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Plummer JW, Hussain R, Bdaiwi AS, Costa ML, Willmering MM, Parra-Robles J, Cleveland ZI, Walkup L. Analytical corrections for B 1-inhomogeneity and signal decay in multi-slice 2D spiral hyperpolarized 129Xe MRI using keyhole reconstruction. Magn Reson Med 2024; 92:967-981. [PMID: 38297511 PMCID: PMC11209825 DOI: 10.1002/mrm.30028] [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: 10/25/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/02/2024]
Abstract
PURPOSE Hyperpolarized xenon MRI suffers from heterogeneous coil bias and magnetization decay that obscure pulmonary abnormalities. Non-physiological signal variability can be mitigated by measuring and mapping the nominal flip angle, and by rescaling the images to correct for signal bias and decay. While flip angle maps can be calculated from sequentially acquired images, scan time and breath-hold duration are doubled. Here, we exploit the low-frequency oversampling of 2D-spiral and keyhole reconstruction to measure flip angle maps from a single acquisition. METHODS Flip angle maps were calculated from two images generated from a single dataset using keyhole reconstructions and a Bloch-equation-based model suitable for hyperpolarized substances. Artifacts resulting from acquisition and reconstruction schemes (e.g., keyhole reconstruction radius, slice-selection profile, spiral-ordering, and oversampling) were assessed using point-spread functions. Simulated flip angle maps generated using keyhole reconstruction were compared against the paired-image approach using RMS error (RMSE). Finally, feasibility was demonstrated for in vivo xenon ventilation imaging. RESULTS Simulations demonstrated accurate flip angle maps and B1-inhomogeneity correction can be generated with only 1.25-fold central-oversampling and keyhole reconstruction radius = 5% (RMSE = 0.460°). These settings also generated accurate flip angle maps in a healthy control (RSME = 0.337°) and a person with cystic fibrosis (RMSE = 0.404°) in as little as 3.3 s. CONCLUSION Regional lung ventilation images with reduced impact of B1-inhomogeneity can be acquired rapidly by combining 2D-spiral acquisition, Bloch-equation-based modeling, and keyhole reconstruction. This approach will be especially useful for breath-hold studies where short scan durations are necessary, such as dynamic imaging and applications in children or people with severely compromised respiratory function.
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Affiliation(s)
- J. W. Plummer
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
| | - R. Hussain
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - A. S. Bdaiwi
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
| | - M. L. Costa
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
| | - M. M. Willmering
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - J. Parra-Robles
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Z. I. Cleveland
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
| | - L.L. Walkup
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
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Bdaiwi AS, Svoboda AM, Murdock KE, Hendricks A, Hossain MM, Kramer EL, Brewington JJ, Willmering MM, Woods JC, Walkup LL, Cleveland ZI. Quantifying abnormal alveolar microstructure in cystic fibrosis lung disease via hyperpolarized 129Xe diffusion MRI. J Cyst Fibros 2024; 23:926-935. [PMID: 38997823 PMCID: PMC11410525 DOI: 10.1016/j.jcf.2024.07.002] [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: 03/12/2024] [Revised: 06/05/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024]
Abstract
RATIONALE Cystic Fibrosis (CF) progresses through recurrent infection and inflammation, causing permanent lung function loss and airway remodeling. CT scans reveal abnormally low-density lung parenchyma in CF, but its microstructural nature remains insufficiently explored due to clinical CT limitations. To this end, diffusion-weighted 129Xe MRI is a non-invasive and validated measure of lung microstructure. In this work, we investigate microstructural changes in people with CF (pwCF) relative to age-matched, healthy subjects using comprehensive imaging and analysis involving pulmonary-function tests (PFTs), and 129Xe MRI. METHODS 38 healthy subjects (age 6-40; 17.2 ± 9.5 years) and 39 pwCF (age 6-40; 15.6 ± 8.0 years) underwent 129Xe-diffusion MRI and PFTs. The distribution of diffusion measurements (i.e., apparent diffusion coefficients (ADC) and morphometric parameters) was assessed via linear binning (LB). The resulting volume percentages of bins were compared between controls and pwCF. Mean ADC and morphometric parameters were also correlated with PFTs. RESULTS Mean whole-lung ADC correlated significantly with age (P < 0.001) for both controls and CF, and with PFTs (P < 0.05) specifically for pwCF. Although there was no significant difference in mean ADC between controls and pwCF (P = 0.334), age-adjusted LB indicated significant voxel-level diffusion (i.e., ADC and morphometric parameters) differences in pwCF compared to controls (P < 0.05). CONCLUSIONS 129Xe diffusion MRI revealed microstructural abnormalities in CF lung disease. Smaller microstructural size may reflect compression from overall higher lung density due to interstitial inflammation, fibrosis, or other pathological changes. While elevated microstructural size may indicate emphysema-like remodeling due to chronic inflammation and infection.
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Affiliation(s)
- Abdullah S Bdaiwi
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Alexandra M Svoboda
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; College of Medicine, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Kyle E Murdock
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Alexandra Hendricks
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH 45221, United States
| | - Md M Hossain
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
| | - Elizabeth L Kramer
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
| | - John J Brewington
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
| | - Matthew M Willmering
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States
| | - Jason C Woods
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States; Department of Physics, University of Cincinnati, Cincinnati, United States
| | - Laura L Walkup
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH 45221, United States; Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States
| | - Zackary I Cleveland
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH 45221, United States; Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, United States; Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States.
