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Nam KM, Gursan A, Lee NG, Klomp DWJ, Wijnen JP, Prompers JJ, Hendriks AD, Bhogal AA. 3D deuterium metabolic imaging (DMI) of the human liver at 7 T using low-rank and subspace model-based reconstruction. Magn Reson Med 2024. [PMID: 39710859 DOI: 10.1002/mrm.30395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 11/13/2024] [Accepted: 11/16/2024] [Indexed: 12/24/2024]
Abstract
PURPOSE To implement a low-rank and subspace model-based reconstruction for 3D deuterium metabolic imaging (DMI) and compare its performance against Fourier transform-based (FFT) reconstruction in terms of spectral fitting reliability. METHODS Both reconstruction methods were applied on simulated and experimental DMI data. Numerical simulations were performed to evaluate the effect of increasing acceleration factors. The impact on spectral fitting results, SNR, and the overall normalized root mean square error (NRMSE) compared to ground-truth data were calculated. A comparative analysis was performed on DMI data acquired from the human liver, including both natural abundance and post-deuterated glucose intake data at 7 T. RESULTS Simulation showed the Cramer-Rao lower bound [%] of water, glucose, sum of glutamate and glutamine (Glx), and lipid signals for the low-rank and subspace model-based reconstruction at R = 1.0 was 12.4, 14.7, 17.3, and 11.0 times lower than FFT. At R = 1.1, NRMSE was 1.4%, 1.3%, 0.8%, and 4.2% lower for the water, glucose, Glx, and lipid, respectively, compared to FFT. However, the NRMSE of the Glx and lipid increased by 0.4% and 3.2% at R = 1.3. For the in vivo DMI experiment, SNR was 2.5-3.0 times higher compared to FFT. The fitted amplitude of water and glucose peaks showed Cramer-Rao lower bound [%] values that were approximately 2.3 times lower than FFT. CONCLUSION Simulations and in vivo experiments on the human liver demonstrate that low-rank and subspace model-based reconstruction with undersampled data mitigates noise and enhances spectral fitting quality.
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Affiliation(s)
- Kyung Min Nam
- Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Ayhan Gursan
- Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Nam G Lee
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
| | - Dennis W J Klomp
- Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Jannie P Wijnen
- Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Jeanine J Prompers
- Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Arjan D Hendriks
- Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Alex A Bhogal
- Center for Image Sciences, High Field MR Research Group, Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
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Zhao Y, Li Y, Guo R, Jin W, Sutton B, Ma C, El Fakhri G, Li Y, Luo J, Liang ZP. Accelerated 3D metabolite T 1 mapping of the brain using variable-flip-angle SPICE. Magn Reson Med 2024; 92:1310-1322. [PMID: 38923032 DOI: 10.1002/mrm.30200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/02/2024] [Accepted: 06/04/2024] [Indexed: 06/28/2024]
Abstract
PURPOSE To develop a practical method to enable 3D T1 mapping of brain metabolites. THEORY AND METHODS Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. RESULTS The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. CONCLUSION We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.
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Affiliation(s)
- Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Siemens Medical Solutions USA, Inc., Urbana, Illinois, USA
| | - Wen Jin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Brad Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Chao Ma
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Georges El Fakhri
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Wang Y, Saha U, Rubakhin SS, Roy EJ, Smith AM, Sweedler JV, Lam F. High-resolution 1H-MRSI at 9.4 T by integrating relaxation enhancement and subspace imaging. NMR IN BIOMEDICINE 2024; 37:e5161. [PMID: 38715469 PMCID: PMC11469943 DOI: 10.1002/nbm.5161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 07/12/2024]
Abstract
Achieving high-resolution and high signal-to-noise ratio (SNR) in vivo metabolic imaging via fast magnetic resonance spectroscopic imaging (MRSI) has been a longstanding challenge. This study combines the methods of relaxation enhancement (RE) and subspace imaging for the first time, enabling high-resolution and high-SNR in vivo MRSI of rodent brains at 9.4 T. Specifically, an RE-based chemical shift imaging sequence, which combines a frequency-selective pulse to excite only the metabolite frequencies with minimum perturbation of the water spins and a pair of adiabatic pulses to spatially localize the slice of interest, is designed and evaluated in vivo. This strategy effectively shortens the apparent T1 of metabolites, thereby increasing the SNR during relatively short repetition time ((TR) compared with acquisitions with only spatially selective wideband excitations, and does not require water suppression. The SNR was further enhanced via a state-of-the-art subspace reconstruction method. A novel subspace learning strategy tailored for 9.4 T and RE acquisitions is developed. In vivo, high-resolution (e.g., voxel size of 0.6 × 0.6 × 1.5 mm3) MRSI of both healthy mouse brains and a glioma-bearing mouse brain in 12.5 min has been demonstrated.
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Affiliation(s)
- Yizun Wang
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Urbi Saha
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Stanislav S. Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Edward J. Roy
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Andrew M. Smith
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Holonyak Micro and Nanotechnology Laboratory, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carle Illinois College of Medicine, Urbana, Illinois, USA
| | - Jonathan V. Sweedler
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Carle Illinois College of Medicine, Urbana, Illinois, USA
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Chan RCK, Wang LL, Huang J, Wang Y, Lui SSY. Anhedonia Across and Beyond the Schizophrenia Spectrum. Schizophr Bull 2024:sbae165. [PMID: 39326030 DOI: 10.1093/schbul/sbae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/28/2024]
Abstract
Anhedonia refers to the diminished ability to experience pleasure, and is a core feature of schizophrenia (SCZ). The neurocognitive and neural correlates of anhedonia remain elusive. Based on several influential theoretical models for negative symptoms, this selective review proposed four important neurocognitive domains, which may unveil the neurobiological mechanisms of anhedonia. The authors critically reviewed the current evidence regarding value representation of reward, prospection, emotion-behavior decoupling, and belief updating in the Chinese setting, covering both behavioral and neuroimaging research. We observed a limited application of the transdiagnostic approach in previous studies on the four domains, and the lack of adequate measures to tap into the expressivity deficit in SCZ. Despite many behavioral paradigms for these four domains utilized both social and non-social stimuli, previous studies seldom focused on the social-versus-non-social differentiation. We further advocated several important directions for future research.
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Affiliation(s)
- Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Ling-Ling Wang
- School of Psychology, Shanghai Normal University, Shanghai, China
| | - Jia Huang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Simon S Y Lui
- Department of Psychiatry, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Hu J, Zhang M, Zhang Y, Zhuang H, Zhao Y, Li Y, Jin W, Qian X, Wang L, Ye G, Tang H, Liu J, Li B, Nachev P, Liang Z, Li Y. Neurometabolic topography and associations with cognition in Alzheimer's disease: A whole-brain high-resolution 3D MRSI study. Alzheimers Dement 2024; 20:6407-6422. [PMID: 39073196 PMCID: PMC11497670 DOI: 10.1002/alz.14137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/29/2024] [Accepted: 06/22/2024] [Indexed: 07/30/2024]
Abstract
INTRODUCTION Altered neurometabolism, detectable via proton magnetic resonance spectroscopic imaging (1H-MRSI), is spatially heterogeneous and underpins cognitive impairments in Alzheimer's disease (AD). However, the spatial relationships between neurometabolic topography and cognitive impairment in AD remain unexplored due to technical limitations. METHODS We used a novel whole-brain high-resolution 1H-MRSI technique, with simultaneously acquired 18F-florbetapir positron emission tomography (PET) imaging, to investigate the relationship between neurometabolic topography and cognitive functions in 117 participants, including 22 prodromal AD, 51 AD dementia, and 44 controls. RESULTS Prodromal AD and AD dementia patients exhibited spatially distinct reductions in N-acetylaspartate, and increases in myo-inositol. Reduced N-acetylaspartate and increased myo-inositol were associated with worse global cognitive performance, and N-acetylaspartate correlated with five specific cognitive scores. Neurometabolic topography provides biological insights into diverse cognitive dysfunctions. DISCUSSION Whole-brain high-resolution 1H-MRSI revealed spatially distinct neurometabolic topographies associated with cognitive decline in AD, suggesting potential for noninvasive brain metabolic imaging to track AD progression. HIGHLIGHTS Whole-brain high-resolution 1H-MRSI unveils neurometabolic topography in AD. Spatially distinct reductions in NAA, and increases in mI, are demonstrated. NAA and mI topography correlates with global cognitive performance. NAA topography correlates with specific cognitive performance.
