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Si D, Kong X, Guo R, Cheng L, Ning Z, Chen Z, Chen S, Herzka DA, Ding H. Single breath-hold three-dimensional whole-heart T 2 mapping with low-rank plus sparse reconstruction. NMR IN BIOMEDICINE 2023:e4924. [PMID: 36912448 DOI: 10.1002/nbm.4924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 02/14/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
The purpose of the current study was to develop and evaluate a three-dimensional single Breath-hOLd cardiac T2 mapping sequence (3D BOLT) with low-rank plus sparse (L + S) reconstruction for rapid whole-heart T2 measurement. 3D BOLT collects three highly accelerated electrocardiogram-triggered volumes with whole-heart coverage, all within a single 12-heartbeat breath-hold. Saturation pulses are performed every heartbeat to prepare longitudinal magnetization before T2 preparation (T2 -prep) or readout, and the echo time of T2 -prep is varied per volume for variable T2 weighting. Accelerated volumes are reconstructed jointly by an L + S algorithm. 3D BOLT was optimized and validated against gradient spin echo (GraSE) and a previously published approach (three-dimensional free-breathing cardiac T2 mapping [3DFBT2]) in both phantoms and human subjects (11 healthy subjects and 10 patients). The repeatability of 3D BOLT was validated on healthy subjects. Retrospective experiments indicated that 3D BOLT with 4.2-fold acceleration achieved T2 measurements comparable with those obtained with fully sampled data. T2 measured in phantoms using 3D BOLT demonstrated good accuracy and precision compared with the reference (R2 > 0.99). All in vivo imaging was successful and the average left ventricle T2 s measured by GraSE, 3DFBT2, and 3D BOLT were comparable and consistent for all healthy subjects (47.0 ± 2.3 vs. 47.7 ± 2.7 vs. 48.4 ± 1.8 ms) and patients (50.8 ± 3.0 vs. 48.6 ± 3.9 vs. 49.1 ± 3.7 ms), respectively. Myocardial T2 measured by 3D BOLT had excellent agreement with 3DFBT2 and there was no significant difference in mean, standard deviation, and coefficient of variation. 3D BOLT showed excellent repeatability (intraclass correlation coefficient: 0.938). The proposed 3D BOLT achieved whole-heart T2 mapping in a single breath-hold with good accuracy, precision, and repeatability on T2 measurements.
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Affiliation(s)
- Dongyue Si
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Xiangchuang Kong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Rui Guo
- School of medical technology, Beijing Institute of Technology, Beijing, China
| | - Lan Cheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Zihan Ning
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Zhensen Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shuo Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Daniel A Herzka
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Haiyan Ding
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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Liu S, Li H, Liu Y, Cheng G, Yang G, Wang H, Zheng H, Liang D, Zhu Y. Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8c81] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/24/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Introduction. To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously. Methods. The proposed framework consists of a reconstruction module and a generative module. The reconstruction module reconstructs MR images from the acquired few undersampled k-space data with the help of a data prior. The generative module then synthesizes the remaining multi-contrast images from the reconstructed images, where the exponential model is implicitly incorporated into the image generation through the supervision of fully sampled labels. The RG-Net was trained and tested on the T1ρ
mapping data from 8 volunteers at net acceleration rates of 17, respectively. Regional T1ρ
analysis for cartilage and the brain was performed to assess the performance of RG-Net. Results. RG-Net yields a high-quality T1ρ
map at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in T1ρ
value analysis. Conclusion. The proposed RG-Net can achieve a high acceleration rate while maintaining good reconstruction quality by undersampling k-space and reducing the contrast number simultaneously for fast MR parametric mapping. The generative module of our framework can also be used as an insertable module in other fast MR parametric mapping methods.
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Zhou Y, Wang H, Liu Y, Liang D, Ying L. Accelerating MR Parameter Mapping Using Nonlinear Compressive Manifold Learning and Regularized Pre-Imaging. IEEE Trans Biomed Eng 2022; 69:2996-3007. [PMID: 35290182 DOI: 10.1109/tbme.2022.3158904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this study, we presented a novel method to reconstruct the MR parametric maps from highly undersampled k-space data. Specifically, we utilized a nonlinear model to sparsely represent the unknown MR parameter-weighted images in high-dimensional feature space. Each image at a specific time point is assumed to belong to a low-dimensional manifold which is learned from training images created based on the parametric model. The final reconstruction is carried out by venturing the sparse representation of the images in the feature space back to the input space, using the pre-imaging technique. Particularly, among an infinite number of solutions that satisfy the data consistency, the one that is closest to the manifold is selected as the desired solution. The underlying optimization problem is solved using kernel trick, sparse coding, and split Bregman iteration algorithm. In addition, both spatial and temporal regularizations were utilized to further improve the reconstruction quality. The proposed method was validated on both phantom and in vivo human brain T2 mapping data. Results showed the proposed method was superior to the conventional linear model-based reconstruction methods, in terms of artifact removal and quantitative estimate accuracy. The proposed method could be potentially beneficial for quantitative MR applications.
