Effect of hybrid of compressed sensing and parallel imaging on the quantitative values measured by 3D quantitative synthetic MRI: A phantom study

Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALA...

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Published inMagnetic resonance imaging Vol. 78; pp. 90 - 97
Main Authors Murata, Syo, Hagiwara, Akifumi, Fujita, Shohei, Haruyama, Takuya, Kato, Shimpei, Andica, Christina, Kamagata, Koji, Goto, Masami, Hori, Masaaki, Yoneyama, Masami, Hamasaki, Nozomi, Hoshito, Haruyoshi, Aoki, Shigeki
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier Inc 01.05.2021
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ISSN0730-725X
1873-5894
1873-5894
DOI10.1016/j.mri.2021.01.001

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Abstract Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (RCSS) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values. In this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using RCSS in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R2) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each RCSS and compared. Within the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200–1400 ms; T2; 50–400 ms) and PD value of 15–100% calculated from 3D-QALAS, the effects of RCSS on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R2 ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms. CS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (RCSS = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values. •The relatively long scan time of 3D-QALAS has hindered its use in clinical practice.•This study examines the effect of a hybrid of compressed sensing and parallel imaging on 3D-QALAS quantitative values.•A hybrid of compressed sensing and parallel imaging may reduce the imaging time by 63.5% and maintain quality.
AbstractList Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (R ) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values. In this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using R in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R ) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each R and compared. Within the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200-1400 ms; T2; 50-400 ms) and PD value of 15-100% calculated from 3D-QALAS, the effects of R on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms. CS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (R  = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values.
Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (RCSS) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values.INTRODUCTIONRecently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (RCSS) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values.In this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using RCSS in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R2) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each RCSS and compared.METHODSIn this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using RCSS in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R2) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each RCSS and compared.Within the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200-1400 ms; T2; 50-400 ms) and PD value of 15-100% calculated from 3D-QALAS, the effects of RCSS on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R2 ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms.RESULTSWithin the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200-1400 ms; T2; 50-400 ms) and PD value of 15-100% calculated from 3D-QALAS, the effects of RCSS on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R2 ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms.CS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (RCSS = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values.CONCLUSIONSCS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (RCSS = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values.
Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (RCSS) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values. In this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using RCSS in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R2) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each RCSS and compared. Within the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200–1400 ms; T2; 50–400 ms) and PD value of 15–100% calculated from 3D-QALAS, the effects of RCSS on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R2 ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms. CS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (RCSS = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values. •The relatively long scan time of 3D-QALAS has hindered its use in clinical practice.•This study examines the effect of a hybrid of compressed sensing and parallel imaging on 3D-QALAS quantitative values.•A hybrid of compressed sensing and parallel imaging may reduce the imaging time by 63.5% and maintain quality.
Author Fujita, Shohei
Hori, Masaaki
Hoshito, Haruyoshi
Murata, Syo
Kamagata, Koji
Yoneyama, Masami
Andica, Christina
Aoki, Shigeki
Haruyama, Takuya
Hagiwara, Akifumi
Kato, Shimpei
Goto, Masami
Hamasaki, Nozomi
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Cites_doi 10.1002/jmri.26744
10.1002/mrm.22481
10.1097/RLI.0000000000000365
10.3174/ajnr.A5905
10.2463/mrms.mp.2018-0132
10.3174/ajnr.A5398
10.1097/RLI.0000000000000510
10.1088/0031-9155/57/21/N391
10.1002/mrm.22504
10.1186/s12968-014-0102-0
10.1259/bjr.20150487
10.1615/CritRevBiomedEng.2014008058
10.1109/TMI.2016.2577642
10.1002/mrm.20605
10.1002/mrm.21391
10.2463/mrms.mp.2018-0119
10.1097/RLI.0000000000000666
10.1097/RLI.0000000000000435
10.1016/j.mri.2019.08.031
10.1007/s00234-019-02250-9
10.1002/mrm.22161
10.1097/RLI.0000000000000628
10.1016/j.mri.2016.12.014
10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S
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Keywords Sensitivity encoding
Quantitative MRI
Synthetic MRI
Compressed sensing
Three-dimensional imaging
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References Kvernby, Warntjes, Carlhäll, Engvall, Ebbers (bb0050) 2014; 16
Küstner, Würslin, Gatidis, Martirosian, Nikolaou, Schwenzer (bb0150) 2016; 35
Morita, Nakaura, Maruyama, Iyama, Oda, Utsunomiya (bb0085) 2020; 19
Russek, Boss, Jackson, Jennings, Evelhoch, Gunter (bb0120) 2012
Lustig, Donoho, Pauly (bb0105) 2007; 58
Fujita, Hagiwara, Hori, Warntjes, Kamagata, Fukunaga (bb0065) 2019; 63
Pruessmann, Weiger, Scheidegger, Boesiger (bb0090) 1999; 42
Stanisz, Odrobina, Pun, Escaravage, Graham, Bronskill (bb0145) 2005; 54
Fujita, Hagiwara, Otsuka, Hori, Takei, Hwang (bb0045) 2020; 55
Kvernby, Warntjes, Engvall, Carlhäll, Ebbers (bb0060) 2017; 38
Hagiwara, Hori, Cohen-Adad, Nakazawa, Suzuki, Kasahara (bb0140) 2019; 54
Hagiwara, Fujita, Ohno, Aoki (bb0135) 2020; 55
Kvernby, Warntjes, Haraldsson, Carlhall, Engvall, Ebbers (bb0055) 2014; 16
Liang, Liu, Wang, Ying (bb0100) 2009; 62
Liu, Duan, Peterson, Kangarlu (bb0110) 2012; 57
Andica, Hagiwara, Hori, Haruyama, Fujita, Maekawa (bb0015) 2019; 61
Maitra, Besag (bb0005) 1998; 3459
Hagiwara, Warntjes, Hori, Andica, Nakazawa, Kumamaru (bb0010) 2017; 52
Geethanath, Reddy, Konar, Imam, Sundaresan, RB (bb0075) 2013; 41
Hagiwara, Kamagata, Shimoji, Yokoyama, Andica, Hori (bb0025) 2019; 40
Tofts (bb0130) 2003
McAllister, Leach, West, Jones, Zhang, Serai (bb0020) 2017; 38
Vranic, Cross, Wang, Hippe, de Weerdt, Mossa-Basha (bb0095) 2019; 40
Fujita, Nakazawa, Hagiwara, Ueda, Horita, Maekawa (bb0030) 2019; 18
Lee, Choi, You, Lee, Kim, Kim (bb0035) 2018; 53
Jaspan, Fleysher, Lipton (bb0080) 2015; 88
Fujita, Hagiwara, Hori, Warntjes, Kamagata, Fukunaga (bb0040) 2019; 50
Chang, Ji (bb0070) 2010; 64
Keenan, Stupic, Boss, Russek, Chenevert, Prasad (bb0125) 2016
Huang, Chen, Yin, Lin, Ye, Guo (bb0115) 2010; 64
Andica (10.1016/j.mri.2021.01.001_bb0015) 2019; 61
Vranic (10.1016/j.mri.2021.01.001_bb0095) 2019; 40
Hagiwara (10.1016/j.mri.2021.01.001_bb0135) 2020; 55
Hagiwara (10.1016/j.mri.2021.01.001_bb0140) 2019; 54
Lee (10.1016/j.mri.2021.01.001_bb0035) 2018; 53
Fujita (10.1016/j.mri.2021.01.001_bb0045) 2020; 55
Keenan (10.1016/j.mri.2021.01.001_bb0125) 2016
Chang (10.1016/j.mri.2021.01.001_bb0070) 2010; 64
Liu (10.1016/j.mri.2021.01.001_bb0110) 2012; 57
Pruessmann (10.1016/j.mri.2021.01.001_bb0090) 1999; 42