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Affiliation(s)
- Jialin Hu
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Miao Zhang
- Department of Nuclear MedicineRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Yaoyu Zhang
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Huixiang Zhuang
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
| | - Yibo Zhao
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
- Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
| | - Yudu Li
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
- National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
| | - Wen Jin
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
- Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
| | - Xiao‐Hang Qian
- Department of GeriatricsRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
- Medical Center on Aging of Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
- Department of Neurology and Institute of NeurologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Lijun Wang
- Department of Neurovascular CenterChanghai HospitalNaval Medical UniversityShanghaiChina
| | - Guanyu Ye
- Department of Neurology and Institute of NeurologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Huidong Tang
- Department of GeriatricsRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
- Medical Center on Aging of Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Jun Liu
- Department of Neurology and Institute of NeurologyRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Biao Li
- Department of Nuclear MedicineRuijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina
| | - Parashkev Nachev
- High‐Dimensional Neurology GroupInstitute of NeurologyUniversity College LondonLondonUK
| | - Zhi‐Pei Liang
- Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
- Department of Electrical and Computer EngineeringUniversity of Illinois at Urbana‐ChampaignUrbanaIllinoisUSA
| | - Yao Li
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
- Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghaiChina
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Rakić M, Turco F, Weng G, Maes F, Sima DM, Slotboom J. Deep learning pipeline for quality filtering of MRSI spectra. NMR IN BIOMEDICINE 2024; 37:e5012. [PMID: 37518942 DOI: 10.1002/nbm.5012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/15/2023] [Accepted: 07/10/2023] [Indexed: 08/01/2023]
Abstract
With the rise of novel 3D magnetic resonance spectroscopy imaging (MRSI) acquisition protocols in clinical practice, which are capable of capturing a large number of spectra from a subject's brain, there is a need for an automated preprocessing pipeline that filters out bad-quality spectra and identifies contaminated but salvageable spectra prior to the metabolite quantification step. This work introduces such a pipeline based on an ensemble of deep-learning classifiers. The dataset consists of 36,338 spectra from one healthy subject and five brain tumor patients, acquired with an EPSI variant, which implemented a novel type of spectral editing named SLOtboom-Weng (SLOW) editing on a 7T MR scanner. The spectra were labeled manually by an expert into four classes of spectral quality as follows: (i) noise, (ii) spectra greatly influenced by lipid-related artifacts (deemed not to contain clinical information), (iii) spectra containing metabolic information slightly contaminated by lipid signals, and (iv) good-quality spectra. The AI model consists of three pairs of networks, each comprising a convolutional autoencoder and a multilayer perceptron network. In the classification step, the encoding half of the autoencoder is kept as a dimensionality reduction tool, while the fully connected layers are added to its output. Each of the three pairs of networks is trained on different representations of spectra (real, imaginary, or both), aiming at robust decision-making. The final class is assigned via a majority voting scheme. The F1 scores obtained on the test dataset for the four previously defined classes are 0.96, 0.93, 0.82, and 0.90, respectively. The arguably lower value of 0.82 was reached for the least represented class of spectra mildly influenced by lipids. Not only does the proposed model minimise the required user interaction, but it also greatly reduces the computation time at the metabolite quantification step (by selecting a subset of spectra worth quantifying) and enforces the display of only clinically relevant information.
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Affiliation(s)
- Mladen Rakić
- Research and Development, Icometrix, Leuven, Belgium
- Department of Electrical Engineering (ESAT), Processing Speech and Images (PSI) and Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Federico Turco
- Institute for Diagnostic and Interventional Radiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | - Guodong Weng
- Institute for Diagnostic and Interventional Radiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | - Frederik Maes
- Department of Electrical Engineering (ESAT), Processing Speech and Images (PSI) and Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Diana M Sima
- Research and Development, Icometrix, Leuven, Belgium
| | - Johannes Slotboom
- Institute for Diagnostic and Interventional Radiology, Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
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Zhao R, Peng X, Kelkar VA, Anastasio MA, Lam F. High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models. IEEE Trans Biomed Eng 2024; 71:1969-1979. [PMID: 38265912 PMCID: PMC11105985 DOI: 10.1109/tbme.2024.3358223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
OBJECTIVE To develop a new method that integrates subspace and generative image models for high-dimensional MR image reconstruction. METHODS We proposed a formulation that synergizes a low-dimensional subspace model of high-dimensional images, an adaptive generative image prior serving as spatial constraints on the sequence of "contrast-weighted" images or spatial coefficients of the subspace model, and a conventional sparsity regularization. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-network-based representation for images with varying contrasts. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged an recently proposed intermediate layer optimization technique for network inversion. RESULTS We evaluated the utility of the proposed method for two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MR spectroscopic imaging. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases. CONCLUSION The proposed method provided a new way to address high-dimensional MR image reconstruction problems by incorporating an adaptive generative model as a data-driven spatial prior for constraining subspace reconstruction. SIGNIFICANCE Our work demonstrated the potential of integrating data-driven and adaptive generative priors with canonical low-dimensional modeling for high-dimensional imaging problems.
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Huang H, Zhang M, Zhao Y, Li Y, Jin W, Guo R, Liu W, Cai B, Li J, Yuan S, Huang X, Lin X, Liang ZP, Li B, Luo J. Simultaneous high-resolution whole-brain MR spectroscopy and [ 18F]FDG PET for temporal lobe epilepsy. Eur J Nucl Med Mol Imaging 2024; 51:721-733. [PMID: 37823910 DOI: 10.1007/s00259-023-06465-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 09/28/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.
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Affiliation(s)
- Hui Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yibo Zhao
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Yudu Li
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Wen Jin
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Rong Guo
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Siemens Medical Solutions USA, Inc, Urbana, IL, 61801, USA
| | - Wei Liu
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Bingyang Cai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jiwei Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Siyu Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xinyun Huang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana Champaign, Urbana, IL, 61801, USA
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Wang J, Ji B, Lei Y, Liu T, Mao H, Yang X. Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal-to-noise ratio and speed of MRS. Med Phys 2023; 50:7955-7966. [PMID: 37947479 PMCID: PMC10872746 DOI: 10.1002/mp.16831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 10/05/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy (MRS) enables non-invasive detection and measurement of biochemicals and metabolites. However, MRS has low signal-to-noise ratio (SNR) when concentrations of metabolites are in the range of millimolar. Standard approach of using a high number of signal averaging (NSA) to achieve sufficient SNR comes at the cost of a long acquisition time. PURPOSE We propose to use deep-learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra, which could enhance the diagnostic value and broaden the clinical applications of MRS. METHODS The study was conducted using data collected from the brain spectroscopy phantom and human subjects. We utilized a stack auto-encoder (SAE) network to train deep learning models for denoising low NSA data (NSA = 1, 2, 4, 8, and 16) randomly truncated from high SNR data collected with high NSA (NSA = 192), which were also used to obtain the ground truth. We applied both self-supervised and fully-supervised training approaches and compared their performance of denoising low NSA data based on improvement in SNR. To prevent overfitting, the SAE network was trained in a patch-based manner. We then tested the denoising methods on noise-containing data collected from the phantom and human subjects, including data from brain tumor patients. We evaluated their performance by comparing the SNR levels and mean squared errors (MSEs) calculated for the whole spectra against high SNR "ground truth", as well as the value of chemical shift of N-acetyl-aspartate (NAA) before and after denoising. RESULTS With the SAE model, the SNR of low NSA data (NSA = 1) obtained from the phantom increased by 28.5% and the MSE decreased by 42.9%. For low NSA data of the human parietal and temporal lobes, the SNR increased by 32.9% and the MSE decreased by 63.1%. In all cases, the chemical shift of NAA in the denoised spectra closely matched with the high SNR spectra without significant distortion to the spectra after denoising. Furthermore, the denoising performance of the SAE model was more effective in denoising spectra with higher noise levels. CONCLUSIONS The reported SAE denoising method is a model-free approach to enhance the SNR of MRS data collected with low NSA. With the denoising capability, it is possible to acquire MRS data with a few NSA, shortening the scan time while maintaining adequate spectroscopic information for detecting and quantifying the metabolites of interest. This approach has the potential to improve the efficiency and effectiveness of clinical MRS data acquisition by reducing the scan time and increasing the quality of spectroscopic data.
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Affiliation(s)
- Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Bing Ji
- Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Hui Mao
- Department of Radiology and Imaging Science and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
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Meng Z, Guo R, Wang T, Bo B, Lin Z, Li Y, Zhao Y, Yu X, Lin DJ, Nachev P, Liang ZP, Li Y. Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T 2 Mapping. IEEE Trans Biomed Eng 2023; 70:3147-3155. [PMID: 37200119 DOI: 10.1109/tbme.2023.3277546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
OBJECTIVE The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. METHODS Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. RESULTS In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). CONCLUSION The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. SIGNIFICANCE Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.
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Zhang T, Zhao Y, Jin W, Li Y, Guo R, Ke Z, Luo J, Li Y, Liang ZP. B 1 mapping using pre-learned subspaces for quantitative brain imaging. Magn Reson Med 2023; 90:2089-2101. [PMID: 37345702 DOI: 10.1002/mrm.29764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.
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Affiliation(s)
- Tianxiao Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Wen Jin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Siemens Medical Solutions USA, Inc., Urbana, Illinois, USA
| | - Ziwen Ke
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Lin Z, Meng Z, Wang T, Guo R, Zhao Y, Li Y, Bo B, Guan Y, Liu J, Zhou H, Yu X, Lin DJ, Liang ZP, Nachev P, Li Y. Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging. J Magn Reson Imaging 2023; 58:838-847. [PMID: 36625533 DOI: 10.1002/jmri.28596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE Cross-sectional cohort. POPULATION A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Zengping Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziyu Meng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Tianyao Wang
- Radiology Department, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Siemens Medical Solutions USA, Inc., Urbana, Illinois, USA
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bin Bo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Guan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Liu
- Radiology Department, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China
| | - Hong Zhou
- Department of Radiology, The First Affiliated Hospital of South China of University, South China of University, Hengyang, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - David J Lin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | | | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Arefeen Y, Xu J, Zhang M, Dong Z, Wang F, White J, Bilgic B, Adalsteinsson E. Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI. Magn Reson Med 2023; 90:483-501. [PMID: 37093775 DOI: 10.1002/mrm.29657] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model. METHODS Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T2 -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints. RESULTS An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T2 -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments. CONCLUSION Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.
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Affiliation(s)
- Yamin Arefeen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Molin Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Jacob White
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Nam KM, Gursan A, Bhogal AA, Wijnen JP, Klomp DWJ, Prompers JJ, Hendriks AD. Deuterium echo-planar spectroscopic imaging (EPSI) in the human liver in vivo at 7 T. Magn Reson Med 2023. [PMID: 37154391 DOI: 10.1002/mrm.29696] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/04/2023] [Accepted: 04/19/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE To demonstrate the feasibility of deuterium echo-planar spectroscopic imaging (EPSI) to accelerate 3D deuterium metabolic imaging in the human liver at 7 T. METHODS A deuterium EPSI sequence, featuring a Hamming-weighted k-space acquisition pattern for the phase-encoding directions, was implemented. Three-dimensional deuterium EPSI and conventional MRSI were performed on a water/acetone phantom and in vivo in the human liver at natural abundance. Moreover, in vivo deuterium EPSI measurements were acquired after oral administration of deuterated glucose. The effect of acquisition time on SNR was evaluated by retrospectively reducing the number of averages. RESULTS The SNR of natural abundance deuterated water signal in deuterium EPSI was 6.5% and 5.9% lower than that of MRSI in the phantom and in vivo experiments, respectively. In return, the acquisition time of in vivo EPSI data could be reduced retrospectively to 2 min, beyond the minimal acquisition time of conventional MRSI (of 20 min in this case), while still leaving sufficient SNR. Three-dimensional deuterium EPSI, after administration of deuterated glucose, enabled monitoring of hepatic glucose dynamics with full liver coverage, a spatial resolution of 20 mm isotropic, and a temporal resolution of 9 min 50 s, which could retrospectively be shortened to 2 min. CONCLUSION In this work, we demonstrate the feasibility of accelerated 3D deuterium metabolic imaging of the human liver using deuterium EPSI. The acceleration obtained with EPSI can be used to increase temporal and/or spatial resolution, which will be valuable to study tissue metabolism of deuterated compounds over time.