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Zhang Z, Li H, Xiao S, Zhou Q, Liu S, Zhou X, Fan L. Hyperpolarized Gas Imaging in Lung Diseases: Functional and Artificial Intelligence Perspective. Acad Radiol 2024:S1076-6332(24)00014-X. [PMID: 38233260 DOI: 10.1016/j.acra.2024.01.014] [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: 12/05/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
Pathophysiologic changes in lung diseases are often accompanied by changes in ventilation and gas exchange. Comprehensive evaluation of lung function cannot be obtained through chest X-ray and computed tomography. Proton-based lung MRI is particularly challenging due to low proton density within the lung tissue. In this review, we discuss an emerging technology--hyperpolarized gas MRI with inhaled 129Xe, which provides functional and microstructural information and has the potential as a clinical tool for detecting the early stage and progression of certain lung diseases. We review the hyperpolarized 129Xe MRI studies in patients with a range of pulmonary diseases, including chronic obstructive pulmonary disease, asthma, cystic fibrosis, pulmonary hypertension, radiation-induced lung injury and interstitial lung disease, and the applications of artificial intelligence were reviewed as well.
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Affiliation(s)
- Ziwei Zhang
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, People's Republic of China (Z.Z., S.L., L.F.)
| | - Haidong Li
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovative Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430071, China (H.L., S.X., Q.Z., X.Z.); University of Chinese Academy of Sciences, Beijing 100049, China (H.L., S.X., X.Z.)
| | - Sa Xiao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovative Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430071, China (H.L., S.X., Q.Z., X.Z.); University of Chinese Academy of Sciences, Beijing 100049, China (H.L., S.X., X.Z.)
| | - Qian Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovative Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430071, China (H.L., S.X., Q.Z., X.Z.)
| | - Shiyuan Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, People's Republic of China (Z.Z., S.L., L.F.)
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovative Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430071, China (H.L., S.X., Q.Z., X.Z.); University of Chinese Academy of Sciences, Beijing 100049, China (H.L., S.X., X.Z.)
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, People's Republic of China (Z.Z., S.L., L.F.).
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Zhou Q, Li H, Rao Q, Zhang M, Zhao X, Shen L, Fang Y, Li H, Liu X, Xiao S, Shi L, Han Y, Ye C, Zhou X. Assessment of pulmonary morphometry using hyperpolarized 129 Xe diffusion-weighted MRI with variable-sampling-ratio compressed sensing patterns. Med Phys 2023; 50:867-878. [PMID: 36196039 DOI: 10.1002/mp.16018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/26/2022] [Accepted: 09/24/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Hyperpolarized (HP) 129 Xe multiple b-values diffusion-weighted magnetic resonance imaging (DW-MRI) has been widely used for quantifying pulmonary microstructural morphometry. However, the technique requires long acquisition times, making it hard to apply in patients with severe pulmonary diseases, who cannot sustain long breath holds. PURPOSE To develop and evaluate the technique of variable-sampling-ratio compressed sensing (VCS) patterns for accelerating HP 129 Xe multiple b-values DW-MRI in humans. METHODS Optimal variable sampling ratios and corresponding k-space undersampling patterns for each b-value were obtained by retrospective simulations based on the fully sampled (FS) DW-MRI dataset acquired from six young healthy volunteers. Then, the FS datasets were retrospectively undersampled using both VCS patterns and conventional compressed sensing (CS) pattern with a similar average acceleration factor. The quality of reconstructed images with retrospective VCS (rVCS) and CS (rCS) datasets were quantified using mean absolute error (MAE) and structural similarity (SSIM). Pulmonary morphometric parameters were also evaluated between rVCS and FS datasets. In addition, prospective VCS multiple b-values 129 Xe DW-MRI datasets were acquired from 14 cigarette smokers and 13 age-matched healthy volunteers. The differences of lung morphological parameters obtained with the proposed method were compared between the groups using independent samples t-test. Pearson correlation coefficient was also utilized for evaluating the correlation of the pulmonary physiological parameters obtained with VCS DW-MRI and pulmonary function tests. RESULTS Lower MAE and higher SSIM values were found in the reconstructed images with rVCS measurement when compared to those using conventional rCS measurement. The details and quality of the images obtained with rVCS and FS measurements were found to be comparable. The mean values of the morphological parameters derived from rVCS and FS datasets showed no significant differences (p > 0.05), and the mean differences of measured acinar duct radius, mean linear intercept, surface-to-volume ratio, and apparent diffusion coefficient with cylinder model were -0.87%, -2.42%, 2.04%, and -0.50%, respectively. By using the VCS technique, significant differences were delineated between the pulmonary morphometric parameters of healthy volunteers and cigarette smokers (p < 0.001), while the acquisition time was reduced by four times. CONCLUSION A fourfold reduction in acquisition time was achieved using the proposed VCS method while preserving good image quality. Our preliminary results demonstrated that the proposed method can be used for evaluating pulmonary injuries caused by cigarette smoking and may prove to be helpful in diagnosing lung diseases in clinical practice.
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Affiliation(s)
- Qian Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Haidong Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qiuchen Rao
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Ming Zhang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiuchao Zhao
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Luyang Shen
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Yuan Fang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Hongchuang Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoling Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Sa Xiao
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Lei Shi
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yeqing Han
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Chaohui Ye
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xin Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China.,University of Chinese Academy of Sciences, Beijing, China
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