the distribution of brain metabolites in vivo. Magnetic resonance spectroscopic imaging (MRSI) is often limited by either a poor spatial resolution and/or a long acquisition time, which severely restricts its applications for clinical and research purposes. Building on a recently developed technique of acquisition-reconstruction for 2D MRSI, we combined a fast Cartesian 1 H-FID-MRSI acquisition sequence, compressed-sensing acceleration, and low-rank total-generalized-variation constrained reconstruction to produce 3D high-resolution whole-brain MRSI with a significant acquisition time reduction. We first evaluated the acceleration performance using retrospective undersampling of a fully sampled dataset. Second, a 20 min accelerated MRSI acquisition was performed on three healthy volunteers, resulting in metabolite maps with 5 mm isotropic resolution. The metabolite maps exhibited the detailed neurochemical composition of all brain regions and revealed parts of the underlying brain anatomy. The latter assessment used previous reported knowledge and a atlas-based analysis to show consistency of the concentration contrasts and ratio across all brain regions. These results acquired on a clinical 3 T MRI scanner successfully combined 3D 1 H-FID-MRSI with a constrained reconstruction to produce detailed mapping of metabolite concentrations at high resolution over the whole brain, with an acquisition time suitable for clinical or research settings.
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Affiliation(s)
- Antoine Klauser
- Department of Radiology and Medical InformaticsUniversity of GenevaSwitzerland
- Center for Biomedical Imaging (CIBM)GenevaSwitzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of PsychiatryLausanne University HospitalSwitzerland
- Service of Child and Adolescent Psychiatry, Department of PsychiatryLausanne University HospitalSwitzerland
| | - Frédéric Grouiller
- Swiss Center for Affective SciencesUniversity of GenevaSwitzerland
- Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Fundamental NeuroscienceUniversity of GenevaSwitzerland
| | - Sébastien Courvoisier
- Department of Radiology and Medical InformaticsUniversity of GenevaSwitzerland
- Center for Biomedical Imaging (CIBM)GenevaSwitzerland
| | - François Lazeyras
- Department of Radiology and Medical InformaticsUniversity of GenevaSwitzerland
- Center for Biomedical Imaging (CIBM)GenevaSwitzerland
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3
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Klauser A, Strasser B, Thapa B, Lazeyras F, Andronesi O. Achieving high-resolution 1H-MRSI of the human brain with compressed-sensing and low-rank reconstruction at 7 Tesla. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 331:107048. [PMID: 34438355 PMCID: PMC8717865 DOI: 10.1016/j.jmr.2021.107048] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/29/2021] [Accepted: 08/08/2021] [Indexed: 06/02/2023]
Abstract
Low sensitivity MR techniques such as magnetic resonance spectroscopic imaging (MRSI) greatly benefit from the gain in signal-to-noise provided by ultra-high field MR. High-resolution and whole-slab brain MRSI remains however very challenging due to lengthy acquisition, low signal, lipid contamination and field inhomogeneity. In this study, we propose an acquisition-reconstruction scheme that combines 1H free-induction-decay (FID)-MRSI sequence, short TR acquisition, compressed sensing acceleration and low-rank modeling with total-generalized-variation constraint to achieve metabolite imaging in two and three dimensions at 7 Tesla. The resulting images and volumes reveal highly detailed distributions that are specific to each metabolite and follow the underlying brain anatomy. The MRSI method was validated in a high-resolution phantom containing fine metabolite structures, and in five healthy volunteers. This new application of compressed sensing acceleration paves the way for high-resolution MRSI in clinical setting with acquisition times of 5 min for 2D MRSI at 2.5 mm and of 20 min for 3D MRSI at 3.3 mm isotropic.
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Affiliation(s)
- Antoine Klauser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Center for Biomedical Imaging (CIBM), Geneva, Switzerland.
| | - Bernhard Strasser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Bijaya Thapa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Francois Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Ovidiu Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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4
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Adany P, Choi IY, Lee P. Method for fast lipid reconstruction and removal processing in 1 H MRSI of the brain. Magn Reson Med 2021; 86:2930-2944. [PMID: 34337788 DOI: 10.1002/mrm.28949] [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: 02/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop a new rapid spatial filtering method for lipid removal, fast lipid reconstruction and removal processing (FLIP), which selectively isolates and removes interfering lipid signals from outside the brain in a full-FOV 2D MRSI and whole-brain 3D echo planar spectroscopic imaging (EPSI). THEORY AND METHODS FLIP uses regularized least-squares regression based on spatial prior information from MRI to selectively remove lipid signals originating from the scalp and measure the brain metabolite signals with minimum cross contamination. FLIP is a noniterative approach, thus allowing a rapid processing speed, and uses only spatial information without any spectral priors. The performance of FLIP was compared with the Papoulis-Gerchberg algorithm (PGA), Hankel singular value decomposition (HSVD), and fast image reconstruction with L2 regularization (L2). RESULTS FLIP in both 2D and 3D MRSI resulted in consistent metabolite quantification in a greater number of voxels with less concentration variation than other algorithms, demonstrating effective and robust lipid-removal performance. The percentage of voxels that met quality criteria with FLIP, PGA, HSVD, and L2 processing were 90%, 57%, 29%, and 42% in 2D MRSI, and 80%, 75%, 76%, and 74% in 3D EPSI, respectively. The quantification results of full-FOV MRSI using FLIP were comparable to those of volume-localized MRSI, while allowing significantly increased spatial coverage. FLIP performed the fastest in 2D MRSI. CONCLUSION FLIP is a new lipid-removal algorithm that promises fast and effective lipid removal with improved volume coverage in MRSI.
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Affiliation(s)
- Peter Adany
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - In-Young Choi
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Molecular & Integrative Physiology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Phil Lee
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Molecular & Integrative Physiology, University of Kansas Medical Center, Kansas City, Kansas, USA
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Maudsley AA, Andronesi OC, Barker PB, Bizzi A, Bogner W, Henning A, Nelson SJ, Posse S, Shungu DC, Soher BJ. Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations. NMR IN BIOMEDICINE 2021; 34:e4309. [PMID: 32350978 PMCID: PMC7606742 DOI: 10.1002/nbm.4309] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 02/01/2020] [Accepted: 03/10/2020] [Indexed: 05/04/2023]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.
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Affiliation(s)
- Andrew A Maudsley
- Department of Radiology, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Ovidiu C Andronesi
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, Massachusetts
| | - Peter B Barker
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, and the Kennedy Krieger Institute, F.M. Kirby Center for Functional Brain Imaging, Baltimore, Maryland
| | - Alberto Bizzi
- Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Wolfgang Bogner
- High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
| | - Anke Henning
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Sarah J Nelson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Stefan Posse
- Department of Neurology, University of New Mexico, Albuquerque, New Mexico
| | - Dikoma C Shungu
- Department of Neuroradiology, Weill Cornell Medical College, New York, New York
| | - Brian J Soher
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
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