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Affiliation(s)
- Kyung Min Nam
- Center for Image Sciences, Department of High Field MR Research, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ayhan Gursan
- Center for Image Sciences, Department of High Field MR Research, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Alex A Bhogal
- Center for Image Sciences, Department of High Field MR Research, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jannie P Wijnen
- Center for Image Sciences, Department of High Field MR Research, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dennis W J Klomp
- Center for Image Sciences, Department of High Field MR Research, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jeanine J Prompers
- Center for Image Sciences, Department of High Field MR Research, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Arjan D Hendriks
- Center for Image Sciences, Department of High Field MR Research, University Medical Center Utrecht, Utrecht, the Netherlands
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Ma C, Han PK, Zhuo Y, Djebra Y, Marin T, El Fakhri G. Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning. Magn Reson Med 2023; 89:1297-1313. [PMID: 36404676 PMCID: PMC9892363 DOI: 10.1002/mrm.29526] [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: 07/21/2022] [Revised: 10/07/2022] [Accepted: 10/24/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE To develop a manifold learning-based method that leverages the intrinsic low-dimensional structure of MR Spectroscopic Imaging (MRSI) signals for joint spectral quantification. METHODS A linear tangent space alignment (LTSA) model was proposed to represent MRSI signals. In the proposed model, the signals of each metabolite were represented using a subspace model and the local coordinates of the subspaces were aligned to the global coordinates of the underlying low-dimensional manifold via linear transform. With the basis functions of the subspaces predetermined via quantum mechanics simulations, the global coordinates and the matrices for the local-to-global coordinate alignment were estimated by fitting the proposed LTSA model to noisy MRSI data with a spatial smoothness constraint on the global coordinates and a sparsity constraint on the matrices. RESULTS The performance of the proposed method was validated using numerical simulation data and in vivo proton-MRSI experimental data acquired on healthy volunteers at 3T. The results of the proposed method were compared with the QUEST method and the subspace-based method. In all the compared cases, the proposed method achieved superior performance over the QUEST and the subspace-based methods both qualitatively in terms of noise and artifacts in the estimated metabolite concentration maps, and quantitatively in terms of spectral quantification accuracy measured by normalized root mean square errors. CONCLUSION Joint spectral quantification using linear tangent space alignment-based manifold learning improves the accuracy of MRSI spectral quantification.
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Affiliation(s)
- Chao Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Kyu Han
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Yue Zhuo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Yanis Djebra
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA,LTCI, Telecom Paris, Institut Polytechnique de Paris, Paris, France
| | - Thibault Marin
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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Lam F, Peng X, Liang ZP. High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions. IEEE SIGNAL PROCESSING MAGAZINE 2023; 40:101-115. [PMID: 37538148 PMCID: PMC10398845 DOI: 10.1109/msp.2022.3203867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven machine learning that exploit unique physical and mathematical properties of MRSI signals have demonstrated impressive performance in addressing these challenges for rapid, high-resolution, quantitative MRSI. This paper provides a systematic review of these progresses in the context of MRSI physics and offers perspectives on promising future directions.
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Affiliation(s)
- Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA
- Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering and Cancer Center at Illinois, University of Illinois Urbana-Champaign
| | - Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering and Cancer Center at Illinois, University of Illinois Urbana-Champaign
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Djebra Y, Marin T, Han PK, Bloch I, El Fakhri G, Ma C. Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:158-169. [PMID: 36121938 PMCID: PMC10024645 DOI: 10.1109/tmi.2022.3207774] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled k -space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI. The performance of the proposed method was evaluated and compared to state-of-the-art methods using numerical simulation studies as well as 2D and 3D in vivo cardiac imaging experiments. The proposed method achieved the best performance in image reconstruction among all the compared methods. The proposed method could prove useful for accelerating many MRI applications, including dynamic MRI, multi-parametric MRI, and MR spectroscopic imaging.
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Affiliation(s)
- Yanis Djebra
- Gordon Center for Medical Imaging, Massachusetts General Hospital, and Department of Radiology, Harvard Medical School, Boston, MA 02129 USA and the LTCI, Telecom Paris, Institut Polytechnique de Paris, Paris, France
| | - Thibault Marin
- Gordon Center for Medical Imaging, Massachusetts General Hospital, and Department of Radiology, Harvard Medical School, Boston, MA 02129 USA
| | - Paul K. Han
- Gordon Center for Medical Imaging, Massachusetts General Hospital, and Department of Radiology, Harvard Medical School, Boston, MA 02129 USA
| | - Isabelle Bloch
- LIP6, Sorbonne University, CNRS Paris, France. This work was partly done while I. Bloch was with the LTCI, Telecom Paris, Institut Polytechnique de Paris, Paris, France
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital, and Department of Radiology, Harvard Medical School, Boston, MA 02129 USA
| | - Chao Ma
- Gordon Center for Medical Imaging, Massachusetts General Hospital, and Department of Radiology, Harvard Medical School, Boston, MA 02129 USA
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McIlvain G, Cerjanic A, Christodoulou AG, McGarry MDJ, Johnson CL. OSCILLATE: A low-rank approach for accelerated magnetic resonance elastography. Magn Reson Med 2022; 88:1659-1672. [PMID: 35649188 PMCID: PMC9339522 DOI: 10.1002/mrm.29308] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/29/2022] [Accepted: 04/30/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE MR elastography (MRE) is a technique to characterize brain mechanical properties in vivo. Due to the need to capture tissue deformation in multiple directions over time, MRE is an inherently long acquisition, which limits achievable resolution and use in challenging populations. The purpose of this work is to develop a method for accelerating MRE acquisition by using low-rank image reconstruction to exploit inherent spatiotemporal correlations in MRE data. THEORY AND METHODS The proposed MRE sampling and reconstruction method, OSCILLATE (Observing Spatiotemporal Correlations for Imaging with Low-rank Leveraged Acceleration in Turbo Elastography), involves alternating which k-space points are sampled between each repetition by a reduction factor, ROSC. Using a predetermined temporal basis from a low-resolution navigator in a joint low-rank image reconstruction, all images can be accurately reconstructed from a reduced amount of k-space data. RESULTS Decomposition of MRE displacement data demonstrated that, on average, 96.1% of all energy from an MRE dataset is captured at rank L = 12 (reduced from a full rank of 24). Retrospectively undersampling data with ROSC = 2 and reconstructing at low-rank (L = 12) yields highly accurate stiffness maps with voxel-wise error of 5.8% ± 0.7%. Prospectively undersampled data at ROSC = 2 were successfully reconstructed without loss of material property map fidelity, with average global stiffness error of 1.0% ± 0.7% compared to fully sampled data. CONCLUSIONS OSCILLATE produces whole-brain MRE data at 2 mm isotropic resolution in 1 min 48 s.
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Affiliation(s)
- Grace McIlvain
- Department of Biomedical Engineering, University of Delaware, Newark, DE, United States
| | - Alex Cerjanic
- Department of Biomedical Engineering, University of Delaware, Newark, DE, United States
- University of Illinois College of Medicine, Urbana, IL, United States
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Matthew DJ McGarry
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE, United States
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19
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Stamatelatou A, Scheenen TWJ, Heerschap A. Developments in proton MR spectroscopic imaging of prostate cancer. MAGMA (NEW YORK, N.Y.) 2022; 35:645-665. [PMID: 35445307 PMCID: PMC9363347 DOI: 10.1007/s10334-022-01011-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 10/25/2022]
Abstract
In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Tom W J Scheenen
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
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20
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Zhang T, Guo R, Li Y, Zhao Y, Li Y, Liang ZP. T 2 ' mapping of the brain from water-unsuppressed 1 H-MRSI and turbo spin-echo data. Magn Reson Med 2022; 88:2198-2207. [PMID: 35844075 DOI: 10.1002/mrm.29386] [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: 01/03/2022] [Revised: 06/14/2022] [Accepted: 06/22/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE To obtain high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps of brain tissues from water-unsuppressed magnetic resonance spectroscopic imaging (MRSI) and turbo spin-echo (TSE) data. METHODS T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ mapping can be achieved using T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping from water-unsuppressed MRSI data and T 2 $$ {\mathrm{T}}_2 $$ mapping from TSE data. However, T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping often suffers from signal dephasing and distortions caused by B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity; T 2 $$ {\mathrm{T}}_2 $$ measurements may be biased due to system imperfections, especially for T 2 $$ {\mathrm{T}}_2 $$ -weighted image with small number of TEs. In this work, we corrected the B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity effect on T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping using a subspace model-based method, incorporating pre-learned spectral basis functions of the water signals. T 2 $$ {\mathrm{T}}_2 $$ estimation bias was corrected using a TE-adjustment method, which modeled the deviation between measured and reference T 2 $$ {\mathrm{T}}_2 $$ decays as TE shifts. RESULTS In vivo experiments were performed to evaluate the performance of the proposed method. High-quality T 2 * $$ {\mathrm{T}}_2^{\ast } $$ maps were obtained in the presence of large field inhomogeneity in the prefrontal cortex. Bias in T 2 $$ {\mathrm{T}}_2 $$ measurements obtained from TSE data was effectively reduced. Based on the T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and T 2 $$ {\mathrm{T}}_2 $$ measurements produced by the proposed method, high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps were obtained, along with neurometabolite maps, from MRSI and TSE data that were acquired in about 9 min. The results obtained from acute stroke and glioma patients demonstrated the feasibility of the proposed method in the clinical setting. CONCLUSIONS High-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps can be obtained from water-unsuppressed 1 H-MRSI and TSE data using the proposed method. With further development, this method may lay a foundation for simultaneously imaging oxygenation and neurometabolic alterations of brain disorders.