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Zibetti MVW, Herman GT, Regatte RR. Fast data-driven learning of parallel MRI sampling patterns for large scale problems. Sci Rep 2021; 11:19312. [PMID: 34588478 PMCID: PMC8481566 DOI: 10.1038/s41598-021-97995-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/01/2021] [Indexed: 12/14/2022] Open
Abstract
In this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images ([Formula: see text]-weighted images and, respectively, [Formula: see text]-weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA.
| | - Gabor T Herman
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
- Department of Computer Science, The Graduate Center, City University of New York, New York, NY, 10016, USA
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, 10016, USA
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Menon RG, Raghavan P, Regatte RR. Pilot study quantifying muscle glycosaminoglycan using bi-exponential T 1ρ mapping in patients with muscle stiffness after stroke. Sci Rep 2021; 11:13951. [PMID: 34230600 PMCID: PMC8260636 DOI: 10.1038/s41598-021-93304-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/16/2021] [Indexed: 01/14/2023] Open
Abstract
Post stroke muscle stiffness is a common problem, which left untreated can lead to disabling muscle contractures. The purpose of this pilot study was to evaluate the feasibility of bi-exponential T1ρ mapping in patients with arm muscle stiffness after stroke and its ability to measure treatment related changes in muscle glycosaminoglycans (GAGs). Five patients with muscle stiffness after stroke and 5 healthy controls were recruited for imaging of the upper arm with 3D-T1ρ mapping. Patients were scanned before and after treatment with hyaluronidase injections, whereas the controls were scanned once. Wilcoxon Mann-Whitney tests compared patients vs. controls and patients pre-treatment vs. post-treatment. With bi-exponential modeling, the long component, T1ρl was significantly longer in the patients (biceps P = 0.01; triceps P = 0.004) compared to controls. There was also a significant difference in the signal fractions of the long and short components (biceps P = 0.03, triceps P = 0.04). The results suggest that muscle stiffness is characterized by increased muscle free water and GAG content. Post-treatment, the T1ρ parameters shifted toward control values. This pilot study demonstrates the application of bi-exponential T1ρ mapping as a marker for GAG content in muscle and as a potential treatment monitoring tool for patients with muscle stiffness after stroke.
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Affiliation(s)
- Rajiv G Menon
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA.
| | - Preeti Raghavan
- Deparments of Physical Medicine and Rehabilitation and Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA
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Zhu Y, Liu Y, Ying L, Qiu Z, Liu Q, Jia S, Wang H, Peng X, Liu X, Zheng H, Liang D. A 4-minute solution for submillimeter whole-brain T 1ρ quantification. Magn Reson Med 2021; 85:3299-3307. [PMID: 33421224 DOI: 10.1002/mrm.28656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop a robust, accurate, and accelerated T1ρ quantification solution for submillimeter in vivo whole-brain imaging. METHODS A multislice T1ρ mapping solution (MS-T1ρ ) was developed based on a two-acquisition scheme using turbo spin echo with RF cycling to allow for whole-brain coverage with 0.8-mm in-plane resolution. A compressed sensing-based fast imaging method, SCOPE, was used to accelerate the MS-T1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS-T1ρ by comparing the T1ρ value obtained using MS-T1ρ with the reference value obtained using the standard single-slice T1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS-T1ρ . RESULTS In the phantom study, the T1ρ values obtained with MS-T1ρ were in good agreement with the reference T1ρ values (R2 = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T1ρ values between MS-T1ρ acquisitions and fully sampled acquisitions for all brain tissues (p-value > .05). In the intraclass correlation coefficient and Bland-Altman analyses, the accelerated T1ρ measurements show moderate to good agreement to the fully sampled reference values. CONCLUSION The proposed MS-T1ρ solution allows for high-resolution whole-brain T1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases.