Geethanath (10.1016/j.mri.2021.01.001_bb0075) 2013; 41
Tofts (10.1016/j.mri.2021.01.001_bb0130) 2003
Küstner (10.1016/j.mri.2021.01.001_bb0150) 2016; 35
Liang (10.1016/j.mri.2021.01.001_bb0100) 2009; 62
Fujita (10.1016/j.mri.2021.01.001_bb0065) 2019; 63
Fujita (10.1016/j.mri.2021.01.001_bb0040) 2019; 50
Fujita (10.1016/j.mri.2021.01.001_bb0030) 2019; 18
Kvernby (10.1016/j.mri.2021.01.001_bb0055) 2014; 16
Lustig (10.1016/j.mri.2021.01.001_bb0105) 2007; 58
Kvernby (10.1016/j.mri.2021.01.001_bb0060) 2017; 38
Russek (10.1016/j.mri.2021.01.001_bb0120) 2012
Hagiwara (10.1016/j.mri.2021.01.001_bb0025) 2019; 40
Stanisz (10.1016/j.mri.2021.01.001_bb0145) 2005; 54
Jaspan (10.1016/j.mri.2021.01.001_bb0080) 2015; 88
Kvernby (10.1016/j.mri.2021.01.001_bb0050) 2014; 16
Maitra (10.1016/j.mri.2021.01.001_bb0005) 1998; 3459
McAllister (10.1016/j.mri.2021.01.001_bb0020) 2017; 38
Huang (10.1016/j.mri.2021.01.001_bb0115) 2010; 64
Morita (10.1016/j.mri.2021.01.001_bb0085) 2020; 19
Hagiwara (10.1016/j.mri.2021.01.001_bb0010) 2017; 52
References_xml – volume: 35
  start-page: 2447
  year: 2016
  end-page: 2458
  ident: bb0150
  article-title: MR image reconstruction using a combination of compressed sensing and partial Fourier acquisition: ESPReSSo
  publication-title: IEEE Trans Med Imaging
– volume: 42
  start-page: 952
  year: 1999
  end-page: 962
  ident: bb0090
  article-title: SENSE: sensitivity encoding for fast MRI
  publication-title: Magn Reson Med
– volume: 57
  start-page: N391
  year: 2012
  end-page: N403
  ident: bb0110
  article-title: Compressed sensing MRI combined with SENSE in partialk-space
  publication-title: Phys Med Biol
– volume: 64
  start-page: 1078
  year: 2010
  end-page: 1088
  ident: bb0115
  article-title: A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: self-feeding sparse SENSE
  publication-title: Magn Reson Med
– volume: 3459
  start-page: 39
  year: 1998
  end-page: 47
  ident: bb0005
  article-title: Bayesian reconstruction in synthetic magnetic resonance imaging
  publication-title: SPIE
– volume: 64
  start-page: 1135
  year: 2010
  end-page: 1139
  ident: bb0070
  article-title: Compressed sensing MRI with multichannel data using multicore processors
  publication-title: Magn Reson Med
– volume: 62
  start-page: 1574
  year: 2009
  end-page: 1584
  ident: bb0100
  article-title: Accelerating SENSE using compressed sensing
  publication-title: Magn Reson Med
– volume: 19
  start-page: 48
  year: 2020
  end-page: 55
  ident: bb0085
  article-title: Hybrid of compressed sensing and parallel imaging applied to three-dimensional isotropic T<sub>2</sub>−weighted Turbo spin-echo MR imaging of the lumbar spine
  publication-title: Magn Reson Med Sci
– start-page: 3290
  year: 2016
  ident: bb0125
  article-title: Multi-site, multi-vendor comparison of T1 measurement using ISMRM/NIST system phantom
– volume: 40
  start-page: 1642
  year: 2019
  end-page: 1648
  ident: bb0025
  article-title: White matter abnormalities in multiple sclerosis evaluated by quantitative synthetic MRI, diffusion tensor imaging, and Neurite orientation dispersion and density imaging
  publication-title: Am J Neuroradiol
– volume: 52
  start-page: 647
  year: 2017
  end-page: 657
  ident: bb0010
  article-title: SyMRI of the brain: rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement
  publication-title: Invest Radiol
– volume: 16
  start-page: 1
  year: 2014
  end-page: 2
  ident: bb0050
  article-title: 3D-quantification using an interleaved look-locker acquisition sequence with T2-prep pulse (3D-QALAS)
  publication-title: J Cardiovasc Magn Reson
– volume: 88
  year: 2015
  ident: bb0080
  article-title: Compressed sensing MRI: a review of the clinical literature
  publication-title: Br J Radiol
– volume: 55
  start-page: 601
  year: 2020
  end-page: 616
  ident: bb0135
  article-title: Variability and standardization of quantitative imaging: monoparametric to multiparametric quantification, radiomics, and artificial intelligence
  publication-title: Invest Radiol
– volume: 53
  start-page: 236
  year: 2018
  end-page: 245
  ident: bb0035