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Affiliation(s)
- Tianxiao Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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21
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Abstract
Abstract
Purpose
Gliomas, the most common primary brain tumours, have recently been re-classified incorporating molecular aspects with important clinical, prognostic, and predictive implications. Concurrently, the reprogramming of metabolism, altering intracellular and extracellular metabolites affecting gene expression, differentiation, and the tumour microenvironment, is increasingly being studied, and alterations in metabolic pathways are becoming hallmarks of cancer. Magnetic resonance spectroscopy (MRS) is a complementary, non-invasive technique capable of quantifying multiple metabolites. The aim of this review focuses on the methodology and analysis techniques in proton MRS (1H MRS), including a brief look at X-nuclei MRS, and on its perspectives for diagnostic and prognostic biomarkers in gliomas in both clinical practice and preclinical research.
Methods
PubMed literature research was performed cross-linking the following key words: glioma, MRS, brain, in-vivo, human, animal model, clinical, pre-clinical, techniques, sequences, 1H, X-nuclei, Artificial Intelligence (AI), hyperpolarization.
Results
We selected clinical works (n = 51), preclinical studies (n = 35) and AI MRS application papers (n = 15) published within the last two decades. The methodological papers (n = 62) were taken into account since the technique first description.
Conclusions
Given the development of treatments targeting specific cancer metabolic pathways, MRS could play a key role in allowing non-invasive assessment for patient diagnosis and stratification, predicting and monitoring treatment responses and prognosis. The characterization of gliomas through MRS will benefit of a wide synergy among scientists and clinicians of different specialties within the context of new translational competences. Head coils, MRI hardware and post-processing analysis progress, advances in research, experts’ consensus recommendations and specific professionalizing programs will make the technique increasingly trustworthy, responsive, accessible.
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22
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Gong K, Han PK, El Fakhri G, Ma C, Li Q. Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning. NMR IN BIOMEDICINE 2022; 35:e4224. [PMID: 31865615 PMCID: PMC7306418 DOI: 10.1002/nbm.4224] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 10/21/2019] [Accepted: 10/22/2019] [Indexed: 05/07/2023]
Abstract
Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.
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Affiliation(s)
| | | | | | - Chao Ma
- Correspondence Chao Ma and Quanzheng Li, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, ,
| | - Quanzheng Li
- Correspondence Chao Ma and Quanzheng Li, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, ,
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23
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Xie YR, Castro DC, Rubakhin SS, Sweedler JV, Lam F. Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling. Anal Chem 2022; 94:5335-5343. [PMID: 35324161 DOI: 10.1021/acs.analchem.1c05279] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Mass spectrometry imaging (MSI) allows for untargeted mapping of the chemical composition of tissues with attomole detection limits. MSI using Fourier transform (FT)-based mass spectrometers, such as FT-ion cyclotron resonance (FT-ICR), grants the ability to examine the chemical space with unmatched mass resolution and mass accuracy. However, direct imaging of large tissue samples using FT-ICR is slow. In this work, we present an approach that combines the subspace modeling of ICR temporal signals with compressed sensing to accelerate high-resolution FT-ICR MSI. A joint subspace and spatial sparsity constrained model computationally reconstructs high-resolution MSI data from the sparsely sampled transients with reduced duration, allowing a significant reduction in imaging time. Simulation studies and experimental implementation of the proposed method in investigation of brain tissues demonstrate a 10-fold enhancement in throughput of FT-ICR MSI, without the need for instrumental or hardware modifications.
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24
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Li Y, Wang Z, Lam F. SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints. IEEE Trans Biomed Eng 2022; 69:3087-3097. [PMID: 35320082 DOI: 10.1109/tbme.2022.3161417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoen-coder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE 1 H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE 1 H-MRSI of the brain, potentially useful for various clinical applications.
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25
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Huang J, Lai JHC, Han X, Chen Z, Xiao P, Liu Y, Chen L, Xu J, Chan KWY. Sensitivity schemes for dynamic glucose-enhanced magnetic resonance imaging to detect glucose uptake and clearance in mouse brain at 3 T. NMR IN BIOMEDICINE 2022; 35:e4640. [PMID: 34750891 DOI: 10.1002/nbm.4640] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 08/30/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
We investigated three dynamic glucose-enhanced (DGE) MRI methods for sensitively monitoring glucose uptake and clearance in both brain parenchyma and cerebrospinal fluid (CSF) at clinical field strength (3 T). By comparing three sequences, namely, Carr-Purcell-Meiboom-Gill (CPMG), on-resonance variable delay multipulse (onVDMP), and on-resonance spin-lock (onSL), a high-sensitivity DGE MRI scheme with truncated multilinear singular value decomposition (MLSVD) denoising was proposed. The CPMG method showed the highest sensitivity in detecting the parenchymal DGE signal among the three methods, while both onVDMP and onSL were more robust for CSF DGE imaging. Here, onVDMP was applied for CSF imaging, as it displayed the best stability of the DGE results in this study. The truncated MLSVD denoising method was incorporated to further improve the sensitivity. The proposed DGE MRI scheme was examined in mouse brain with 50%/25%/12.5% w/w D-glucose injections. The results showed that this combination could detect DGE signal changes from the brain parenchyma and CSF with as low as a 12.5% w/w D-glucose injection. The proposed DGE MRI schemes could sensitively detect the glucose signal change from brain parenchyma and CSF after D-glucose injection at a clinically relevant concentration, demonstrating high potential for clinical translation.
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Affiliation(s)
- Jianpan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Joseph H C Lai
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Xiongqi Han
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Zilin Chen
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Peng Xiao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yang Liu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Lin Chen
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Jiadi Xu
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kannie W Y Chan
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, China
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26
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Hangel G, Niess E, Lazen P, Bednarik P, Bogner W, Strasser B. Emerging methods and applications of ultra-high field MR spectroscopic imaging in the human brain. Anal Biochem 2022; 638:114479. [PMID: 34838516 DOI: 10.1016/j.ab.2021.114479] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 10/15/2021] [Accepted: 11/16/2021] [Indexed: 12/21/2022]
Abstract
Magnetic Resonance Spectroscopic Imaging (MRSI) of the brain enables insights into the metabolic changes and fluxes in diseases such as tumors, multiple sclerosis, epilepsy, or hepatic encephalopathy, as well as insights into general brain functionality. However, the routine application of MRSI is mostly hampered by very low signal-to-noise ratios (SNR) due to the low concentrations of metabolites, about 10000 times lower than water. Furthermore, MRSI spectra have a dense information content with many overlapping metabolite resonances, especially for proton MRSI. MRI scanners at ultra-high field strengths, like 7 T or above, offer the opportunity to increase SNR, as well as the separation between resonances, thus promising to solve both challenges. Yet, MRSI at ultra-high field strengths is challenged by decreased B0- and B1-homogeneity, shorter T2 relaxation times, stronger chemical shift displacement errors, and aggravated lipid contamination. Therefore, to capitalize on the advantages of ultra-high field strengths, these challenges must be overcome. This review focuses on the challenges MRSI of the human brain faces at ultra-high field strength, as well as the possible applications to this date.
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Affiliation(s)
- Gilbert Hangel
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria; Department of Neurosurgery, Medical University of Vienna, Austria
| | - Eva Niess
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Philipp Lazen
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Petr Bednarik
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Wolfgang Bogner
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Bernhard Strasser
- High Field MR Centre, Department of Medical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria.
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27
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Song JE, Kim DH. Improved Multi-Echo Gradient-Echo-Based Myelin Water Fraction Mapping Using Dimensionality Reduction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:27-38. [PMID: 34357864 DOI: 10.1109/tmi.2021.3102977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Multi-echo gradient-echo (mGRE)-based myelin water fraction (MWF) mapping is a promising myelin water imaging (MWI) modality but is vulnerable to noise and artifact corruption. The linear dimensionality reduction (LDR) method has recently shown improvements with regard to these challenges. However, the magnitude value based low rank operators have been shown to misestimate the MWF for regions with [Formula: see text] anisotropy. This paper presents a nonlinear dimensionality reduction (NLDR) method to estimate the MWF map better by encouraging nonlinear low dimensionality of mGRE signal sources. Specifically, we implemented a fully connected deep autoencoder to extract the low-dimensional features of complex-valued signals and incorporated a sparse regularization to separate the anomaly sources that do not reside in the low-dimensional manifold. Simulations and in vivo experiments were performed to evaluate the accuracy of the MWF map under various situations. The proposed NLDR-based MWF improves the accuracy of the MWF map over the conventional nonlinear least-squares method and the LDR-based MWF and maintains robustness against noise and artifact corruption.
