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Ahmed AH, Zou Q, Nagpal P, Jacob M. Dynamic Imaging Using Deep Bi-Linear Unsupervised Representation (DEBLUR). IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2693-2703. [PMID: 35436187 PMCID: PMC9744437 DOI: 10.1109/tmi.2022.3168559] [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] [Indexed: 06/14/2023]
Abstract
Bilinear models such as low-rank and dictionary methods, which decompose dynamic data to spatial and temporal factor matrices are powerful and memory-efficient tools for the recovery of dynamic MRI data. Current bilinear methods rely on sparsity and energy compaction priors on the factor matrices to regularize the recovery. Motivated by deep image prior, we introduce a novel bilinear model, whose factor matrices are generated using convolutional neural networks (CNNs). The CNN parameters, and equivalently the factors, are learned from the undersampled data of the specific subject. Unlike current unrolled deep learning methods that require the storage of all the time frames in the dataset, the proposed approach only requires the storage of the factors or compressed representation; this approach allows the direct use of this scheme to large-scale dynamic applications, including free breathing cardiac MRI considered in this work. To reduce the run time and to improve performance, we initialize the CNN parameters using existing factor methods. We use sparsity regularization of the network parameters to minimize the overfitting of the network to measurement noise. Our experiments on free-breathing and ungated cardiac cine data acquired using a navigated golden-angle gradient-echo radial sequence show the ability of our method to provide reduced spatial blurring as compared to classical bilinear methods as well as a recent unsupervised deep-learning approach.
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2
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Klauser A, Strasser B, Thapa B, Lazeyras F, Andronesi O. Achieving high-resolution 1H-MRSI of the human brain with compressed-sensing and low-rank reconstruction at 7 Tesla. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 331:107048. [PMID: 34438355 PMCID: PMC8717865 DOI: 10.1016/j.jmr.2021.107048] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/29/2021] [Accepted: 08/08/2021] [Indexed: 06/02/2023]
Abstract
Low sensitivity MR techniques such as magnetic resonance spectroscopic imaging (MRSI) greatly benefit from the gain in signal-to-noise provided by ultra-high field MR. High-resolution and whole-slab brain MRSI remains however very challenging due to lengthy acquisition, low signal, lipid contamination and field inhomogeneity. In this study, we propose an acquisition-reconstruction scheme that combines 1H free-induction-decay (FID)-MRSI sequence, short TR acquisition, compressed sensing acceleration and low-rank modeling with total-generalized-variation constraint to achieve metabolite imaging in two and three dimensions at 7 Tesla. The resulting images and volumes reveal highly detailed distributions that are specific to each metabolite and follow the underlying brain anatomy. The MRSI method was validated in a high-resolution phantom containing fine metabolite structures, and in five healthy volunteers. This new application of compressed sensing acceleration paves the way for high-resolution MRSI in clinical setting with acquisition times of 5 min for 2D MRSI at 2.5 mm and of 20 min for 3D MRSI at 3.3 mm isotropic.
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Affiliation(s)
- Antoine Klauser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Center for Biomedical Imaging (CIBM), Geneva, Switzerland.
| | - Bernhard Strasser
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Bijaya Thapa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Francois Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Ovidiu Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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3
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Adany P, Choi IY, Lee P. Method for fast lipid reconstruction and removal processing in 1 H MRSI of the brain. Magn Reson Med 2021; 86:2930-2944. [PMID: 34337788 DOI: 10.1002/mrm.28949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop a new rapid spatial filtering method for lipid removal, fast lipid reconstruction and removal processing (FLIP), which selectively isolates and removes interfering lipid signals from outside the brain in a full-FOV 2D MRSI and whole-brain 3D echo planar spectroscopic imaging (EPSI). THEORY AND METHODS FLIP uses regularized least-squares regression based on spatial prior information from MRI to selectively remove lipid signals originating from the scalp and measure the brain metabolite signals with minimum cross contamination. FLIP is a noniterative approach, thus allowing a rapid processing speed, and uses only spatial information without any spectral priors. The performance of FLIP was compared with the Papoulis-Gerchberg algorithm (PGA), Hankel singular value decomposition (HSVD), and fast image reconstruction with L2 regularization (L2). RESULTS FLIP in both 2D and 3D MRSI resulted in consistent metabolite quantification in a greater number of voxels with less concentration variation than other algorithms, demonstrating effective and robust lipid-removal performance. The percentage of voxels that met quality criteria with FLIP, PGA, HSVD, and L2 processing were 90%, 57%, 29%, and 42% in 2D MRSI, and 80%, 75%, 76%, and 74% in 3D EPSI, respectively. The quantification results of full-FOV MRSI using FLIP were comparable to those of volume-localized MRSI, while allowing significantly increased spatial coverage. FLIP performed the fastest in 2D MRSI. CONCLUSION FLIP is a new lipid-removal algorithm that promises fast and effective lipid removal with improved volume coverage in MRSI.