  article-title: Age-related changes in tissue value properties in children: simultaneous quantification of relaxation times and proton density using synthetic magnetic resonance imaging
  publication-title: Invest Radiol
– volume: 50
  start-page: 1834
  year: 2019
  end-page: 1842
  ident: bb0040
  article-title: 3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: scan-rescan repeatability and comparison with conventional T1 -weighted images
  publication-title: J Magn Reson Imaging
– volume: 54
  start-page: 39
  year: 2019
  end-page: 47
  ident: bb0140
  article-title: Linearity, Bias, Intrascanner repeatability, and Interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous Relaxometry at 3 T: a validation study with a standardized phantom and healthy controls
  publication-title: Invest Radiol
– volume: 18
  start-page: 260
  year: 2019
  end-page: 264
  ident: bb0030
  article-title: Estimation of gadolinium-based contrast agent concentration using quantitative synthetic MRI and its application to brain metastases: a feasibility study
  publication-title: Magn Reson Med Sci
– volume: 38
  start-page: 2364
  year: 2017
  end-page: 2372
  ident: bb0020
  article-title: Quantitative synthetic MRI in children: normative intracranial tissue segmentation values during development
  publication-title: Am J Neuroradiol
– volume: 63
  start-page: 235
  year: 2019
  end-page: 243
  ident: bb0065
  article-title: Three-dimensional high-resolution simultaneous quantitative mapping of the whole brain with 3D-QALAS: an accuracy and repeatability study
  publication-title: Magn Reson Imaging
– volume: 40
  start-page: 92
  year: 2019
  end-page: 98
  ident: bb0095
  article-title: Compressed sensing-sensitivity encoding (CS-SENSE) accelerated brain imaging: reduced scan time without reduced image quality
  publication-title: AJNR Am J Neuroradiol
– volume: 54
  start-page: 507
  year: 2005
  end-page: 512
  ident: bb0145
  article-title: T1, T2 relaxation and magnetization transfer in tissue at 3T
  publication-title: Magn Reson Med
– volume: 38
  start-page: 13
  year: 2017
  end-page: 20
  ident: bb0060
  article-title: Clinical feasibility of 3D-QALAS–single breath-hold 3D myocardial T1-and T2-mapping
  publication-title: Magn Reson Imaging
– start-page: 85
  year: 2003
  end-page: 110
  ident: bb0130
  article-title: PD: proton density of tissue water
  publication-title: Quantitative MRI of the Brain: Measuring Changes Caused by Disease
– volume: 61
  start-page: 1055
  year: 2019
  end-page: 1066
  ident: bb0015
  article-title: Aberrant myelination in patients with Sturge-weber syndrome analyzed using synthetic quantitative magnetic resonance imaging
  publication-title: Neuroradiology
– volume: 41
  start-page: 183
  year: 2013
  end-page: 204
  ident: bb0075
  article-title: Compressed sensing MRI: a review
  publication-title: Crit Rev Biomed Eng
– volume: 58
  start-page: 1182
  year: 2007
  end-page: 1195
  ident: bb0105
  article-title: Sparse MRI: the application of compressed sensing for rapid MR imaging
  publication-title: Magn Reson Med
– volume: 16
  start-page: 1
  year: 2014
  end-page: 14
  ident: bb0055
  article-title: Simultaneous three-dimensional myocardial T1 and T2 mapping in one breath hold with 3D-QALAS
  publication-title: J Cardiovasc Magn Reson
– volume: 55
  start-page: 249
  year: 2020
  end-page: 256
  ident: bb0045
  article-title: Deep learning approach for generating MRA images from 3D quantitative synthetic MRI without additional scans
  publication-title: Invest Radiol
– start-page: 2456
  year: 2012
  ident: bb0120
  article-title: Characterization of NIST/ISMRM MRI system phantom
– volume: 50
  start-page: 1834
  issue: 6
  year: 2019
  ident: 10.1016/j.mri.2021.01.001_bb0040
  article-title: 3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: scan-rescan repeatability and comparison with conventional T1 -weighted images
  publication-title: J Magn Reson Imaging
  doi: 10.1002/jmri.26744
– volume: 64
  start-page: 1135
  issue: 4
  year: 2010
  ident: 10.1016/j.mri.2021.01.001_bb0070
  article-title: Compressed sensing MRI with multichannel data using multicore processors
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.22481