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28
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Dai E, Lee PK, Dong Z, Fu F, Setsompop K, McNab JA. Distortion-Free Diffusion Imaging Using Self-Navigated Cartesian Echo-Planar Time Resolved Acquisition and Joint Magnitude and Phase Constrained Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:63-74. [PMID: 34383645 PMCID: PMC8799377 DOI: 10.1109/tmi.2021.3104291] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Echo-planar time resolved imaging (EPTI) is an effective approach for acquiring high-quality distortion-free images with a multi-shot EPI (ms-EPI) readout. As with traditional ms-EPI acquisitions, inter-shot phase variations present a main challenge when incorporating EPTI into a diffusion-prepared pulse sequence. The aim of this study is to develop a self-navigated Cartesian EPTI-based (scEPTI) acquisition together with a magnitude and phase constrained reconstruction for distortion-free diffusion imaging. A self-navigated Cartesian EPTI-based diffusion-prepared pulse sequence is designed. The different phase components in EPTI diffusion signal are analyzed and an approach to synthesize a fully phase-matched navigator for the inter-shot phase correction is demonstrated. Lastly, EPTI contains richer magnitude and phase information than conventional ms-EPI, such as the magnitude and phase correlations along the temporal dimension. The potential of these magnitude and phase correlations to enhance the reconstruction is explored. The reconstruction results with and without phase matching and with and without phase or magnitude constraints are compared. Compared with reconstruction without phase matching, the proposed phase matching method can improve the accuracy of inter-shot phase correction and reduce signal corruption in the final diffusion images. Magnitude constraints further improve image quality by suppressing the background noise and thereby increasing SNR, while phase constraints can mitigate possible image blurring from adding magnitude constraints. The high-quality distortion-free diffusion images and simultaneous diffusion-relaxometry imaging capacity provided by the proposed EPTI design represent a highly valuable tool for both clinical and neuroscientific assessments of tissue microstructure.
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29
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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: 9] [Impact Index Per Article: 3.0] [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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30
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Li X, Strasser B, Neuberger U, Vollmuth P, Bendszus M, Wick W, Dietrich J, Batchelor TT, Cahill DP, Andronesi OC. Deep learning super-resolution magnetic resonance spectroscopic imaging of brain metabolism and mutant isocitrate dehydrogenase glioma. Neurooncol Adv 2022; 4:vdac071. [PMID: 35911635 PMCID: PMC9332900 DOI: 10.1093/noajnl/vdac071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Magnetic resonance spectroscopic imaging (MRSI) can be used in glioma patients to map the metabolic alterations associated with IDH1,2 mutations that are central criteria for glioma diagnosis. The aim of this study was to achieve super-resolution (SR) MRSI using deep learning to image tumor metabolism in patients with mutant IDH glioma. METHODS We developed a deep learning method based on generative adversarial network (GAN) using Unet as generator network to upsample MRSI by a factor of 4. Neural networks were trained on simulated metabolic images from 75 glioma patients. The performance of deep neuronal networks was evaluated on MRSI data measured in 20 glioma patients and 10 healthy controls at 3T with a whole-brain 3D MRSI protocol optimized for detection of d-2-hydroxyglutarate (2HG). To further enhance structural details of metabolic maps we used prior information from high-resolution anatomical MR imaging. SR MRSI was compared to ground truth by Mann-Whitney U-test of peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM), feature-based similarity index measure (FSIM), and mean opinion score (MOS). RESULTS Deep learning SR improved PSNR by 17%, SSIM by 5%, FSIM by 7%, and MOS by 30% compared to conventional interpolation methods. In mutant IDH glioma patients proposed method provided the highest resolution for 2HG maps to clearly delineate tumor margins and tumor heterogeneity. CONCLUSIONS Our results indicate that proposed deep learning methods are effective in enhancing spatial resolution of metabolite maps. Patient results suggest that this may have great clinical potential for image guided precision oncology therapy.
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Affiliation(s)
- Xianqi Li
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Mathematical Sciences, Florida Institute of Technology, Melbourne, Florida, USA
| | - Bernhard Strasser
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jorg Dietrich
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tracy T Batchelor
- Department of Neurology, Brigham and Women Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ovidiu C Andronesi
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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31
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Ruhm L, Avdievich N, Ziegs T, Nagel AM, De Feyter HM, de Graaf RA, Henning A. Deuterium metabolic imaging in the human brain at 9.4 Tesla with high spatial and temporal resolution. Neuroimage 2021; 244:118639. [PMID: 34637905 PMCID: PMC8591372 DOI: 10.1016/j.neuroimage.2021.118639] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/10/2021] [Accepted: 10/05/2021] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To present first highly spatially resolved deuterium metabolic imaging (DMI) measurements of the human brain acquired with a dedicated coil design and a fast chemical shift imaging (CSI) sequence at an ultrahigh field strength of B0 = 9.4 T. 2H metabolic measurements with a temporal resolution of 10 min enabled the investigation of the glucose metabolism in healthy human subjects. METHODS The study was performed with a double-tuned coil with 10 TxRx channels for 1H and 8TxRx/2Rx channels for 2H and an Ernst angle 3D CSI sequence with a nominal spatial resolution of 2.97 ml and a temporal resolution of 10 min. RESULTS The metabolism of [6,6'-2H2]-labeled glucose due to the TCA cycle could be made visible in high resolution metabolite images of deuterated water, glucose and Glx over the entire human brain. CONCLUSION X-nuclei MRSI as DMI can highly benefit from ultrahigh field strength enabling higher temporal and spatial resolutions.
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Affiliation(s)
- Loreen Ruhm
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany; IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls University of Tübingen, Germany.
| | - Nikolai Avdievich
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Theresia Ziegs
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany; IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls University of Tübingen, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Erlangen, Germany; Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Henk M De Feyter
- Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Robin A de Graaf
- Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Anke Henning
- High-Field MR Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas/Texas, United States
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Wang Z, Li Y, Lam F. High-resolution, 3D multi-TE 1 H MRSI using fast spatiospectral encoding and subspace imaging. Magn Reson Med 2021; 87:1103-1118. [PMID: 34752641 DOI: 10.1002/mrm.29015] [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: 02/22/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a novel method to achieve fast, high-resolution, 3D multi-TE 1 H-MRSI of the brain. METHODS A new multi-TE MRSI acquisition strategy was developed that integrates slab selective excitation with adiabatic refocusing for better volume coverage, rapid spatiospectral encoding, sparse multi-TE sampling, and interleaved water navigators for field mapping and calibration. Special data processing strategies were developed to interpolate the sparsely sampled data, remove nuisance signals, and reconstruct multi-TE spatiospectral distributions with high SNR. Phantom and in vivo experiments have been carried out to demonstrate the capability of the proposed method. RESULTS The proposed acquisition can produce multi-TE 1 H-MRSI data with three TEs at a nominal spatial resolution of 3.4 × 3.4 × 5.3 mm3 in around 20 min. High-SNR brain metabolite spatiospectral reconstructions can be obtained from both a metabolite phantom and in vivo experiments by the proposed method. CONCLUSION High-resolution, 3D multi-TE 1 H-MRSI of the brain can be achieved within clinically feasible time. This capability, with further optimizations, could be translated to clinical applications and neuroscience studies where simultaneously mapping metabolites and neurotransmitters and TE-dependent molecular spectral changes are of interest.
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Affiliation(s)
- Zepeng Wang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yahang Li
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Fan Lam
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Guo R, Ma C, Li Y, Zhao Y, Wang T, Li Y, El Fakhri G, Liang ZP. High-Resolution Label-Free Molecular Imaging of Brain Tumor. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3049-3052. [PMID: 34891886 DOI: 10.1109/embc46164.2021.9630623] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Molecular imaging has long been recognized as an important tool for diagnosis, characterization, and monitoring of treatment responses of brain tumors. Magnetic resonance spectroscopic imaging (MRSI) is a label-free molecular imaging technique capable of mapping metabolite distributions non-invasively. Several metabolites detectable by MRSI, including Choline, Lactate and N-Acetyl Aspartate, have been proved useful biomarkers for brain tumor characterization. However, clinical application of MRSI has been limited by poor resolution, small spatial coverage, low signal-to-noise ratio and long scan time. This work presents a novel MRSI method for fast, high-resolution metabolic imaging of brain tumor. This method synergistically integrates fast acquisition sequence, sparse sampling, subspace modeling and machine learning to enable 3D mapping of brain metabolites with a spatial resolution of 2.0×3.0×3.0 mm3 in a 7-minute scan. Experimental results obtained from patients with diagnosed brain tumor showed great promise for capturing small-size tumors and revealing intra-tumor metabolic heterogeneities.Clinical Relevance - This paper presents a novel technique for label-free molecular imaging of brain tumor. With further development, this technology may enable many potential clinical applications, from tumor detection, characterization, to assessment of treatment efficacy.
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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: 1.3] [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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Manhard MK, Stockmann J, Liao C, Park D, Han S, Fair M, van den Boomen M, Polimeni J, Bilgic B, Setsompop K. A multi-inversion multi-echo spin and gradient echo echo planar imaging sequence with low image distortion for rapid quantitative parameter mapping and synthetic image contrasts. Magn Reson Med 2021; 86:866-880. [PMID: 33764563 PMCID: PMC8793364 DOI: 10.1002/mrm.28761] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 02/02/2021] [Accepted: 02/12/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE Brain imaging exams typically take 10-20 min and involve multiple sequential acquisitions. A low-distortion whole-brain echo planar imaging (EPI)-based approach was developed to efficiently encode multiple contrasts in one acquisition, allowing for calculation of quantitative parameter maps and synthetic contrast-weighted images. METHODS Inversion prepared spin- and gradient-echo EPI was developed with slice-order shuffling across measurements for efficient acquisition with T1 , T2 , and T 2 ∗ weighting. A dictionary-matching approach was used to fit the images to quantitative parameter maps, which in turn were used to create synthetic weighted images with typical clinical contrasts. Dynamic slice-optimized multi-coil shimming with a B0 shim array was used to reduce B0 inhomogeneity and, therefore, image distortion by >50%. Multi-shot EPI was also implemented to minimize distortion and blurring while enabling high in-plane resolution. A low-rank reconstruction approach was used to mitigate errors from shot-to-shot phase variation. RESULTS The slice-optimized shimming approach was combined with in-plane parallel-imaging acceleration of 4× to enable single-shot EPI with more than eight-fold distortion reduction. The proposed sequence efficiently obtained 40 contrasts across the whole-brain in just over 1 min at 1.2 × 1.2 × 3 mm resolution. The multi-shot variant of the sequence achieved higher in-plane resolution of 1 × 1 × 4 mm with good image quality in 4 min. Derived quantitative maps showed comparable values to conventional mapping methods. CONCLUSION The approach allows fast whole-brain imaging with quantitative parameter maps and synthetic weighted contrasts. The slice-optimized multi-coil shimming and multi-shot reconstruction approaches result in minimal EPI distortion, giving the sequence the potential to be used in rapid screening applications.
