nach oben

Erschienen in:

01.08.2019 | Original Communication

Optimizing 3D FLAIR to detect MS lesions: pushing past factory settings for precise results

verfasst von: Augustin Lecler, C. Bouzad, R. Deschamps, F. Maizeroi, J. C. Sadik, A. Gueguen, O. Gout, H. Picard, J. Savatovsky

Erschienen in: Journal of Neurology | Ausgabe 11/2019

Einloggen, um Zugang zu erhalten

Abstract

Background

To assess the diagnostic value of three 3D FLAIR sequences with differing repetition-times (TR) at 3-Tesla when detecting multiple sclerosis (MS) lesions.

Methods

In this prospective study, approved by the institutional review board, 27 patients with confirmed MS were prospectively included. One radiologist performed manual segmentations of all high-signal intensity lesions using three 3D FLAIR data sets with different TR of 4800 ms (“FLAIR₄₈₀₀”), 8000 ms (“FLAIR₈₀₀₀”) and 10,000 ms (“FLAIR_10,000”) and two radiologists double-checked it. The main judgment criterion was the overall number of lesions; secondary objectives were the assessment of lesion location, as well as measuring contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). A non-parametric Wilcoxon’s test was used to compare the differing FLAIR.

Results

The FLAIR₈₀₀₀ and FLAIR_10,000 detected significantly more overall lesions per patient as compared with the FLAIR₄₈₀₀ [116.1 (± 61.7) (p = 0.02) and 115.8 (± 56.3) (p = 0.03) versus 99.2 (± 66.9), respectively]. The FLAIR₈₀₀₀ and FLAIR_10,000 detected four and eight times more cortical or juxta-cortical lesions per patient as compared with FLAIR₄₈₀₀ [1.6 (± 2.2) (p = 0.001) and 4.1 (± 5.9) (p = 6 × 10^–5) versus 0.4 (± 1.1), respectively]. CNR was significantly correlated to the TR value. It was significantly higher with FLAIR_10,000 than it was with FLAIR₈₀₀₀ and FLAIR₄₈₀₀ [16.3 (± 3.5) versus 15 (± 2.4) (p = 0.01) and 12 (± 2.2) (p = 2 × 10^–6), respectively]

Conclusion

An optimized 3D FLAIR with a long TR significantly improved both overall lesion detection and CNR in MS patients as compared to a 3D FLAIR with factory settings.

Nur mit Berechtigung zugänglich

Geurts JJG, Calabrese M, Fisher E, Rudick RA (2012) Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol 11:1082–1092. https://doi.org/10.1016/S1474-4422(12)70230-2 CrossRefPubMed

Klaver R, De Vries HE, Schenk GJ, Geurts JJG (2013) Grey matter damage in multiple sclerosis: a pathology perspective. Prion 7:66–75. https://doi.org/10.4161/pri.23499 CrossRefPubMedPubMedCentral

Messina S, Patti F (2014) Gray matters in multiple sclerosis: cognitive impairment and structural MRI. Mult Scler Int 2014:609694. https://doi.org/10.1155/2014/609694 CrossRefPubMedPubMedCentral

Pirko I, Lucchinetti CF, Sriram S, Bakshi R (2007) Gray matter involvement in multiple sclerosis. Neurology 68:634–642. https://doi.org/10.1212/01.wnl.0000250267.85698.7a CrossRefPubMed

Runge VM, Price AC, Kirshner HS et al (1984) Magnetic resonance imaging of multiple sclerosis: a study of pulse-technique efficacy. AJR Am J Roentgenol 143:1015–1026. https://doi.org/10.2214/ajr.143.5.1015 CrossRefPubMed

McDonald WI, Compston A, Edan G et al (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann Neurol 50:121–127CrossRef

Thompson AJ, Banwell BL, Barkhof F et al (2017) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. https://doi.org/10.1016/S1474-4422(17)30470-2 CrossRefPubMed

Barkhof F, Filippi M, Miller DH et al (1997) Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain J Neurol 120(Pt 11):2059–2069CrossRef

Tintoré M, Rovira A, Martínez MJ et al (2000) Isolated demyelinating syndromes: comparison of different MR imaging criteria to predict conversion to clinically definite multiple sclerosis. AJNR Am J Neuroradiol 21:702–706PubMed

10.