– volume: 40
  start-page: 1642
  issue: 10
  year: 2019
  ident: 10.1016/j.mri.2021.01.001_bb0025
  article-title: White matter abnormalities in multiple sclerosis evaluated by quantitative synthetic MRI, diffusion tensor imaging, and Neurite orientation dispersion and density imaging
  publication-title: Am J Neuroradiol
– volume: 52
  start-page: 647
  issue: 10
  year: 2017
  ident: 10.1016/j.mri.2021.01.001_bb0010
  article-title: SyMRI of the brain: rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement
  publication-title: Invest Radiol
  doi: 10.1097/RLI.0000000000000365
– volume: 40
  start-page: 92
  issue: 1
  year: 2019
  ident: 10.1016/j.mri.2021.01.001_bb0095
  article-title: Compressed sensing-sensitivity encoding (CS-SENSE) accelerated brain imaging: reduced scan time without reduced image quality
  publication-title: AJNR Am J Neuroradiol
  doi: 10.3174/ajnr.A5905
– volume: 19
  start-page: 48
  issue: 1
  year: 2020
  ident: 10.1016/j.mri.2021.01.001_bb0085
  article-title: Hybrid of compressed sensing and parallel imaging applied to three-dimensional isotropic T2−weighted Turbo spin-echo MR imaging of the lumbar spine
  publication-title: Magn Reson Med Sci
  doi: 10.2463/mrms.mp.2018-0132
– volume: 38
  start-page: 2364
  issue: 12
  year: 2017
  ident: 10.1016/j.mri.2021.01.001_bb0020
  article-title: Quantitative synthetic MRI in children: normative intracranial tissue segmentation values during development
  publication-title: Am J Neuroradiol
  doi: 10.3174/ajnr.A5398
– volume: 16
  start-page: 1
  issue: 1
  year: 2014
  ident: 10.1016/j.mri.2021.01.001_bb0050
  article-title: 3D-quantification using an interleaved look-locker acquisition sequence with T2-prep pulse (3D-QALAS)
  publication-title: J Cardiovasc Magn Reson
– volume: 54
  start-page: 39
  issue: 1
  year: 2019
  ident: 10.1016/j.mri.2021.01.001_bb0140
  article-title: Linearity, Bias, Intrascanner repeatability, and Interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous Relaxometry at 3 T: a validation study with a standardized phantom and healthy controls
  publication-title: Invest Radiol
  doi: 10.1097/RLI.0000000000000510
– volume: 57
  start-page: N391
  issue: 21
  year: 2012
  ident: 10.1016/j.mri.2021.01.001_bb0110
  article-title: Compressed sensing MRI combined with SENSE in partialk-space
  publication-title: Phys Med Biol
  doi: 10.1088/0031-9155/57/21/N391
– volume: 64
  start-page: 1078
  issue: 4
  year: 2010
  ident: 10.1016/j.mri.2021.01.001_bb0115
  article-title: A rapid and robust numerical algorithm for sensitivity encoding with sparsity constraints: self-feeding sparse SENSE
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.22504
– volume: 16
  start-page: 1
  issue: 1
  year: 2014
  ident: 10.1016/j.mri.2021.01.001_bb0055
  article-title: Simultaneous three-dimensional myocardial T1 and T2 mapping in one breath hold with 3D-QALAS
  publication-title: J Cardiovasc Magn Reson
  doi: 10.1186/s12968-014-0102-0
– volume: 88
  issue: 1056
  year: 2015
  ident: 10.1016/j.mri.2021.01.001_bb0080
  article-title: Compressed sensing MRI: a review of the clinical literature
  publication-title: Br J Radiol
  doi: 10.1259/bjr.20150487
– volume: 41
  start-page: 183
  issue: 3
  year: 2013
  ident: 10.1016/j.mri.2021.01.001_bb0075
  article-title: Compressed sensing MRI: a review
  publication-title: Crit Rev Biomed Eng
  doi: 10.1615/CritRevBiomedEng.2014008058
– volume: 35
  start-page: 2447
  issue: 11
  year: 2016
  ident: 10.1016/j.mri.2021.01.001_bb0150
  article-title: MR image reconstruction using a combination of compressed sensing and partial Fourier acquisition: ESPReSSo
  publication-title: IEEE Trans Med Imaging
  doi: 10.1109/TMI.2016.2577642
– start-page: 85
  year: 2003
  ident: 10.1016/j.mri.2021.01.001_bb0130
  article-title: PD: proton density of tissue water
– volume: 54
  start-page: 507
  issue: 3
  year: 2005
  ident: 10.1016/j.mri.2021.01.001_bb0145
  article-title: T1, T2 relaxation and magnetization transfer in tissue at 3T
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.20605
– volume: 58
  start-page: 1182
  issue: 6
  year: 2007
  ident: 10.1016/j.mri.2021.01.001_bb0105