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Affiliation(s)
- Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jason Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Daniel Park
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sohyun Han
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea, Republic of
| | - Merlin Fair
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Maaike van den Boomen
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Jon Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
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Li Y, Xiong J, Guo R, Zhao Y, Li Y, Liang ZP. Improved estimation of myelin water fractions with learned parameter distributions. Magn Reson Med 2021; 86:2795-2809. [PMID: 34216050 DOI: 10.1002/mrm.28889] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data. METHODS A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense. RESULTS Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials. CONCLUSION This paper addressed the problem of robust MWF estimation from gradient-echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.
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Affiliation(s)
- Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jiahui Xiong
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Saucedo A, Macey PM, Thomas MA. Accelerated radial echo-planar spectroscopic imaging using golden angle view-ordering and compressed-sensing reconstruction with total variation regularization. Magn Reson Med 2021; 86:46-61. [PMID: 33604944 PMCID: PMC11616894 DOI: 10.1002/mrm.28728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 12/30/2020] [Accepted: 01/20/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE To implement a novel, accelerated, 2D radial echo-planar spectroscopic imaging (REPSI) sequence using undersampled radial k-space trajectories and compressed-sensing reconstruction, and to compare results with those from an undersampled Cartesian spectroscopic sequence. METHODS The REPSI sequence was implemented using golden-angle view-ordering on a 3T MRI scanner. Radial and Cartesian echo-planar spectroscopic imaging (EPSI) data were acquired at six acceleration factors, each with time-equivalent scan durations, and reconstructed using compressed sensing with total variation regularization. Results from prospectively and retrospectively undersampled phantom and in vivo brain data were compared over estimated concentrations and Cramer-Rao lower-bound values, normalized RMS errors of reconstructed metabolite maps, and percent absolute differences between fully sampled and reconstructed spectroscopic images. RESULTS The REPSI method with compressed sensing is able to tolerate greater reductions in scan time compared with EPSI. The reconstruction and quantitation metrics (i.e., spectral normalized RMS error maps, metabolite map normalized RMS error values [e.g., for total N-acetyl asparate, REPSI = 9.4% vs EPSI = 16.3%; acceleration factor = 2.5], percent absolute difference maps, and concentration and Cramer-Rao lower-bound estimates) showed that accelerated REPSI can reduce the scan time by a factor of 2.5 while retaining image and quantitation quality. CONCLUSION Accelerated MRSI using undersampled radial echo-planar acquisitions provides greater reconstruction accuracy and more reliable quantitation for a range of acceleration factors compared with time-equivalent compressed-sensing reconstructions of undersampled Cartesian EPSI. Compared to the Cartesian approach, radial undersampling with compressed sensing could help reduce 2D spectroscopic imaging acquisition time, and offers a better trade-off between imaging speed and quality.
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Affiliation(s)
- Andres Saucedo
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA
- Physics and Biology in Medicine Interdepartmental Graduate Program, University of California Los Angeles, Los Angeles, California, USA
| | - Paul M. Macey
- School of Nursing, University of California, Los Angeles, Los Angeles, California, USA
| | - M. Albert Thomas
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA
- Physics and Biology in Medicine Interdepartmental Graduate Program, University of California Los Angeles, Los Angeles, California, USA
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38
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Ji B, Hosseini Z, Wang L, Zhou L, Tu X, Mao H. Spectral Wavelet-feature Analysis and Classification Assisted Denoising for enhancing magnetic resonance spectroscopy. NMR IN BIOMEDICINE 2021; 34:e4497. [PMID: 33751691 PMCID: PMC8969585 DOI: 10.1002/nbm.4497] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 01/21/2021] [Accepted: 02/08/2021] [Indexed: 05/11/2023]
Abstract
Magnetic resonance spectroscopy (MRS) is capable of revealing important biochemical and metabolic information of tissues noninvasively. However, the low concentrations of metabolites often lead to poor signal-to-noise ratio (SNR) and a long acquisition time. Therefore, the applications of MRS in detection and quantitative measurements of metabolites in vivo remain limited. Reducing or even eliminating noise can improve SNR sufficiently to obtain high quality spectra in addition to increasing the number of signal averaging (NSA) or the field strength, both of which are limited in clinical applications. We present a Spectral Wavelet-feature ANalysis and Classification Assisted Denoising (SWANCAD) approach to differentiate signal and noise peaks in magnetic resonance spectra based on their respective wavelet features, followed by removing the identified noise components to improve SNR. The performance of this new denoising approach was evaluated by measuring and comparing SNRs and quantified metabolite levels of low NSA spectra (e.g. NSA = 8) before and after denoising using the SWANCAD approach or by conventional spectral fitting and denoising methods, such as LCModel and wavelet threshold methods, as well as the high NSA spectra (e.g. NSA = 192) recorded in the same sampling volumes. The results demonstrated that SWANCAD offers a more effective way to detect the signals and improve SNR by removing noise from the noisy spectra collected with low NSA or in the subminute scan time (e.g. NSA = 8 or 16 s). The potential applications of SWANCAD include using low NSA to accelerate MRS acquisition while maintaining adequate spectroscopic information for detection and quantification of the metabolites of interest when a limited time is available for an MRS examination in the clinical setting.
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Affiliation(s)
- Bing Ji
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, The United States of America
| | - Zahra Hosseini
- MR R&D Collaborations, Siemens Medical Solutions Inc., Atlanta, Georgia, The United States of America
| | - Liya Wang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, The United States of America
- Department of Radiology, The People’s Hospital of Longhua, Shenzhen, Guangdong, China
| | - Lei Zhou
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, The United States of America
| | - Xinhua Tu
- School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
| | - Hui Mao
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, The United States of America
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Bogner W, Otazo R, Henning A. Accelerated MR spectroscopic imaging-a review of current and emerging techniques. NMR IN BIOMEDICINE 2021; 34:e4314. [PMID: 32399974 PMCID: PMC8244067 DOI: 10.1002/nbm.4314] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 03/24/2020] [Accepted: 03/30/2020] [Indexed: 05/14/2023]
Abstract
Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an enormous evolution from theoretical concepts in the early 1980s to the robust imaging technique that it is today. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely phase-encoded acquisition technique to a method that today combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, undersampling of k-space and time domain, and use of spatial-spectral prior knowledge in the reconstruction. In this way in vivo MRSI has considerably advanced in terms of spatial coverage, spatial resolution, acquisition speed, artifact suppression, number of detectable metabolites and quantification precision. Acceleration not only has been the enabling factor in high-resolution whole-brain 1 H-MRSI, but today is also common in non-proton MRSI (31 P, 2 H and 13 C) and applied in many different organs. In this process, MRSI techniques had to constantly adapt, but have also benefitted from the significant increase of magnetic field strength boosting the signal-to-noise ratio along with high gradient fidelity and high-density receive arrays. In combination with recent trends in image reconstruction and much improved computation power, these advances led to a number of novel developments with respect to MRSI acceleration. Today MRSI allows for non-invasive and non-ionizing mapping of the spatial distribution of various metabolites' tissue concentrations in animals or humans, is applied for clinical diagnostics and has been established as an important tool for neuro-scientific and metabolism research. This review highlights the developments of the last five years and puts them into the context of earlier MRSI acceleration techniques. In addition to 1 H-MRSI it also includes other relevant nuclei and is not limited to certain body regions or specific applications.
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Affiliation(s)
- Wolfgang Bogner
- High‐Field MR Center, Department of Biomedical Imaging and Image‐Guided TherapyMedical University of ViennaViennaAustria
| | - Ricardo Otazo
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew York, New YorkUSA
| | - Anke Henning
- Max Planck Institute for Biological CyberneticsTübingenGermany
- Advanced Imaging Research Center, UT Southwestern Medical CenterDallasTexasUSA
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Bartnik-Olson BL, Alger JR, Babikian T, Harris AD, Holshouser B, Kirov II, Maudsley AA, Thompson PM, Dennis EL, Tate DF, Wilde EA, Lin A. The clinical utility of proton magnetic resonance spectroscopy in traumatic brain injury: recommendations from the ENIGMA MRS working group. Brain Imaging Behav 2021; 15:504-525. [PMID: 32797399 PMCID: PMC7882010 DOI: 10.1007/s11682-020-00330-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Proton (1H) magnetic resonance spectroscopy provides a non-invasive and quantitative measure of brain metabolites. Traumatic brain injury impacts cerebral metabolism and a number of research groups have successfully used this technique as a biomarker of injury and/or outcome in both pediatric and adult TBI populations. However, this technique is underutilized, with studies being performed primarily at centers with access to MR research support. In this paper we present a technical introduction to the acquisition and analysis of in vivo 1H magnetic resonance spectroscopy and review 1H magnetic resonance spectroscopy findings in different injury populations. In addition, we propose a basic 1H magnetic resonance spectroscopy data acquisition scheme (Supplemental Information) that can be added to any imaging protocol, regardless of clinical magnetic resonance platform. We outline a number of considerations for study design as a way of encouraging the use of 1H magnetic resonance spectroscopy in the study of traumatic brain injury, as well as recommendations to improve data harmonization across groups already using this technique.
