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Comparison between synthetic and conventional magnetic resonance imaging in patients with multiple sclerosis and controls

  • Francesca Di Giuliano
  • Silvia MinosseEmail author
  • Eliseo Picchi
  • Girolama Alessandra Marfia
  • Valerio Da Ros
  • Massimo Muto
  • Mario Muto
  • Chiara Adriana Pistolese
  • Andrea Laghi
  • Francesco Garaci
  • Roberto Floris
Research Article

Abstract

Objectives

Synthetic magnetic resonance imaging (SyMRI) allows to obtain different weighted-images using the multiple-dynamic multiple-echo sequence lasting 6 min. The aim is to compare quantitatively and qualitatively synthetic- and conventional MRI in patients with multiple sclerosis (MS) and controls assessing the contrast (C), the signal to noise ratio (SNR), and the contrast to noise ratio (CNR). We evaluated the lesion count and lesion-to-white matter contrast (\({\text{C}}_{{\text{l } - \text{ WM}}} {)}\) in the MS patients.

Methods and methods

51 patients underwent synthetic- and conventional MRI. Qualitative analysis was evaluated by assigning scores to all synthetic- and conventional MRI sequences by two neuroradiologists. Lesions were counted in MS patients both in the conventional- and synthetic T2-FLAIR. Regions of interest were placed in the cerebrospinal fluid, in the white- and grey matter. For the sequences were evaluated: C, CNR, and SNR.

Results

Synthetic T2-FLAIR images are qualitatively inferior. C and CNR were significantly higher in synthetic T1W and T2W images compared to conventional images, but not for T2-FLAIR. The SNR value was always lower in synthetic images than in conventional ones.

Conclusions

SyMRI can be used in clinical practice because it has a similar diagnostic accuracy which reduces the scanning time compared to the conventional one. However, synthetic T2-FLAIR images need to be improved.

Keywords

Multiple sclerosis Brain Magnetic resonance imaging Synthetic magnetic resonance imaging 

Notes

Funding

Supported by research project "Magic-MRI"—Villa Benedetta Group.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no financial activities related to the present article.

Ethical approval

This prospective, single-centre, Health Insurance Portability and Accountability Act-compliant study was approved by the Institutional Review Board.

Informed consent

Patients gave written informed consent before enrolment.