Rovira À, Wattjes MP, Tintoré M et al (2015) Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-clinical implementation in the diagnostic process. Nat Rev Neurol 11:471–482. https://doi.org/10.1038/nrneurol.2015.106 CrossRefPubMed

11.

Traboulsee A, Simon JH, Stone L et al (2016) Revised recommendations of the consortium of MS centers task force for a standardized MRI protocol and clinical guidelines for the diagnosis and follow-up of multiple sclerosis. AJNR Am J Neuroradiol 37:394–401. https://doi.org/10.3174/ajnr.A4539 CrossRefPubMed

12.

Gramsch C, Nensa F, Kastrup O et al (2015) Diagnostic value of 3D fluid attenuated inversion recovery sequence in multiple sclerosis. Acta Radiol Stockh Swed 1987 56:622–627. https://doi.org/10.1177/0284185114534413 CrossRef

13.

Okuda T, Korogi Y, Shigematsu Y et al (1999) Brain lesions: when should fluid-attenuated inversion-recovery sequences be used in MR evaluation? Radiology 212:793–798. https://doi.org/10.1148/radiology.212.3.r99se07793 CrossRefPubMed

14.

Tan IL, van Schijndel RA, Pouwels PJW et al (2002) Serial isotropic three-dimensional fast FLAIR imaging: using image registration and subtraction to reveal active multiple sclerosis lesions. AJR Am J Roentgenol 179:777–782. https://doi.org/10.2214/ajr.179.3.1790777 CrossRefPubMed

15.

Bink A, Schmitt M, Gaa J et al (2006) Detection of lesions in multiple sclerosis by 2D FLAIR and single-slab 3D FLAIR sequences at 3.0 T: initial results. Eur Radiol 16:1104–1110. https://doi.org/10.1007/s00330-005-0107-z CrossRefPubMed

16.

Moraal B, Roosendaal SD, Pouwels PJW et al (2008) Multi-contrast, isotropic, single-slab 3D MR imaging in multiple sclerosis. Eur Radiol 18:2311–2320. https://doi.org/10.1007/s00330-008-1009-7 CrossRefPubMed

17.

Hashemi RH, Bradley WG, Chen DY et al (1995) Suspected multiple sclerosis: MR imaging with a thin-section fast FLAIR pulse sequence. Radiology 196:505–510. https://doi.org/10.1148/radiology.196.2.7617868 CrossRefPubMed

18.

Iannucci G, Rovaris M, Giacomotti L et al (2001) Correlation of multiple sclerosis measures derived from T2-weighted, T1-weighted, magnetization transfer, and diffusion tensor MR imaging. AJNR Am J Neuroradiol 22:1462–1467PubMed

19.

Nelson F, Poonawalla A, Hou P et al (2008) 3D MPRAGE improves classification of cortical lesions in multiple sclerosis. Mult Scler Houndmills Basingstoke Engl 14:1214–1219. https://doi.org/10.1177/1352458508094644 CrossRef

20.

Polak P, Magnano C, Zivadinov R, Poloni G (2012) 3D FLAIRED: 3D fluid attenuated inversion recovery for enhanced detection of lesions in multiple sclerosis. Magn Reson Med 68:874–881. https://doi.org/10.1002/mrm.23289 CrossRefPubMed

21.

Geurts JJG, Pouwels PJW, Uitdehaag BMJ et al (2005) Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. Radiology 236:254–260. https://doi.org/10.1148/radiol.2361040450 CrossRefPubMed

22.