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Affiliation(s)
| | - Jeffry R Alger
- Departments of Neurology and Radiology, University of California Los Angeles, Los Angeles, CA, USA
- NeuroSpectroScopics LLC, Sherman Oaks, Los Angeles, CA, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Talin Babikian
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, USA
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Ashley D Harris
- Department of Radiology, University of Calgary, Calgary, Canada
- Child and Adolescent Imaging Research Program, Alberta Children's Hospital Research Institute and the Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Barbara Holshouser
- Department of Radiology, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Ivan I Kirov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Andrew A Maudsley
- Department of Radiology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and Ophthalmology, USC, Los Angeles, CA, USA
| | - Emily L Dennis
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Boston, MA, USA
| | - David F Tate
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Alexander Lin
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Emir UE, Sood J, Chiew M, Thomas MA, Lane SP. High-resolution metabolic mapping of the cerebellum using 2D zoom magnetic resonance spectroscopic imaging. Magn Reson Med 2020; 85:2349-2358. [PMID: 33283917 DOI: 10.1002/mrm.28614] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/03/2020] [Accepted: 11/04/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE The human cerebellum plays an important role in the functional activity of the cerebrum, ranging from motor to cognitive systems given its relaying role between the spinal cord and cerebrum. The cerebellum poses many challenges to Magnetic Resonance Spectroscopic Imaging (MRSI) due to its caudal location, susceptibility to physiological artifacts, and partial volume artifacts resulting from its complex anatomical structure. Thus, in the present study, we propose a high-resolution MRSI acquisition scheme for the cerebellum. METHODS A zoom or reduced field of view (rFOV) metabolite-cycled MRSI acquisition at 3 Tesla, with a grid of 48 × 48, was developed to achieve a nominal resolution of 62.5 μL. Single-slice rFOV MRSI data were acquired from the cerebellum of 5 healthy subjects with a nominal resolution of 2.5 × 2.5 × 10 mm3 in 9.6 min. Spectra were quantified using the LCModel package. A spatially unbiased atlas template of the cerebellum was used to analyze metabolite distributions in the cerebellum. RESULTS The superior quality of the achieved spectra-enabled generation of high-resolution metabolic maps of total N-acetylaspartate, total Creatine (tCr), total Choline (tCho), glutamate+glutamine, and myo-inositol, with Cramér-Rao lower bounds below 50%. A template-based regions of interest (ROI) analysis resulted in spatially dependent metabolite distributions in 9 ROIs. The group-averaged high-resolution metabolite maps across subjects increased the contrast-to-noise ratio between cerebellum regions. CONCLUSION These findings indicate that very high-resolution metabolite probing of the cerebellum is feasible using rFOV or zoomed MRSI at 3 Tesla.
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Affiliation(s)
- Uzay E Emir
- School of Health Sciences, Purdue University, West Lafayette, Indiana, USA.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Jaiyta Sood
- School of Health Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Micheal Albert Thomas
- Department of Radiology, University of California Los Angeles, Los Angeles, California, USA
| | - Sean P Lane
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
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Li Y, Wang T, Zhang T, Lin Z, Li Y, Guo R, Zhao Y, Meng Z, Liu J, Yu X, Liang ZP, Nachev P. Fast high-resolution metabolic imaging of acute stroke with 3D magnetic resonance spectroscopy. Brain 2020; 143:3225-3233. [PMID: 33141145 PMCID: PMC7719019 DOI: 10.1093/brain/awaa264] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/05/2020] [Accepted: 06/29/2020] [Indexed: 01/24/2023] Open
Abstract
Impaired oxygen and cellular metabolism is a hallmark of ischaemic injury in acute stroke. Magnetic resonance spectroscopic imaging (MRSI) has long been recognized as a potentially powerful tool for non-invasive metabolic imaging. Nonetheless, long acquisition time, poor spatial resolution, and narrow coverage have limited its clinical application. Here we investigated the feasibility and potential clinical utility of rapid, high spatial resolution, near whole-brain 3D metabolic imaging based on a novel MRSI technology. In an 8-min scan, we simultaneously obtained 3D maps of N-acetylaspartate and lactate at a nominal spatial resolution of 2.0 × 3.0 × 3.0 mm3 with near whole-brain coverage from a cohort of 18 patients with acute ischaemic stroke. Serial structural and perfusion MRI was used to define detailed spatial maps of tissue-level outcomes against which high-resolution metabolic changes were evaluated. Within hypoperfused tissue, the lactate signal was higher in areas that ultimately infarcted compared with those that recovered (P < 0.0001). Both lactate (P < 0.0001) and N-acetylaspartate (P < 0.001) differed between infarcted and other regions. Within the areas of diffusion-weighted abnormality, lactate was lower where recovery was observed compared with elsewhere (P < 0.001). This feasibility study supports further investigation of fast high-resolution MRSI in acute stroke.
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Affiliation(s)
- Yao Li
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Tianyao Wang
- Radiology Department, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
| | - Tianxiao Zhang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zengping Lin
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ziyu Meng
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jun Liu
- Radiology Department, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Parashkev Nachev
- High-Dimensional Neurology Group, Institute of Neurology, University College London, London, UK
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Chen L, Cao S, Koehler RC, van Zijl PCM, Xu J. High-sensitivity CEST mapping using a spatiotemporal correlation-enhanced method. Magn Reson Med 2020; 84:3342-3350. [PMID: 32597519 PMCID: PMC7722217 DOI: 10.1002/mrm.28380] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 05/01/2020] [Accepted: 05/23/2020] [Indexed: 01/29/2023]
Abstract
PURPOSE To obtain high-sensitivity CEST maps by exploiting the spatiotemporal correlation between CEST images. METHODS A postprocessing method accomplished by multilinear singular value decomposition (MLSVD) was used to enhance the CEST SNR by exploiting the correlation between the Z-spectrum for each voxel and the low-rank property of the overall CEST data. The performance of this method was evaluated using CrCEST in ischemic mouse brain at 11.7 tesla. Then, MLSVD CEST was applied to obtain Cr, amide, and amine CEST maps of the ischemic mouse brain to demonstrate its general applications. RESULTS Complex-valued Gaussian noise was added to CEST k-space data to mimic a low SNR situation. MLSVD CEST analysis was able to suppress the noise, recover the degraded CEST peak, and provide better CrCEST quality compared to the smoothing and singular value decomposition (SVD)-based denoising methods. High-resolution Cr, amide, and amine CEST maps of an ischemic stroke using MLSVD CEST suggest that CrCEST is also a sensitive pH mapping method, and a wide range of pH changes can be detected by combing CrCEST with amine CEST at high magnetic fields. CONCLUSION MLSVD CEST provides a simple and efficient way to improve the SNR of CEST images.
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Affiliation(s)
- Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA,Corresponding Author: Lin Chen, Ph.D., Kennedy Krieger Institute, Johns Hopkins University School of Medicine, 707 N. Broadway, Baltimore, MD, 21205,
| | - Suyi Cao
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Raymond C. Koehler
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter C. M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Clifford B, Gu Y, Liu Y, Kim K, Huang S, Li Y, Lam F, Liang ZP, Yu X. High-Resolution Dynamic 31P-MR Spectroscopic Imaging for Mapping Mitochondrial Function. IEEE Trans Biomed Eng 2020; 67:2745-2753. [PMID: 32011244 PMCID: PMC7384926 DOI: 10.1109/tbme.2020.2969892] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To enable non-invasive dynamic metabolic mapping in rodent model studies of mitochondrial function using 31P-MR spectroscopic imaging (MRSI). METHODS We developed a novel method for high-resolution dynamic 31P-MRSI. The method synergistically integrates physics-based models of spectral structures, biochemical modeling of molecular dynamics, and subspace learning to capture spatiospectral variations. Fast data acquisition was achieved using rapid spiral trajectories and sparse sampling of (k, t, T)-space; image reconstruction was accomplished using a low-rank tensor-based framework. RESULTS The proposed method provided high-resolution dynamic metabolic mapping in rat hindlimb at spatial and temporal resolutions of 4[Formula: see text]2 mm3 and 1.28 s, respectively. This allowed for in vivo mapping of the time-constant of phosphocreatine resynthesis, a well established index of mitochondrial oxidative capacity. Multiple rounds of in vivo experiments were performed to demonstrate reproducibility, and in vitro experiments were used to validate the accuracy of the estimated metabolite maps. CONCLUSIONS A new model-based method is proposed to achieve high-resolution dynamic 31P-MRSI. The proposed method's ability to delineate metabolic heterogeneity was demonstrated in rat hindlimb. SIGNIFICANCE Abnormal mitochondrial metabolism is a key cellular dysfunction in many prevalent diseases such as diabetes and heart disease; however, current understanding of mitochondrial function is mostly gained from studies on isolated mitochondria under nonphysiological conditions. The proposed method has the potential to open new avenues of research by allowing in vivo and longitudinal studies of mitochondrial dysfunction in disease development and progression.