References

  1. 1.
    Maitra R, Riddles JJ (2010) Synthetic magnetic resonance imaging revisited. IEEE Trans Med Imaging.  https://doi.org/10.1109/TMI.2009.2039487 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Riederer SJ, Lee JN, Farzaneh F, Wang HZ, Wright RC (1986) Magnetic resonance image synthesis. Clinical implementation. Acta Radiol Suppl 369:466–468PubMedGoogle Scholar
  3. 3.
    Betts AM, Leach JL, Jones BV, Zhang B, Serai S (2016) Brain imaging with synthetic MR in children: clinical quality assessment. Neuroradiology.  https://doi.org/10.1007/s00234-016-1723-9 CrossRefPubMedGoogle Scholar
  4. 4.
    West H, Leach JL, Jones BV, Care M, Radhakrishnan R, Merrow AC, Alvarado E, Serai SD (2017) Clinical validation of synthetic brain MRI in children: initial experience. Neuroradiology.  https://doi.org/10.1007/s00234-016-1765-z CrossRefPubMedGoogle Scholar
  5. 5.
    Lee SM, Choi YH, Cheon JE, Kim IO, Cho SH, Kim WH, Kim HJ, Cho HH, You SK, Park SH, Hwang MJ (2017) Image quality at synthetic brain magnetic resonance imaging in children. Pediatr Radiol.  https://doi.org/10.1007/s00247-017-3913-y CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    McAllister A, Leach J, West H, Jones B, Zhang B, Serai S (2017) Quantitative synthetic MRI in children: normative intracranial tissue segmentation values during development. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A5398 CrossRefPubMedGoogle Scholar
  7. 7.
    Park S, Kwack KS, Lee YJ, Gho SM, Lee HY (2017) Initial experience with synthetic MRI of the knee at 3T: comparison with conventional T1weighted imaging and T2mapping. Br J Radiol.  https://doi.org/10.1259/bjr.20170350 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Boudabbous S, Neroladaki A, Bagetakos I, Hamard M, Delattre BM, Vargas MI (2018) Feasibility of synthetic MRI in knee imaging in routine practice. Acta Radiol Open.  https://doi.org/10.1177/2058460118769686 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Larsson HBW, Frederiksen J, Kjær L, Henriksen O, Olesen J (1988) In vivo determination of T1and T2in the brain of patients with severe but stable multiple sclerosis. Magn Reson Med.  https://doi.org/10.1002/mrm.1910070106 CrossRefPubMedGoogle Scholar
  10. 10.
    Hasan KM, Walimuni IS, Abid H, Wolinsky JS, Narayana PA (2012) Multi-modal quantitative MRI investigation of brain tissue neurodegeneration in multiple sclerosis. J Magn Reson Imaging.  https://doi.org/10.1002/jmri.23539 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Townsend TN, Bernasconi N, Pike GB, Bernasconi A (2004) Quantitative analysis of temporal lobe white matter T2 relaxation time in temporal lobe epilepsy. Neuroimage.  https://doi.org/10.1016/j.neuroimage.2004.06.009 CrossRefPubMedGoogle Scholar
  12. 12.
    Mamere AE, Saraiva LAL, Matos ALM, Carneiro AAO, Santos AC (2009) Evaluation of delayed neuronal and axonal damage secondary to moderate and severe traumatic brain injury using quantitative MR imaging techniques. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A1477 CrossRefPubMedGoogle Scholar
  13. 13.
    Granziera C, Daducci A, Donati A, Bonnier G, Romascano D, Roche A, Bach Cuadra M, Schmitter D, Klöppel S, Meuli R, Von Gunten A, Krueger G (2015) A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment. NeuroImage Clin.  https://doi.org/10.1016/j.nicl.2015.06.003 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Bobman SA, Riederer SJ, Lee JN, Suddarth SA, Wang HZ, Drayer BP, MacFall JR (1985) Cerebral magnetic resonance image synthesis. Am J Neuroradiol 6:265–269PubMedGoogle Scholar
  15. 15.
    Granberg T, Uppman M, Hashim F, Cananau C, Nordin LE, Shams S, Berglund J, Forslin Y, Aspelin P, Fredrikson S, Kristoffersen-Wiberg M (2016) Clinical feasibility of synthetic MRI in multiple sclerosis: a diagnostic and volumetric validation study. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A4665 CrossRefPubMedGoogle Scholar
  16. 16.
    Krauss W, Gunnarsson M, Nilsson M, Thunberg P (2018) Conventional and synthetic MRI in multiple sclerosis: a comparative study. Eur Radiol.  https://doi.org/10.1007/s00330-017-5100-9 CrossRefPubMedGoogle Scholar
  17. 17.
    Blystad I, Warntjes JBM, Smedby O, Landtblom AM, Lundberg P, Larsson EM (2012) Synthetic MRI of the brain in a clinical setting. Acta Radiol.  https://doi.org/10.1258/ar.2012.120195 CrossRefPubMedGoogle Scholar
  18. 18.
    Tanenbaum LN, Tsiouris AJ, Johnson AN, Naidich TP, DeLano MC, Melhem ER, Quarterman P, Parameswaran SX, Shankaranarayanan A, Goyen M, Field AS (2017) Synthetic MRI for clinical neuroimaging: results of the magnetic resonance image compilation (MAGiC) prospective, multicenter, multireader trial. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A5227 CrossRefPubMedGoogle Scholar
  19. 19.
    Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging.  https://doi.org/10.1016/j.mri.2012.05.001 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Hagiwara A, Warntjes M, Hori M, Andica C, Nakazawa M, Kumamaru KK, Abe O, Aoki S (2017) SyMRI of the brain: rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement. Invest Radiol.  https://doi.org/10.1097/RLI.0000000000000365 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Hagiwara A, Hori M, Yokoyama K, Takemura MY, Andica C, Tabata T, Kamagata K, Suzuki M, Kumamaru KK, Nakazawa M, Takano N, Kawasaki H, Hamasaki N, Kunimatsu A, Aoki S (2017) Synthetic MRI in the detection of multiple sclerosis plaques. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A5012 CrossRefPubMedGoogle Scholar
  22. 22.
    Ryu K, Nam Y, Gho S, Jang J, Lee H, Cha J, Baek HJ, Park J, Kim D (2019) Data-driven synthetic MRI FLAIR artifact correction via deep neural network. J Magn Reson Imaging.  https://doi.org/10.1002/jmri.26712 CrossRefPubMedGoogle Scholar
  23. 23.
    Hagiwara A, Otsuka Y, Hori M, Tachibana Y, Yokoyama K, Fujita S, Andica C, Kamagata K, Irie R, Koshino S, Maekawa T, Chougar L, Wada A, Takemura MY, Hattori N, Aoki S (2019) Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A5927 CrossRefPubMedGoogle Scholar
  24. 24.
    Bedell BJ, Narayana PA (1998) Implementation and evaluation of a new pulse sequence for rapid acquisition of double inversion recovery images for simultaneous suppression of white matter and CSF. J Magn Reson Imaging.  https://doi.org/10.1002/jmri.1880080305 CrossRefPubMedGoogle Scholar
  25. 25.
    Nelson F, Poonawalla AH, Hou P, Huang F, Wolinsky JS, Narayana PA (2007) Improved identification of intracortical lesions in multiple sclerosis with phase-sensitive inversion recovery in combination with fast double inversion recovery MR imaging. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A0645 CrossRefPubMedGoogle Scholar
  26. 26.
    Blystad I, Håkansson I, Tisell A, Ernerudh J, Smedby LP, Larsson EM (2016) Quantitative MRI for analysis of active multiple sclerosis lesions without gadolinium-based contrast agent. Am J Neuroradiol.  https://doi.org/10.3174/ajnr.A4501 CrossRefPubMedGoogle Scholar
  27. 27.
    Hagiwara A, Hori M, Suzuki M, Andica C, Nakazawa M, Tsuruta K, Takano N, Sato S, Hamasaki N, Yoshida M, Kumamaru KK, Ohtomo K, Aoki S (2016) Contrast-enhanced synthetic MRI for the detection of brain metastases. Acta Radiol Open.  https://doi.org/10.1177/2058460115626757 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2019

