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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Background:

Synthetic MRI can provide multiple contrast-weighted brain images with high resolution from a single scan a 3D sequence using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS).

Objective:

This study aimed to assess the diagnostic image quality of 3D synthetic MRI using compressed sensing (CS) in clinical practice.

Methods:

We retrospectively reviewed the imaging data of 47 patients who underwent brain MRI, including 3D synthetic MRI using CS in a single session, between December 2020 and February 2021. Two neuroradiologists independently evaluated the overall image quality, anatomic demarcation, and artifacts for synthetic 3D T1-weighted, T2-weighted, fluid-attenuated inversion recovery (FLAIR), phase-sensitive inversion recovery (PSIR), and double inversion recovery images, using a 5-point Likert scale. The interobserver agreement between the two readers was assessed using percent agreement and weighted κ statistics.

Results:

The overall image quality of 3D synthetic T1WI and PSIR was good to excellent, with easy or excellent anatomic demarcation and mild or no visible artifact. However, other 3D synthetic MRI-derived images showed insufficient image quality and anatomic demarcation with marked CSF pulsation artifacts. In particular, 3D synthetic FLAIR showed high-signal artifacts on the brain surface.

Conclusion:

3D synthetic MRI, at its current status, cannot completely replace conventional brain MRI in daily clinical practice. However, 3D synthetic MRI can achieve scan-time reduction using CS and parallel imaging and may be useful for motion-prone or pediatric patients requiring 3D images where time-efficiency is important.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-10-27
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References