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Affiliation(s)
- Peter Adany
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - In-Young Choi
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Neurology, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Molecular & Integrative Physiology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Phil Lee
- Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas, USA.,Department of Molecular & Integrative Physiology, University of Kansas Medical Center, Kansas City, Kansas, USA
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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: 14.7] [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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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 DOI: 10.1002/mrm.28728] [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: 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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Lam F, Li Y, Peng X. Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:545-555. [PMID: 31352337 DOI: 10.1109/tmi.2019.2930586] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals by learning a nonlinear low-dimensional representation of the general MR spectra. Specifically, we trained a deep neural network to capture the low-dimensional manifold, where the high-dimensional spectroscopic signals reside. A regularization formulation is proposed to effectively integrate the learned model and physics-based data acquisition model for MRSI reconstruction with the capability to incorporate additional spatiospectral constraints. An efficient numerical algorithm was developed to solve the associated optimization problem involving back-propagating the trained network. Simulation and experimental results were obtained to demonstrate the representation power of the learned model and the ability of the proposed formulation in producing SNR-enhancing reconstruction from the practical MRSI data.
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Lam F, Li Y, Guo R, Clifford B, Liang ZP. Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces. Magn Reson Med 2020; 83:377-390. [PMID: 31483526 PMCID: PMC6824949 DOI: 10.1002/mrm.27980] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 08/02/2019] [Accepted: 08/12/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE To develop a subspace learning method for the recently proposed subspace-based MRSI approach known as SPICE, and achieve ultrafast 1 H-MRSI of the brain. THEORY AND METHODS A novel strategy is formulated to learn a low-dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning "empirical" distributions of molecule-specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics-based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition-specific subspace for spatiospectral encoding and processing. High-resolution MRSI acquisitions combining ultrashort-TE/short-TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method. RESULTS The accuracy of the learned subspace and the capability of the proposed method in producing high-resolution 3D 1 H metabolite maps and high-quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm3 in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing ( B 0 map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters. CONCLUSIONS The proposed method enables ultrafast 1 H-MRSI of the brain using a learned subspace, eliminating the need of acquiring subject-dependent navigator data (known as D 1 ) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of high-resolution MRSI.
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Affiliation(s)
- Fan Lam
- Department of Bioengineering, University of Illinois at Urbana-Champaign
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
| | - Bryan Clifford
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
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8
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Lin L, Považan M, Berrington A, Chen Z, Barker PB. Water removal in MR spectroscopic imaging with L2 regularization. Magn Reson Med 2019; 82:1278-1287. [PMID: 31148254 DOI: 10.1002/mrm.27824] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 04/03/2019] [Accepted: 05/01/2019] [Indexed: 12/31/2022]
Abstract
PURPOSE An L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T. METHODS Water signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals. RESULTS The L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect. CONCLUSIONS With usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.
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Affiliation(s)
- Liangjie Lin
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Department of Electronic Science, Xiamen University, Xiamen, China
| | - Michal Považan
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Adam Berrington
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Zhong Chen
- Department of Electronic Science, Xiamen University, Xiamen, China
| | - Peter B Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
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9
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Klauser A, Courvoisier S, Kasten J, Kocher M, Guerquin-Kern M, Van De Ville D, Lazeyras F. Fast high-resolution brain metabolite mapping on a clinical 3T MRI by accelerated 1 H-FID-MRSI and low-rank constrained reconstruction. Magn Reson Med 2018; 81:2841-2857. [PMID: 30565314 DOI: 10.1002/mrm.27623] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 10/18/2018] [Accepted: 11/12/2018] [Indexed: 12/18/2022]
Abstract
PURPOSE Epitomizing the advantages of ultra short echo time and no chemical shift displacement error, high-resolution-free induction decay magnetic resonance spectroscopic imaging (FID-MRSI) sequences have proven to be highly effective in providing unbiased characterizations of metabolite distributions. However, its merits are often overshadowed in high-resolution settings by reduced signal-to-noise ratios resulting from the smaller voxel volumes procured by extensive phase encoding and the related acquisition times. METHODS To address these limitations, we here propose an acquisition and reconstruction scheme that offers both implicit dataset denoising and acquisition acceleration. Specifically, a slice selective high-resolution FID-MRSI sequence was implemented. Spectroscopic datasets were processed to remove fat contamination, and then reconstructed using a total generalized variation (TGV) regularized low-rank model. We further measured reconstruction performance for random undersampled data to assess feasibility of a compressed-sensing SENSE acceleration scheme. Performance of the lipid suppression was assessed using an ad hoc phantom, while that of the low-rank TGV reconstruction model was benchmarked using simulated MRSI data. To assess real-world performance, 2D FID-MRSI acquisitions of the brain in healthy volunteers were reconstructed using the proposed framework. RESULTS Results from the phantom and simulated data demonstrate that skull lipid contamination is effectively removed and that data reconstruction quality is improved with the low-rank TGV model. Also, we demonstrated that the presented acquisition and reconstruction methods are compatible with a compressed-sensing SENSE acceleration scheme. CONCLUSIONS An original reconstruction pipeline for 2D 1 H-FID-MRSI datasets was presented that places high-resolution metabolite mapping on 3T MR scanners within clinically feasible limits.