Vural G, Keklikoğlu HD, Temel Ş et al (2013) Comparison of double inversion recovery and conventional magnetic resonance brain imaging in patients with multiple sclerosis and relations with disease disability. Neuroradiol J 26:133–142. https://doi.org/10.1177/197140091302600201 CrossRefPubMedPubMedCentral

23.

Wattjes MP, Lutterbey GG, Gieseke J et al (2007) Double inversion recovery brain imaging at 3T: diagnostic value in the detection of multiple sclerosis lesions. AJNR Am J Neuroradiol 28:54–59CrossRef

24.

Favaretto A, Poggiali D, Lazzarotto A et al (2015) The parallel analysis of phase sensitive inversion recovery (PSIR) and double inversion recovery (DIR) images significantly improves the detection of cortical lesions in multiple sclerosis (MS) since clinical onset. PLoS ONE 10:e0127805. https://doi.org/10.1371/journal.pone.0127805 CrossRefPubMedPubMedCentral

25.

Nelson F, Poonawalla AH, Hou P et al (2007) Improved identification of intracortical lesions in multiple sclerosis with phase-sensitive inversion recovery in combination with fast double inversion recovery MR imaging. AJNR Am J Neuroradiol 28:1645–1649. https://doi.org/10.3174/ajnr.A0645 CrossRefPubMed

26.

Lecler A, El Sanharawi I, El Methni J et al (2019) Improving detection of multiple sclerosis lesions in the posterior fossa using an optimized 3D-FLAIR sequence at 3T. AJNR Am J Neuroradiol. https://doi.org/10.3174/ajnr.A6107 CrossRefPubMed

27.

Bossuyt PM, Reitsma JB, Bruns DE et al (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 277:826–832. https://doi.org/10.1148/radiol.2015151516 CrossRefPubMed

28.

Goerner FL, Clarke GD (2011) Measuring signal-to-noise ratio in partially parallel imaging MRI. Med Phys 38:5049–5057. https://doi.org/10.1118/1.3618730 CrossRefPubMedPubMedCentral

29.

R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2019. https://www.R-project.org Accessed 11 Mar 2019

30.

Eng J (2003) Sample size estimation: how many individuals should be studied? Radiology 227:309–313. https://doi.org/10.1148/radiol.2272012051 CrossRefPubMed

31.

Gaitán MI, Yañes P, Sati P et al (2016) Optimal detection of infratentorial lesions with a combined dual-echo MRI sequence: “PT2”. Mult Scler Houndmills Basingstoke Engl 22:1367–1370. https://doi.org/10.1177/1352458515615226 CrossRef

32.

Wiggermann V, Hernández-Torres E, Traboulsee A et al (2016) FLAIR2: a combination of FLAIR and T2 for improved MS lesion detection. AJNR Am J Neuroradiol 37:259–265. https://doi.org/10.3174/ajnr.A4514 CrossRefPubMed

33.

Moraal B, Wattjes MP, Geurts JJG et al (2010) Improved detection of active multiple sclerosis lesions: 3D subtraction imaging. Radiology 255:154–163. https://doi.org/10.1148/radiol.09090814 CrossRefPubMed

34.

Rydberg JN, Riederer SJ, Rydberg CH, Jack CR (1995) Contrast optimization of fluid-attenuated inversion recovery (FLAIR) imaging. Magn Reson Med 34:868–877CrossRef

35.

Kolber P, Montag S, Fleischer V et al (2015) Identification of cortical lesions using DIR and FLAIR in early stages of multiple sclerosis. J Neurol 262:1473–1482. https://doi.org/10.1007/s00415-015-7724-5 CrossRefPubMed

36.

Filippi M, Rocca MA, Ciccarelli O et al (2016) MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol 15:292–303. https://doi.org/10.1016/S1474-4422(15)00393-2 CrossRefPubMedPubMedCentral

37.

Wattjes MP, Harzheim M, Kuhl CK et al (2006) Does high-field MR imaging have an influence on the classification of patients with clinically isolated syndromes according to current diagnostic mr imaging criteria for multiple sclerosis? AJNR Am J Neuroradiol 27:1794–1798PubMed

38.

