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Subspace-constrained approaches to low-rank fMRI acceleration. Neuroimage 2021; 238:118235. [PMID: 34091032 PMCID: PMC7611820 DOI: 10.1016/j.neuroimage.2021.118235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 12/02/2022] Open
Abstract
Acceleration methods in fMRI aim to reconstruct high fidelity images from under-sampled k-space, allowing fMRI datasets to achieve higher temporal resolution, reduced physiological noise aliasing, and increased statistical degrees of freedom. While low levels of acceleration are typically part of standard fMRI protocols through parallel imaging, there exists the potential for approaches that allow much greater acceleration. One such existing approach is k-t FASTER, which exploits the inherent low-rank nature of fMRI. In this paper, we present a reformulated version of k-t FASTER which includes additional L2 constraints within a low-rank framework. We evaluated the effect of three different constraints against existing low-rank approaches to fMRI reconstruction: Tikhonov constraints, low-resolution priors, and temporal subspace smoothness. The different approaches are separately tested for robustness to under-sampling and thermal noise levels, in both retrospectively and prospectively-undersampled finger-tapping task fMRI data. Reconstruction quality is evaluated by accurate reconstruction of low-rank subspaces and activation maps. The use of L2 constraints was found to achieve consistently improved results, producing high fidelity reconstructions of statistical parameter maps at higher acceleration factors and lower SNR values than existing methods, but at a cost of longer computation time. In particular, the Tikhonov constraint proved very robust across all tested datasets, and the temporal subspace smoothness constraint provided the best reconstruction scores in the prospectively-undersampled dataset. These results demonstrate that regularized low-rank reconstruction of fMRI data can recover functional information at high acceleration factors without the use of any model-based spatial constraints.
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Deep residual network for highly accelerated fMRI reconstruction using variable density spiral trajectory. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.02.070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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3
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Chiew M, Miller KL. Improved statistical efficiency of simultaneous multi-slice fMRI by reconstruction with spatially adaptive temporal smoothing. Neuroimage 2019; 203:116165. [PMID: 31494247 PMCID: PMC6854456 DOI: 10.1016/j.neuroimage.2019.116165] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 08/29/2019] [Accepted: 09/04/2019] [Indexed: 11/27/2022] Open
Abstract
We introduce an approach to reconstruction of simultaneous multi-slice (SMS)-fMRI data that improves statistical efficiency. The method incorporates regularization to adjust temporal smoothness in a spatially varying, encoding-dependent manner, reducing the g-factor noise amplification per temporal degree of freedom. This results in a net improvement in tSNR and GLM efficiency, where the efficiency gain can be derived analytically as a function of the encoding and reconstruction parameters. Residual slice leakage and aliasing is limited when fMRI signal energy is dominated by low frequencies. Analytical predictions, simulated and experimental results demonstrate a marked improvement in statistical efficiency in the temporally regularized reconstructions compared to conventional slice-GRAPPA reconstructions, particularly in central brain regions. Furthermore, experimental results confirm that residual slice leakage and aliasing errors are not noticeably increased compared to slice-GRAPPA reconstruction. This approach to temporally regularized image reconstruction in SMS-fMRI improves statistical power, and allows for explicit choice of reconstruction parameters by directly assessing their impact on noise variance per degree of freedom.
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Affiliation(s)
- Mark Chiew
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
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Zhang L, Chen X, Lin J, Ding X, Bao L, Cai C, Li J, Chen Z, Cai S. Fast quantitative susceptibility reconstruction via total field inversion with improved weighted L 0 norm approximation. NMR IN BIOMEDICINE 2019; 32:e4067. [PMID: 30811722 DOI: 10.1002/nbm.4067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 12/26/2018] [Accepted: 01/03/2019] [Indexed: 06/09/2023]
Abstract
Quantitative susceptibility mapping (QSM) is a meaningful MRI technique owing to its unique relation to actual physical tissue magnetic properties. The reconstruction of QSM is usually decomposed into three sub-problems, which are solved independently. However, this decomposition does not conform to the causes of the problems, and may cause discontinuity of parameters and error accumulation. In this paper, a fast reconstruction method named fast TFI based on total field inversion was proposed. It can accelerate the total field inversion by using a specially selected preconditioner and advanced solution of the weighted L0 regularization. Due to the employment of an effective model, the proposed method can efficiently reconstruct the QSM of brains with lesions, where other methods may encounter problems. Experimental results from simulation and in vivo data verified that the new method has better reconstruction accuracy, faster convergence ability and excellent robustness, which may promote clinical application of QSM.