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Affiliation(s)
- Antoine Klauser
- Department of Radiology and Medical, Informatics, University of Geneva, Geneva, Switzerland
| | - Sebastien Courvoisier
- Department of Radiology and Medical, Informatics, University of Geneva, Geneva, Switzerland
| | - Jeffrey Kasten
- Department of Radiology and Medical, Informatics, University of Geneva, Geneva, Switzerland
| | - Michel Kocher
- Department of Radiology and Medical, Informatics, University of Geneva, Geneva, Switzerland
| | | | - Dimitri Van De Ville
- Department of Radiology and Medical, Informatics, University of Geneva, Geneva, Switzerland.,Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Francois Lazeyras
- Department of Radiology and Medical, Informatics, University of Geneva, Geneva, Switzerland
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Nassirpour S, Chang P, Avdievitch N, Henning A. Compressed sensing for high-resolution nonlipid suppressed 1 H FID MRSI of the human brain at 9.4T. Magn Reson Med 2018; 80:2311-2325. [PMID: 29707804 DOI: 10.1002/mrm.27225] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 03/06/2018] [Accepted: 03/26/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE 1 H FID MRSI sequence at 9.4T. METHODS X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed 1 H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared. RESULTS Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved. CONCLUSION By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min.
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Affiliation(s)
- Sahar Nassirpour
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls University of Tuebingen, Germany
| | - Paul Chang
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls University of Tuebingen, Germany
| | - Nikolai Avdievitch
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,Institute of Physics, Ernst-Moritz-Arndt University Greifswald, Greifswald, Germany
| | - Anke Henning
- Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.,Institute of Physics, Ernst-Moritz-Arndt University Greifswald, Greifswald, Germany
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Bhattacharya I, Jacob M. DENOISING AND DEINTERLEAVING OF EPSI DATA USING STRUCTURED LOW-RANK MATRIX RECOVERY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:679-682. [PMID: 33633819 PMCID: PMC7902243 DOI: 10.1109/isbi.2018.8363665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Echo-planar spectroscopic imaging (EPSI) sequence with spectrally interleaving is often used to rapidly collect metabolic MRI data. The main problem in using it on high field scanners is the presence of spurious peaks resulting from phase distortions between interleaves as well as the low signal to noise ratio. We introduce a novel structured low-rank framework for the simultaneous denoising and deinterleaving of spectrally interleaved EPSI data. The proposed algorithm exploits annihilation relations resulting from the linear predicability of exponential signals as well as due to uncorrected phase relations between interleaves. The algorithm is formulated as a structured nuclear norm minimization of a block Hankel matrix, derived from the interleaves. Experiments using hyperpolarized 13 C mouse kidney EPSI data demonstrate the ability of the algorithm to remove ghost peaks from EPSI data collected using bipolar readout gradients.
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Affiliation(s)
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, IA, USA
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12
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Han PK, Ma C, Deng K, Hu S, Jee KW, Ying K, Chen YL, El Fakhri G. A minimum-phase Shinnar-Le Roux spectral-spatial excitation RF pulse for simultaneous water and lipid suppression in 1H-MRSI of body extremities. Magn Reson Imaging 2018; 45:18-25. [PMID: 28917812 PMCID: PMC5709164 DOI: 10.1016/j.mri.2017.09.008] [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: 06/15/2017] [Revised: 09/11/2017] [Accepted: 09/12/2017] [Indexed: 02/05/2023]
Abstract
PURPOSE To develop a spectral-spatial (SPSP) excitation RF pulse for simultaneous water and lipid suppression in proton (1H) magnetic resonance spectroscopic imaging (MRSI) of body extremities. METHODS An SPSP excitation pulse is designed to excite Creatine (Cr) and Choline (Cho) metabolite signals while suppressing the overwhelming water and lipid signals. The SPSP pulse is designed using a recently proposed multidimensional Shinnar-Le Roux (SLR) RF pulse design method. A minimum-phase spectral selectivity profile is used to minimize signal loss from T2⁎ decay. RESULTS The performance of the SPSP pulse is evaluated via Bloch equation simulations and phantom experiments. The feasibility of the proposed method is demonstrated using three-dimensional, short repetition-time, free induction decay-based 1H-MRSI in the thigh muscle at 3T. CONCLUSION The proposed SPSP excitation pulse is useful for simultaneous water and lipid suppression. The proposed method enables new applications of high-resolution 1H-MRSI in body extremities.
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Affiliation(s)
- Paul Kyu Han
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Chao Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Kexin Deng
- Biomedical Engineering, Tsinghua University, Beijing, People's Republic of China
| | - Shuang Hu
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Department of Nuclear Medicine, West China Hospital, Sichuan University, Sichuan, People's Republic of China
| | - Kyung-Wook Jee
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Kui Ying
- Engineering Physics, Tsinghua University, Beijing, People's Republic of China
| | - Yen-Lin Chen
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
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