Tur C, Thompson AJ (2015) Early accurate diagnosis crucial in multiple sclerosis. Practitioner 259(21–27):2–3

39.

Noyes K, Weinstock-Guttman B (2013) Impact of diagnosis and early treatment on the course of multiple sclerosis. Am J Manag Care 19:s321–331PubMed

40.

van Munster CEP, Jonkman LE, Weinstein HC et al (2015) Gray matter damage in multiple sclerosis: impact on clinical symptoms. Neuroscience 303:446–461. https://doi.org/10.1016/j.neuroscience.2015.07.006 CrossRefPubMed

41.

Harrison DM, Roy S, Oh J et al (2015) Association of cortical lesion burden on 7-T magnetic resonance imaging with cognition and disability in multiple sclerosis. JAMA Neurol 72:1004–1012. https://doi.org/10.1001/jamaneurol.2015.1241 CrossRefPubMedPubMedCentral

42.

Mike A, Glanz BI, Hildenbrand P et al (2011) Identification and clinical impact of multiple sclerosis cortical lesions as assessed by routine 3T MR imaging. AJNR Am J Neuroradiol 32:515–521. https://doi.org/10.3174/ajnr.A2340 CrossRefPubMed

Titel: Optimizing 3D FLAIR to detect MS lesions: pushing past factory settings for precise results
verfasst von: Augustin Lecler
C. Bouzad
R. Deschamps
F. Maizeroi
J. C. Sadik
A. Gueguen
O. Gout
H. Picard
J. Savatovsky
Publikationsdatum: 01.08.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Neurology / Ausgabe 11/2019
Print ISSN: 0340-5354
Elektronische ISSN: 1432-1459
DOI: https://doi.org/10.1007/s00415-019-09490-y

Leitlinien kompakt für die Neurologie

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Neu im Fachgebiet Neurologie

Niedriger diastolischer Blutdruck erhöht Risiko für schwere kardiovaskuläre Komplikationen

25.04.2024 Hypotonie Nachrichten

Wenn unter einer medikamentösen Hochdrucktherapie der diastolische Blutdruck in den Keller geht, steigt das Risiko für schwere kardiovaskuläre Ereignisse: Darauf deutet eine Sekundäranalyse der SPRINT-Studie hin.

Frühe Alzheimertherapie lohnt sich

25.04.2024 AAN-Jahrestagung 2024 Nachrichten

Ist die Tau-Last noch gering, scheint der Vorteil von Lecanemab besonders groß zu sein. Und beginnen Erkrankte verzögert mit der Behandlung, erreichen sie nicht mehr die kognitive Leistung wie bei einem früheren Start. Darauf deuten neue Analysen der Phase-3-Studie Clarity AD.

Viel Bewegung in der Parkinsonforschung

25.04.2024 Parkinson-Krankheit Nachrichten

Neue arznei- und zellbasierte Ansätze, Frühdiagnose mit Bewegungssensoren, Rückenmarkstimulation gegen Gehblockaden – in der Parkinsonforschung tut sich einiges. Auf dem Deutschen Parkinsonkongress ging es auch viel um technische Innovationen.

Demenzkranke durch Antipsychotika vielfach gefährdet

23.04.2024 Demenz Nachrichten

Wenn Demenzkranke aufgrund von Symptomen wie Agitation oder Aggressivität mit Antipsychotika behandelt werden, sind damit offenbar noch mehr Risiken verbunden als bislang angenommen.

Update Neurologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Abstract

Background

Methods

Results

Conclusion

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 11/2019

The clinical and radiological profile of primary lateral sclerosis: a population-based study

Biological and imaging predictors of cognitive impairment after stroke: a systematic review

Legionella infection associated with dimethyl fumarate used for treatment of multiple sclerosis

RCVS and TGA: a common pathophysiology?

Zona incerta deep-brain stimulation in orthostatic tremor: efficacy and mechanism of improvement