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Affiliation(s)
- Li Zhang
- Department of Electronic Science, Xiamen University, Xiamen, China
| | - Xi Chen
- Department of Communication Engineering, Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Magnetic Resonance Center, Zhongshan Hospital, Medical College of Xiamen University, Xiamen, China
| | - Xinghao Ding
- Department of Communication Engineering, Xiamen University, Xiamen, China
| | - Lijun Bao
- Department of Electronic Science, Xiamen University, Xiamen, China
| | - Congbo Cai
- Department of Electronic Science, Xiamen University, Xiamen, China
- Department of Communication Engineering, Xiamen University, Xiamen, China
| | - Jing Li
- Xingaoyi Medical Equipment Co., Ltd., Yuyao, Zhejiang, China
| | - Zhong Chen
- Department of Electronic Science, Xiamen University, Xiamen, China
| | - Shuhui Cai
- Department of Electronic Science, Xiamen University, Xiamen, China
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Mehranian A, Belzunce MA, McGinnity CJ, Bustin A, Prieto C, Hammers A, Reader AJ. Multi-modal synergistic PET and MR reconstruction using mutually weighted quadratic priors. Magn Reson Med 2019; 81:2120-2134. [PMID: 30325053 PMCID: PMC6563465 DOI: 10.1002/mrm.27521] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 08/15/2018] [Accepted: 08/15/2018] [Indexed: 11/06/2022]
Abstract
PURPOSE To propose a framework for synergistic reconstruction of PET-MR and multi-contrast MR data to improve the image quality obtained from noisy PET data and from undersampled MR data. THEORY AND METHODS Weighted quadratic priors were devised to preserve common boundaries between PET-MR images while reducing noise, PET Gibbs ringing, and MR undersampling artifacts. These priors are iteratively reweighted using normalized multi-modal Gaussian similarity kernels. Synergistic PET-MR reconstructions were built on the PET maximum a posteriori expectation maximization algorithm and the MR regularized sensitivity encoding method. The proposed approach was compared to conventional methods, total variation, and prior-image weighted quadratic regularization methods. Comparisons were performed on a simulated [18 F]fluorodeoxyglucose-PET and T1 /T2 -weighted MR brain phantom, 2 in vivo T1 /T2 -weighted MR brain datasets, and an in vivo [18 F]fluorodeoxyglucose-PET and fluid-attenuated inversion recovery/T1 -weighted MR brain dataset. RESULTS Simulations showed that synergistic reconstructions achieve the lowest quantification errors for all image modalities compared to conventional, total variation, and weighted quadratic methods. Whereas total variation regularization preserved modality-unique features, this method failed to recover PET details and was not able to reduce MR artifacts compared to our proposed method. For in vivo MR data, our method maintained similar image quality for 3× and 14× accelerated data. Reconstruction of the PET-MR dataset also demonstrated improved performance of our method compared to the conventional independent methods in terms of reduced Gibbs and undersampling artifacts. CONCLUSION The proposed methodology offers a robust multi-modal synergistic image reconstruction framework that can be readily built on existing established algorithms.