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Affiliation(s)
- Bryan Clifford
- Department of Electrical and Computer Engineering and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Yuning Gu
- Department of Biomedical Engineering and the Case Center for Imaging Research, Case Western Reserve University
| | - Yuchi Liu
- Department of Biomedical Engineering and the Case Center for Imaging Research, Case Western Reserve University
| | - Kihwan Kim
- Department of Biomedical Engineering and the Case Center for Imaging Research, Case Western Reserve University
| | - Sherry Huang
- Department of Biomedical Engineering and the Case Center for Imaging Research, Case Western Reserve University
| | - Yudu Li
- Department of Electrical and Computer Engineering and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Fan Lam
- Department of Bioengineering and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Zhi-Pei Liang
- Department of Electrical and Computer Engineering and the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | - Xin Yu
- Departments of Biomedical Engineering, Radiology, and Physiology and Biophysics, as well as the Case Center for Imaging Research, Case Western Reserve University, Cleveland, OH 44106-7207 USA
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45
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Novel proton exchange rate MRI presents unique contrast in brains of ischemic stroke patients. J Neurosci Methods 2020; 346:108926. [PMID: 32896540 DOI: 10.1016/j.jneumeth.2020.108926] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 08/23/2020] [Accepted: 08/31/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND To map and quantify the proton exchange rate (kex) of brain tissues using improved omega plots in ischemic stroke patients and to investigate whether kex can serve as a potential endogenous surrogate imaging biomarker for detecting the metabolic state and the pathologic changes due to ischemic stroke. NEW METHOD Three sets of Z-spectra were acquired from seventeen ischemic stroke patients using a spin echo-echo planar imaging sequence with pre-saturation chemical exchange saturation transfer (CEST) pulse at B1 of 1.5, 2.5, and 3.5 μT, respectively. Pixel-wise kex was calculated from improved omega plot of water direct saturation (DS)-removed Z-spectral signals. RESULTS The derived kex maps can differentiate infarcts from contralateral normal brain tissues with significantly increased signal (893 ± 52 s-1vs. 739 ± 34 s-1, P < 0.001). COMPARISON WITH EXISTING METHOD(S) The kex maps were found to be different from conventional contrasts from diffusion-weighted imaging (DWI), CEST, and semi-solid magnetization transfer (MT) MRI. In brief, kex MRI showed larger lesion areas than DWI with different degrees and different lesion contrast compared to CEST and MT. CONCLUSIONS In this preliminary translational research, the kex MRI based on DS-removed omega plots has been demonstrated for in vivo imaging of clinical ischemic stroke patients. As a noninvasive and unique MRI contrast, kex MRI at 3 T may serve as a potential surrogate imaging biomarker for the metabolic changes of stroke and help for monitoring the evolution and the treatment of stroke.
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46
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Guo R, Zhao Y, Li Y, Wang T, Li Y, Sutton B, Liang ZP. Simultaneous QSM and metabolic imaging of the brain using SPICE: Further improvements in data acquisition and processing. Magn Reson Med 2020; 85:970-977. [PMID: 32810319 DOI: 10.1002/mrm.28459] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 07/09/2020] [Accepted: 07/12/2020] [Indexed: 01/23/2023]
Abstract
PURPOSE To achieve high-resolution mapping of brain tissue susceptibility in simultaneous QSM and metabolic imaging. METHODS Simultaneous QSM and metabolic imaging was first achieved using SPICE (spectroscopic imaging by exploiting spatiospectral correlation), but the QSM maps thus obtained were at relatively low-resolution (2.0 × 3.0 × 3.0 mm3 ). We overcome this limitation using an improved SPICE data acquisition method with the following novel features: 1) sampling (k, t)-space in dual densities, 2) sampling central k-space fully to achieve nominal spatial resolution of 3.0 × 3.0 × 3.0 mm3 for metabolic imaging, and 3) sampling outer k-space sparsely to achieve spatial resolution of 1.0 × 1.0 × 1.9 mm3 for QSM. To keep the scan time short, we acquired spatiospectral encodings in echo-planar spectroscopic imaging trajectories in central k-space but in CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) trajectories in outer k-space using blipped phase encodings. For data processing and image reconstruction, a union-of-subspaces model was used, effectively incorporating sensitivity encoding, spatial priors, and spectral priors of individual molecules. RESULTS In vivo experiments were carried out to evaluate the feasibility and potential of the proposed method. In a 6-min scan, QSM maps at 1.0 × 1.0 × 1.9 mm3 resolution and metabolic maps at 3.0 × 3.0 × 3.0 mm3 nominal resolution were obtained simultaneously. Compared with the original method, the QSM maps obtained using the new method reveal fine-scale brain structures more clearly. CONCLUSION We demonstrated the feasibility of achieving high-resolution QSM simultaneously with metabolic imaging using a modified SPICE acquisition method. The improved capability of SPICE may further enhance its practical utility in brain mapping.
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Affiliation(s)
- Rong Guo
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Yibo Zhao
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Yudu Li
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Tianyao Wang
- Department of Radiology, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, People's Republic of China
| | - Yao Li
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Brad Sutton
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Zhi-Pei Liang
- Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
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Tang L, Zhao Y, Li Y, Guo R, Clifford B, El Fakhri G, Ma C, Liang ZP, Luo J. Accelerated J-resolved 1 H-MRSI with limited and sparse sampling of ( k , t 1 , t 2 -space. Magn Reson Med 2020; 85:30-41. [PMID: 32726510 DOI: 10.1002/mrm.28413] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 06/13/2020] [Accepted: 06/15/2020] [Indexed: 01/18/2023]
Abstract
PURPOSE To accelerate the acquisition of J-resolved proton magnetic resonance spectroscopic imaging (1 H-MRSI) data for high-resolution mapping of brain metabolites and neurotransmitters. METHODS The proposed method used a subspace model to represent multidimensional spatiospectral functions, which significantly reduced the number of parameters to be determined from J-resolved 1 H-MRSI data. A semi-LASER-based (Localization by Adiabatic SElective Refocusing) echo-planar spectroscopic imaging (EPSI) sequence was used for data acquisition. The proposed data acquisition scheme sampled k , t 1 , t 2 -space in variable density, where t1 and t2 specify the J-coupling and chemical-shift encoding times, respectively. Selection of the J-coupling encoding times (or, echo time values) was based on a Cramer-Rao lower bound analysis, which were optimized for gamma-aminobutyric acid (GABA) detection. In image reconstruction, parameters of the subspace-based spatiospectral model were determined by solving a constrained optimization problem. RESULTS Feasibility of the proposed method was evaluated using both simulated and experimental data from a spectroscopic phantom. The phantom experimental results showed that the proposed method, with a factor of 12 acceleration in data acquisition, could determine the distribution of J-coupled molecules with expected accuracy. In vivo study with healthy human subjects also showed that 3D maps of brain metabolites and neurotransmitters can be obtained with a nominal spatial resolution of 3.0 × 3.0 × 4.8 mm3 from J-resolved 1 H-MRSI data acquired in 19.4 min. CONCLUSIONS This work demonstrated the feasibility of highly accelerated J-resolved 1 H-MRSI using limited and sparse sampling of k , t 1 , t 2 -space and subspace modeling. With further development, the proposed method may enable high-resolution mapping of brain metabolites and neurotransmitters in clinical applications.
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Affiliation(s)
- Lihong Tang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bryan Clifford
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Georges El Fakhri
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Chao Ma
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jie Luo
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Cao P, Cui D, Vardhanabhuti V, Hui ES. Development of fast deep learning quantification for magnetic resonance fingerprinting in vivo. Magn Reson Imaging 2020; 70:81-90. [DOI: 10.1016/j.mri.2020.03.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 03/10/2020] [Accepted: 03/25/2020] [Indexed: 12/20/2022]
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49
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Ho RJ, Lam F. High-Resolution 3D Spin-Echo MRSI Using Interleaved Water Navigators, Sparse Sampling and Subspace-Based Processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1465-1468. [PMID: 33018267 DOI: 10.1109/embc44109.2020.9176633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This work presents a new method to achieve accelerated, high-resolution magnetic resonance spectroscopic imaging (MRSI) with spin-echo excitations. A new data acquisition strategy is proposed that integrates adiabatic refocusing, elimination of lipid suppression, rapid spatiospectral encoding with sparse (k,t)-space sampling, and interleaved water navigators. This integration leads to a significantly improved combination of volume coverage, spatial resolution (approximately 3 × 3.4 × 4 mm3) and speed (< 10 minutes), while eliminating additional scans for field mapping and coil sensitivity estimation. A data processing strategy that integrates parallel imaging reconstruction and subspace-based processing is devised to produce high-SNR spatiospectral reconstruction from the sparsely sampled, noisy and highresolution MRSI data. Promising in vivo results have been obtained to demonstrate the potential of the proposed method.Clinical relevance- The proposed method enabled volumetric MRSI with a nominal resolution of 3 × 3.4 × 4 mm3 in less than 10 minutes. With further developments and optimizations, the proposed method is expected to be useful for providing molecular-level information of brain functions and diseases, and has the potential to provide new biomarkers for disease diagnosis and treatment monitoring.
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50
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Dong Z, Wang F, Reese TG, Bilgic B, Setsompop K. Echo planar time-resolved imaging with subspace reconstruction and optimized spatiotemporal encoding. Magn Reson Med 2020; 84:2442-2455. [PMID: 32333478 DOI: 10.1002/mrm.28295] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 03/01/2020] [Accepted: 03/31/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To develop new encoding and reconstruction techniques for fast multi-contrast/quantitative imaging. METHODS The recently proposed Echo Planar Time-resolved Imaging (EPTI) technique can achieve fast distortion- and blurring-free multi-contrast/quantitative imaging. In this work, a subspace reconstruction framework is developed to improve the reconstruction accuracy of EPTI at high encoding accelerations. The number of unknowns in the reconstruction is significantly reduced by modeling the temporal signal evolutions using low-rank subspace. As part of the proposed reconstruction approach, a B0 -update algorithm and a shot-to-shot B0 variation correction method are developed to enable the reconstruction of high-resolution tissue phase images and to mitigate artifacts from shot-to-shot phase variations. Moreover, the EPTI concept is extended to 3D k-space for 3D GE-EPTI, where a new "temporal-variant" of CAIPI encoding is proposed to further improve performance. RESULTS The effectiveness of the proposed subspace reconstruction was demonstrated first in 2D GESE EPTI, where the reconstruction achieved higher accuracy when compared to conventional B0 -informed GRAPPA. For 3D GE-EPTI, a retrospective undersampling experiment demonstrates that the new temporal-variant CAIPI encoding can achieve up to 72× acceleration with close to 2× reduction in reconstruction error when compared to conventional spatiotemporal-CAIPI encoding. In a prospective undersampling experiment, high-quality whole-brain T 2 ∗ and tissue phase maps at 1 mm isotropic resolution were acquired in 52 seconds at 3T using 3D GE-EPTI with temporal-variant CAIPI encoding. CONCLUSION The proposed subspace reconstruction and optimized temporal-variant CAIPI encoding can further improve the performance of EPTI for fast quantitative mapping.
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Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Timothy G Reese
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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