Authors and Affiliations

  • Francesca Di Giuliano
    • 1
    • 2
  • Silvia Minosse
    • 1
    Email author
  • Eliseo Picchi
    • 1
    • 2
  • Girolama Alessandra Marfia
    • 3
    • 4
  • Valerio Da Ros
    • 5
  • Massimo Muto
    • 6
  • Mario Muto
    • 7
  • Chiara Adriana Pistolese
    • 1
    • 2
  • Andrea Laghi
    • 8
  • Francesco Garaci
    • 1
    • 2
  • Roberto Floris
    • 1
    • 2
  1. 1.Department of Biomedicine and PreventionUniversity of Rome “Tor Vergata”RomeItaly
  2. 2.U.O.C Diagnostic Imaging and Neuroradiology, Department of Integrated Care ProcessesFondazione PTV Policlinico “Tor Vergata”, University of Rome “Tor Vergata”RomeItaly
  3. 3.Multiple Sclerosis Clinical and Research Unit, Department of Systems MedicineUniversity of Rome “Tor Vergata”RomeItaly
  4. 4.Neurology Unit, Department of NeurosciencesFondazione PTV Policlinico “Tor Vergata”, University of Rome “Tor Vergata”RomeItaly
  5. 5.Department of Diagnostic Imaging and Interventional RadiologyPoliclinico Tor VergataRomeItaly
  6. 6.Department of Neurosciences and Reproductive and Odontostomatological SciencesUniversity of Naples Federico IINaplesItaly
  7. 7.Department of NeuroradiologyA.O.R.N. CardarelliNaplesItaly
  8. 8.Department of Surgical and Medical Sciences and Translational Medicine, Radiology Unit“Sapienza” University of Rome, Sant’Andrea University HospitalRomeItaly

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