  article-title: Sparse MRI: the application of compressed sensing for rapid MR imaging
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.21391
– volume: 18
  start-page: 260
  issue: 4
  year: 2019
  ident: 10.1016/j.mri.2021.01.001_bb0030
  article-title: Estimation of gadolinium-based contrast agent concentration using quantitative synthetic MRI and its application to brain metastases: a feasibility study
  publication-title: Magn Reson Med Sci
  doi: 10.2463/mrms.mp.2018-0119
– volume: 55
  start-page: 601
  issue: 9
  year: 2020
  ident: 10.1016/j.mri.2021.01.001_bb0135
  article-title: Variability and standardization of quantitative imaging: monoparametric to multiparametric quantification, radiomics, and artificial intelligence
  publication-title: Invest Radiol
  doi: 10.1097/RLI.0000000000000666
– volume: 53
  start-page: 236
  issue: 4
  year: 2018
  ident: 10.1016/j.mri.2021.01.001_bb0035
  article-title: Age-related changes in tissue value properties in children: simultaneous quantification of relaxation times and proton density using synthetic magnetic resonance imaging
  publication-title: Invest Radiol
  doi: 10.1097/RLI.0000000000000435
– volume: 63
  start-page: 235
  year: 2019
  ident: 10.1016/j.mri.2021.01.001_bb0065
  article-title: Three-dimensional high-resolution simultaneous quantitative mapping of the whole brain with 3D-QALAS: an accuracy and repeatability study
  publication-title: Magn Reson Imaging
  doi: 10.1016/j.mri.2019.08.031
– volume: 61
  start-page: 1055
  issue: 9
  year: 2019
  ident: 10.1016/j.mri.2021.01.001_bb0015
  article-title: Aberrant myelination in patients with Sturge-weber syndrome analyzed using synthetic quantitative magnetic resonance imaging
  publication-title: Neuroradiology
  doi: 10.1007/s00234-019-02250-9
– volume: 62
  start-page: 1574
  issue: 6
  year: 2009
  ident: 10.1016/j.mri.2021.01.001_bb0100
  article-title: Accelerating SENSE using compressed sensing
  publication-title: Magn Reson Med
  doi: 10.1002/mrm.22161
– start-page: 3290
  year: 2016
  ident: 10.1016/j.mri.2021.01.001_bb0125
  article-title: Multi-site, multi-vendor comparison of T1 measurement using ISMRM/NIST system phantom
– volume: 55
  start-page: 249
  issue: 4
  year: 2020
  ident: 10.1016/j.mri.2021.01.001_bb0045
  article-title: Deep learning approach for generating MRA images from 3D quantitative synthetic MRI without additional scans
  publication-title: Invest Radiol
  doi: 10.1097/RLI.0000000000000628
– volume: 38
  start-page: 13
  year: 2017
  ident: 10.1016/j.mri.2021.01.001_bb0060
  article-title: Clinical feasibility of 3D-QALAS–single breath-hold 3D myocardial T1-and T2-mapping
  publication-title: Magn Reson Imaging
  doi: 10.1016/j.mri.2016.12.014
– volume: 3459
  start-page: 39
  year: 1998
  ident: 10.1016/j.mri.2021.01.001_bb0005
  article-title: Bayesian reconstruction in synthetic magnetic resonance imaging
  publication-title: SPIE
– volume: 42
  start-page: 952
  issue: 5
  year: 1999
  ident: 10.1016/j.mri.2021.01.001_bb0090
  article-title: SENSE: sensitivity encoding for fast MRI
  publication-title: Magn Reson Med
  doi: 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S
– start-page: 2456
  year: 2012
  ident: 10.1016/j.mri.2021.01.001_bb0120
  article-title: Characterization of NIST/ISMRM MRI system phantom
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Snippet Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple...
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SubjectTerms Adult
Brain - diagnostic imaging
Compressed sensing
Female
Humans
Imaging, Three-Dimensional
Linear Models
Magnetic Resonance Imaging - instrumentation
Male
Phantoms, Imaging
Quantitative MRI
Sensitivity encoding
Signal-To-Noise Ratio
Synthetic MRI
Three-dimensional imaging
Title Effect of hybrid of compressed sensing and parallel imaging on the quantitative values measured by 3D quantitative synthetic MRI: A phantom study
URI https://www.clinicalkey.com/#!/content/1-s2.0-S0730725X21000023
https://dx.doi.org/10.1016/j.mri.2021.01.001
https://www.ncbi.nlm.nih.gov/pubmed/33444595
https://www.proquest.com/docview/2478598832
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