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Affiliation(s)
- Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Yuanyuan Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Leslie Ying
- Department of Biomedical Engineering and Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, 14260, USA
| | - Zhilang Qiu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, Jiangxi, 330031, China
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
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Menon RG, Zibetti MVW, Jain R, Ge Y, Regatte RR. Performance Comparison of Compressed Sensing Algorithms for Accelerating T 1ρ Mapping of Human Brain. J Magn Reson Imaging 2020; 53:1130-1139. [PMID: 33190362 DOI: 10.1002/jmri.27421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 10/15/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND 3D-T1ρ mapping is useful to quantify various neurologic disorders, but data are currently time-consuming to acquire. PURPOSE To compare the performance of five compressed sensing (CS) algorithms-spatiotemporal finite differences (STFD), exponential dictionary (EXP), 3D-wavelet transform (WAV), low-rank (LOW) and low-rank plus sparse model with spatial finite differences (L + S SFD)-for 3D-T1ρ mapping of the human brain with acceleration factors (AFs) of 2, 5, and 10. STUDY TYPE Retrospective. SUBJECTS Eight healthy volunteers underwent T1ρ imaging of the whole brain. FIELD STRENGTH/SEQUENCE The sequence was fully sampled 3D Cartesian ultrafast gradient echo sequence with a customized T1ρ preparation module on a clinical 3T scanner. ASSESSMENT The fully sampled data was undersampled by factors of 2, 5, and 10 and reconstructed with the five CS algorithms. Image reconstruction quality was evaluated and compared to the SENSE reconstruction of the fully sampled data (reference) and T1ρ estimation errors were assessed as a function of AF. STATISTICAL TESTS Normalized root mean squared errors (nRMSE) and median normalized absolute deviation (MNAD) errors were calculated to compare image reconstruction errors and T1ρ estimation errors, respectively. Linear regression plots, Bland-Altman plots, and Pearson correlation coefficients (CC) are shown. RESULTS For image reconstruction quality, at AF = 2, EXP transforms had the lowest mRMSE (1.56%). At higher AF values, STFD performed better, with the smallest errors (3.16% at AF = 5, 4.32% at AF = 10). For whole-brain quantitative T1ρ mapping, at AF = 2, EXP performed best (MNAD error = 1.62%). At higher AF values (AF = 5, 10), the STFD technique had the least errors (2.96% at AF = 5, 4.24% at AF = 10) and the smallest variance from the reference T1ρ estimates. DATA CONCLUSION This study demonstrates the use of different CS algorithms that may be useful in reducing the scan time required to perform volumetric T1ρ mapping of the brain. LEVEL OF EVIDENCE 2. TECHNICAL EFFICACY STAGE 1.
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Affiliation(s)
- Rajiv G Menon
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Marcelo V W Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Rajan Jain
- Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Department of Neurosurgery, New York University Grossman School of Medicine, New York, New York, USA
| | - Yulin Ge
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA
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Zibetti MVW, Johnson PM, Sharafi A, Hammernik K, Knoll F, Regatte RR. Rapid mono and biexponential 3D-T 1ρ mapping of knee cartilage using variational networks. Sci Rep 2020; 10:19144. [PMID: 33154515 PMCID: PMC7645759 DOI: 10.1038/s41598-020-76126-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 10/06/2020] [Indexed: 11/09/2022] Open
Abstract
In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin-lattice relaxation time in the rotating frame (T1ρ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T1ρ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T1ρ parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T1ρ mapping, with MNAD around 5% for AF = 2, which increases almost linearly with the AF to an MNAD of 13% for AF = 8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4% for AF = 2 and reaching MNAD of 13.1% for AF = 8. The VN was able to produce 3D-T1ρ mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5%.
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Affiliation(s)
- Marcelo V W Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA.
| | - Patricia M Johnson
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA
| | - Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA
| | | | - Florian Knoll
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA
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Johnson CP, Thedens DR, Kruger SJ, Magnotta VA. Three-Dimensional GRE T 1ρ mapping of the brain using tailored variable flip-angle scheduling. Magn Reson Med 2020; 84:1235-1249. [PMID: 32052489 DOI: 10.1002/mrm.28198] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 01/07/2020] [Accepted: 01/13/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To introduce a new approach called tailored variable flip-angle (VFA) scheduling for SNR-efficient 3D T1ρ mapping of the brain using a magnetization-prepared gradient-echo sequence. METHODS Simulations were used to assess the relative SNR efficiency, quantitative accuracy, and spatial blurring of tailored VFA scheduling for T1ρ mapping of brain tissue compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS), a state-of-the-art technique for accurate 3D gradient-echo T1ρ mapping. Simulations were also used to calculate optimal imaging parameters for tailored VFA scheduling versus MAPSS, without and with nulling of CSF. Four participants were imaged at 3T MRI to demonstrate the feasibility of tailored VFA scheduling for T1ρ mapping of the brain. Using MAPSS as a reference standard, in vivo data were used to validate the relative SNR efficiency and quantitative accuracy of the new approach. RESULTS Tailored VFA scheduling can provide a 2-fold to 4-fold gain in the SNR of the resulting T1ρ map as compared with MAPSS when using identical sequence parameters while limiting T1ρ quantification errors to 2% or less. In vivo whole-brain 3D T1ρ maps acquired with tailored VFA scheduling had superior SNR efficiency than is achievable with MAPSS, and the SNR efficiency improved with a greater number of views per segment. CONCLUSIONS Tailored VFA scheduling is an SNR-efficient GRE technique for 3D T1ρ mapping of the brain that provides increased flexibility in choice of imaging parameters compared with MAPSS, which may benefit a variety of applications.
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Affiliation(s)
- Casey P Johnson
- Veterinary Clinical Sciences Department, University of Minnesota, Saint Paul, MN, USA.,Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | | | | | - Vincent A Magnotta
- Department of Radiology, University of Iowa, Iowa City, IA, USA.,Department of Psychiatry, University of Iowa, Iowa City, IA, USA.,Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
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