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Affiliation(s)
- Abolfazl Mehranian
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing's College LondonUnited Kingdom
| | - Martin A. Belzunce
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing's College LondonUnited Kingdom
| | - Colm J. McGinnity
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' HospitalLondonUnited Kingdom
| | - Aurelien Bustin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing's College LondonUnited Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing's College LondonUnited Kingdom
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' HospitalLondonUnited Kingdom
| | - Andrew J. Reader
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging SciencesKing's College LondonUnited Kingdom
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Simultaneous BOLD detection and incomplete fMRI data reconstruction. Med Biol Eng Comput 2018; 56:599-610. [DOI: 10.1007/s11517-017-1707-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 08/03/2017] [Indexed: 10/19/2022]
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Abascal JFPJ, Desco M, Parra-Robles J. Incorporation of Prior Knowledge of Signal Behavior Into the Reconstruction to Accelerate the Acquisition of Diffusion MRI Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:547-556. [PMID: 29408783 DOI: 10.1109/tmi.2017.2765281] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Diffusion MRI data are generally acquired using hyperpolarized gases during patient breath-hold, which yields a compromise between achievable image resolution, lung coverage, and number of -values. In this paper, we propose a novel method that accelerates the acquisition of diffusion MRI data by undersampling in both the spatial and -value dimensions and incorporating knowledge about signal decay into the reconstruction (SIDER). SIDER is compared with total variation (TV) reconstruction by assessing its effect on both the recovery of ventilation images and the estimated mean alveolar dimensions (MADs). Both methods are assessed by retrospectively undersampling diffusion data sets ( =8) of healthy volunteers and patients with Chronic Obstructive Pulmonary Disease (COPD) for acceleration factors between x2 and x10. TV led to large errors and artifacts for acceleration factors equal to or larger than x5. SIDER improved TV, with a lower solution error and MAD histograms closer to those obtained from fully sampled data for acceleration factors up to x10. SIDER preserved image quality at all acceleration factors, although images were slightly smoothed and some details were lost at x10. In conclusion, we developed and validated a novel compressed sensing method for lung MRI imaging and achieved high acceleration factors, which can be used to increase the amount of data acquired during breath-hold. This methodology is expected to improve the accuracy of estimated lung microstructure dimensions and provide more options in the study of lung diseases with MRI.
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Li X, Ma X, Li L, Zhang Z, Zhang X, Tong Y, Wang L, Sen Song, Guo H. Dual-TRACER: High resolution fMRI with constrained evolution reconstruction. Neuroimage 2018; 164:172-182. [DOI: 10.1016/j.neuroimage.2017.02.087] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 02/16/2017] [Accepted: 02/27/2017] [Indexed: 11/25/2022] Open
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Weizman L, Miller KL, Eldar YC, Chiew M. PEAR: PEriodic And fixed Rank separation for fast fMRI. Med Phys 2017; 44:6166-6182. [PMID: 28945924 PMCID: PMC5836861 DOI: 10.1002/mp.12599] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 08/16/2017] [Accepted: 09/12/2017] [Indexed: 12/30/2022] Open
Abstract
PURPOSE In functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial-temporal resolution trade-off and increase statistical robustness through increased degrees-of-freedom. High-quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements. METHODS The proposed approach decomposes the fMRI signal into a component which has a fixed rank and a component consisting of a sum of periodic signals which is sparse in the temporal Fourier domain. Data reconstruction is performed by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR - PEriodic And fixed Rank separation for fast fMRI. RESULTS Experimental results include purely synthetic simulation, a simulation with real timecourses and retrospective undersampling of a real fMRI dataset. Evaluation was performed both quantitatively and visually versus ground truth, comparing PEAR to two additional recent methods for fMRI reconstruction from undersampled measurements. Results demonstrate PEAR's improvement in estimating the timecourses and activation maps versus the methods compared against at acceleration ratios of R = 8,10.66 (for simulated data) and R = 6.66,10 (for real data). CONCLUSIONS This paper presents PEAR, an undersampled fMRI reconstruction approach based on decomposing the fMRI signal to periodic and fixed rank components. PEAR results in reconstruction with higher fidelity than when using a fixed-rank based model or a conventional Low-rank + Sparse algorithm. We have shown that splitting the functional information between the components leads to better modeling of fMRI, over state-of-the-art methods.