  1. FujitaS. HagiwaraA. HoriM. WarntjesM. KamagataK. FukunagaI. GotoM. TakuyaH. TakasuK. AndicaC. MaekawaT. TakemuraM.Y. IrieR. WadaA. SuzukiM. AokiS. 3D quantitative synthetic MRI‐derived cortical thickness and subcortical brain volumes: Scan–rescan repeatability and comparison with conventional T 1 weighted images.J. Magn. Reson. Imaging20195061834184210.1002/jmri.2674430968991
    [Google Scholar]
  2. FujitaS. HagiwaraA. HoriM. WarntjesM. KamagataK. FukunagaI. AndicaC. MaekawaT. IrieR. TakemuraM.Y. KumamaruK.K. WadaA. SuzukiM. OzakiY. AbeO. AokiS. Three-dimensional high-resolution simultaneous quantitative mapping of the whole brain with 3D-QALAS: An accuracy and repeatability study.Magn. Reson. Imaging20196323524310.1016/j.mri.2019.08.03131445118
    [Google Scholar]
  3. FujitaS. YokoyamaK. HagiwaraA. KatoS. AndicaC. KamagataK. HattoriN. AbeO. AokiS. 3D quantitative synthetic MRI in the evaluation of multiple sclerosis lesions.AJNR Am. J. Neuroradiol.202142347147810.3174/ajnr.A693033414234
    [Google Scholar]
  4. KraussW. GunnarssonM. NilssonM. ThunbergP. Conventional and synthetic MRI in multiple sclerosis: A comparative study.Eur. Radiol.20182841692170010.1007/s00330‑017‑5100‑929134354
    [Google Scholar]
  5. HagiwaraA. HoriM. YokoyamaK. TakemuraM.Y. AndicaC. TabataT. KamagataK. SuzukiM. KumamaruK.K. NakazawaM. TakanoN. KawasakiH. HamasakiN. KunimatsuA. AokiS. Synthetic MRI in the detection of multiple sclerosis plaques.AJNR Am. J. Neuroradiol.201738225726310.3174/ajnr.A501227932506
    [Google Scholar]
  6. HagiwaraA. HoriM. SuzukiM. AndicaC. NakazawaM. TsurutaK. TakanoN. SatoS. HamasakiN. YoshidaM. KumamaruK.K. OhtomoK. AokiS. Contrast-enhanced synthetic MRI for the detection of brain metastases.Acta Radiol. Open20165210.1177/205846011562675726962461
    [Google Scholar]
  7. LandisJ.R. KochG.G. The measurement of observer agreement for categorical data.Biometrics197733115917410.2307/2529310843571
    [Google Scholar]
  8. AndicaC. HagiwaraA. HoriM. NakazawaM. GotoM. KoshinoS. KamagataK. KumamaruK.K. AokiS. Automated brain tissue and myelin volumetry based on quantitative MR imaging with various in-plane resolutions.J. Neuroradiol.201845316416810.1016/j.neurad.2017.10.00229132939
    [Google Scholar]
  9. FujitaS. HagiwaraA. OtsukaY. HoriM. TakeiN. HwangK.P. IrieR. AndicaC. KamagataK. AkashiT. Kunishima KumamaruK. SuzukiM. WadaA. AbeO. AokiS. Deep learning approach for generating MRA images from 3D quantitative synthetic MRI without additional scans.Invest. Radiol.202055424925610.1097/RLI.000000000000062831977603
    [Google Scholar]
  10. LeeS.M. ChoiY.H. CheonJ.E. KimI.O. ChoS.H. KimW.H. KimH.J. ChoH.H. YouS.K. ParkS.H. HwangM.J. Image quality at synthetic brain magnetic resonance imaging in children.Pediatr. Radiol.201747121638164710.1007/s00247‑017‑3913‑y28638982
    [Google Scholar]
  11. KvernbyS. WarntjesM.J.B. HaraldssonH. CarlhällC.J. EngvallJ. EbbersT. Simultaneous three-dimensional myocardial T1 and T2 mapping in one breath hold with 3D-QALAS.J. Cardiovasc. Magn. Reson.201416110210.1186/s12968‑014‑0102‑025526880
    [Google Scholar]
  12. LikertR. A technique for the measurement of attitudes.Arch. Psychol.19322214055
    [Google Scholar]
  13. BlystadI. WarntjesJ.B.M. SmedbyO. LandtblomA-M. LundbergP. LarssonE-M. Synthetic MRI of the brain in a clinical setting.Acta Radiol.201253101158116310.1258/ar.2012.12019523024181
    [Google Scholar]
  14. TanenbaumL.N. TsiourisA.J. JohnsonA.N. NaidichT.P. DeLanoM.C. MelhemE.R. QuartermanP. ParameswaranS.X. ShankaranarayananA. GoyenM. FieldA.S. Synthetic MRI for clinical neuroimaging: Results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial.AJNR Am. J. Neuroradiol.20173861103111010.3174/ajnr.A522728450439
    [Google Scholar]
  15. WestH. LeachJ.L. JonesB.V. CareM. RadhakrishnanR. MerrowA.C. AlvaradoE. SeraiS.D. Clinical validation of synthetic brain MRI in children: Initial experience.Neuroradiology2017591435010.1007/s00234‑016‑1765‑z27889836
    [Google Scholar]
  16. BettsA.M. LeachJ.L. JonesB.V. ZhangB. SeraiS. Brain imaging with synthetic MR in children: Clinical quality assessment.Neuroradiology201658101017102610.1007/s00234‑016‑1723‑927438803
    [Google Scholar]
  17. RyuK.H. BaekH.J. MoonJ.I. Initial clinical experience of synthetic MRI as a routine neuroimaging protocol in daily practice: a single-center study.Journal of Neuroradiology202047151160
    [Google Scholar]
  18. JiS. YangD. LeeJ. ChoiS.H. KimH. KangK.M. Synthetic MRI: technologies and applications in neuroradiology.J Magn Reson Imaging20225541013102533188560
    [Google Scholar]
  19. GranbergT. UppmanM. HashimF. CananauC. NordinL.E. ShamsS. BerglundJ. ForslinY. AspelinP. FredriksonS. Kristoffersen-WibergM. Clinical feasibility of synthetic MRI in multiple sclerosis: A diagnostic and volumetric validation study.AJNR Am. J. Neuroradiol.20163761023102910.3174/ajnr.A466526797137
    [Google Scholar]
  20. RyuK. NamY. GhoS.M. JangJ. LeeH.J. ChaJ. BaekH.J. ParkJ. KimD.H. Data‐driven synthetic MRI FLAIR artifact correction via deep neural network.J. Magn. Reson. Imaging20195051413142310.1002/jmri.2671230884007
    [Google Scholar]
  21. HagiwaraA. OtsukaY. HoriM. TachibanaY. YokoyamaK. FujitaS. AndicaC. KamagataK. IrieR. KoshinoS. MaekawaT. ChougarL. WadaA. TakemuraM.Y. HattoriN. AokiS. Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation.AJNR Am. J. Neuroradiol.201940222423010.3174/ajnr.A592730630834
    [Google Scholar]
  22. KingK. XuD. BrauA.C. LaiP. BeattyP.J. MarinelliL. A new combination of compressed sensing and data driven parallel imaging.Proceedings of the 18th Scientific Meeting International Society for Magnetic Resonance in Medicine2010
    [Google Scholar]
  23. TakeiN. HagiwaraA. FujitaS. Compressed sensing 3D multi-parametric imaging toward isotropic 1mm3 imaging.Proceedings of the 27th Annual Meeting of ISMRM2019
    [Google Scholar]
  24. FujitaS. HagiwaraA. TakeiN. HwangK.P. FukunagaI. KatoS. AndicaC. KamagataK. YokoyamaK. HattoriN. AbeO. AokiS. Accelerated isotropic multiparametric imaging by high spatial resolution 3D-QALAS with compressed sensing: A phantom, volunteer, and patient study.Invest. Radiol.202156529230010.1097/RLI.000000000000074433273376
    [Google Scholar]
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