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Affiliation(s)
- Lior Weizman
- Department of Electrical EngineeringTechnion ‐ Israel Institue of TechnologyHaifaIsrael
- FMRIB CentreUniversity of OxfordOxfordUK
| | | | - Yonina C. Eldar
- Department of Electrical EngineeringTechnion ‐ Israel Institue of TechnologyHaifaIsrael
| | - Mark Chiew
- FMRIB CentreUniversity of OxfordOxfordUK
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Aggarwal P, Gupta A. Double temporal sparsity based accelerated reconstruction of compressively sensed resting-state fMRI. Comput Biol Med 2017; 91:255-266. [PMID: 29101794 DOI: 10.1016/j.compbiomed.2017.10.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/14/2017] [Accepted: 10/19/2017] [Indexed: 12/31/2022]
Abstract
A number of reconstruction methods have been proposed recently for accelerated functional Magnetic Resonance Imaging (fMRI) data collection. However, existing methods suffer with the challenge of greater artifacts at high acceleration factors. This paper addresses the issue of accelerating fMRI collection via undersampled k-space measurements combined with the proposed method based on l1-l1 norm constraints, wherein we impose first l1-norm sparsity on the voxel time series (temporal data) in the transformed domain and the second l1-norm sparsity on the successive difference of the same temporal data. Hence, we name the proposed method as Double Temporal Sparsity based Reconstruction (DTSR) method. The robustness of the proposed DTSR method has been thoroughly evaluated both at the subject level and at the group level on real fMRI data. Results are presented at various acceleration factors. Quantitative analysis in terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative analysis in terms of reproducibility of brain Resting State Networks (RSNs) demonstrate that the proposed method is accurate and robust. In addition, the proposed DTSR method preserves brain networks that are important for studying fMRI data. Compared to the existing methods, the DTSR method shows promising potential with an improvement of 10-12 dB in PSNR with acceleration factors upto 3.5 on resting state fMRI data. Simulation results on real data demonstrate that DTSR method can be used to acquire accelerated fMRI with accurate detection of RSNs.
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Affiliation(s)
- Priya Aggarwal
- Signal Processing and Bio-medical Imaging Lab, Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology (IIIT), Delhi, India.
| | - Anubha Gupta
- Signal Processing and Bio-medical Imaging Lab, Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology (IIIT), Delhi, India.
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Cai C, Chen X, Liu W, Cai S, Zeng D, Ding X. Rapid reconstruction of quantitative susceptibility mapping via improved ℓ 0 norm approximation. Comput Biol Med 2016; 79:59-67. [PMID: 27744181 DOI: 10.1016/j.compbiomed.2016.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 09/30/2016] [Accepted: 10/01/2016] [Indexed: 01/08/2023]
Abstract
Quantitative susceptibility mapping (QSM) reconstruction is a well-known ill-posed problem. Various regularization techniques have been proposed for solving this problem. In this paper, a rapid method is proposed that uses ℓ0 norm minimization in a gradient domain. Because ℓ0 minimization is an NP-hard problem, a special alternating optimization strategy is employed to simplify the reconstruction algorithm. The proposed algorithm uses only simple point-wise multiplications and thresholding operations, and significantly speeds up the calculation. Both numerical simulations and in vivo experiments demonstrate that the proposed method can reconstruct susceptibility fast and accurately. Because morphology information weighted methods have achieved considerable success in QSM, we performed a quantitative comparison with some typical weighted methods, such as MEDI (morphology enabled dipole inversion), iLSQR (improved least squares algorithm), and wℓ1 (weighted ℓ1 norm minimization). The reconstructed results show that the proposed method can provide accurate results with a satisfactory speed.
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Affiliation(s)
- Congbo Cai
- Department of Communications Engineering, Xiamen University, Fujian, China.
| | - Xi Chen
- Department of Communications Engineering, Xiamen University, Fujian, China
| | - Weijun Liu
- Department of Communications Engineering, Xiamen University, Fujian, China
| | - Shuhui Cai
- Department of Electronic Science, Xiamen University, Fujian, China
| | - Delu Zeng
- Department of Communications Engineering, Xiamen University, Fujian, China
| | - Xinghao Ding
- Department of Communications Engineering, Xiamen University, Fujian, China
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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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Chavarrías C, Abascal JFPJ, Montesinos P, Desco M. Erratum: “Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior-based algorithm (PICCS)” [Med. Phys. 42
, 3814-3821 (2015)]. Med Phys 2015; 42:4997. [DOI: 10.1118/1.4926781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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