FLAIR*: A Combined MR Contrast Technique for Visualizing White Matter Lesions and Parenchymal Veins

Published Online:https://doi.org/10.1148/radiol.12120208

FLAIR* is an MR imaging contrast technique that combines the advantages of cerebrospinal fluid–suppressed T2-weighted imaging and T2*-weighted imaging, yielding images that provide high contrast for white matter (WM) lesions and veins in the brain and that are well suited to routine imaging of WM diseases, such as multiple sclerosis, in a clinical setting.


To evaluate a magnetic resonance (MR) imaging contrast technique, called FLAIR*, that combines the advantages of T2-weighted fluid-attenuated inversion recovery (FLAIR) contrast and T2*-weighted contrast on a single image for assessment of white matter (WM) diseases such as multiple sclerosis (MS).

Materials and Methods

This prospective pilot study was HIPAA compliant and institutional review board approved. Ten patients with clinically definite MS (eight men, two women; mean age, 41 years) provided informed consent and underwent 3.0-T MR imaging. Images from a T2-weighted FLAIR sequence were combined with images from a T2*-weighted segmented echo-planar imaging sequence performed during contrast material injection, yielding high-isotropic-resolution (0.55 × 0.55 × 0.55 mm3) FLAIR* images. Qualitative assessment was performed for image quality, lesion conspicuity, and vein conspicuity. Contrast-to-noise ratio (CNR) was calculated to compare normal-appearing WM (NAWM) with cerebrospinal fluid, lesions, and veins. To evaluate the differences in CNR among imaging modalities, a bootstrap procedure clustered on subjects was used, together with paired t tests.


High-quality FLAIR* images of the brain were produced at 3.0 T, yielding conspicuous lesions and veins. Lesion-to-NAWM and NAWM-to-vein CNR values were significantly higher for FLAIR* images than for T2-weighted FLAIR images (P < .0001). Findings on FLAIR* images included intralesional veins for lesions located throughout the brain and a hypointense rim around some WM lesions.


High-isotropic-resolution FLAIR* images obtained at 3.0 T yield high contrast for WM lesions and parenchymal veins, making it well suited to investigate the relationship between WM abnormalities and veins in a clinical setting.

© RSNA, 2012


  • 1 Barkhof F, Scheltens P. Imaging of white matter lesions. Cerebrovasc Dis 2002;13(Suppl 2):21–30. Crossref, MedlineGoogle Scholar
  • 2 Simon JH, Li D, Traboulsee A, et al.. Standardized MR imaging protocol for multiple sclerosis: Consortium of MS Centers consensus guidelines. AJNR Am J Neuroradiol 2006;27(2):455–461. MedlineGoogle Scholar
  • 3 Filippi M, Rocca MA, De Stefano N, et al.. Magnetic resonance techniques in multiple sclerosis: the present and the future. Arch Neurol 2011;68(12):1514–1520. Crossref, MedlineGoogle Scholar
  • 4 Reichenbach JR, Venkatesan R, Schillinger DJ, Kido DK, Haacke EM. Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology 1997;204(1):272–277. LinkGoogle Scholar
  • 5 Haacke EM, Mittal S, Wu Z, Neelavalli J, Cheng YC. Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. AJNR Am J Neuroradiol 2009;30(1):19–30. Crossref, MedlineGoogle Scholar
  • 6 Scharf J, Bräuherr E, Forsting M, Sartor K. Significance of haemorrhagic lacunes on MRI in patients with hypertensive cerebrovascular disease and intracerebral haemorrhage. Neuroradiology 1994;36(7):504–508. Crossref, MedlineGoogle Scholar
  • 7 Wu Z, Mittal S, Kish K, Yu Y, Hu J, Haacke EM. Identification of calcification with MRI using susceptibility-weighted imaging: a case study. J Magn Reson Imaging 2009;29(1):177–182. Crossref, MedlineGoogle Scholar
  • 8 Haacke EM, Cheng NY, House MJ, et al.. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging 2005;23(1):1–25. Crossref, MedlineGoogle Scholar
  • 9 Pitt D, Boster A, Pei W, et al.. Imaging cortical lesions in multiple sclerosis with ultra-high-field magnetic resonance imaging. Arch Neurol 2010;67(7):812–818. Crossref, MedlineGoogle Scholar
  • 10 Hammond KE, Metcalf M, Carvajal L, et al.. Quantitative in vivo magnetic resonance imaging of multiple sclerosis at 7 Tesla with sensitivity to iron. Ann Neurol 2008;64(6):707–713. Crossref, MedlineGoogle Scholar
  • 11 Yao B, Bagnato F, Matsuura E, et al.. Chronic multiple sclerosis lesions: characterization with high-field-strength MR imaging. Radiology 2012;262(1):206–215. LinkGoogle Scholar
  • 12 Zwanenburg JJ, Versluis MJ, Luijten PR, Petridou N. Fast high resolution whole brain T2* weighted imaging using echo planar imaging at 7T. Neuroimage 2011;56(4):1902–1907. Crossref, MedlineGoogle Scholar
  • 13 Sati P, Thomasson D, Biassou N, Reich DS, Butman JA. Ultra-fast acquisition of high-resolution susceptibility-weighted-imaging at 3T [abstr]. In: Proceedings of the Nineteenth Meeting of the International Society for Magnetic Resonance in Medicine. Berkeley, Calif: International Society for Magnetic Resonance in Medicine, 2011. Google Scholar
  • 14 Polman CH, Reingold SC, Banwell B, et al.. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69(2):292–302. Crossref, MedlineGoogle Scholar
  • 15 Kurtzke JF. A new scale for evaluating disability in multiple sclerosis. Neurology 1955;5(8):580–583. Crossref, MedlineGoogle Scholar
  • 16 Lucas BC, Bogovic JA, Carass A, et al.. The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software. Neuroinformatics 2010;8(1):5–17. Crossref, MedlineGoogle Scholar
  • 17 Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;17(1):87–97. Crossref, MedlineGoogle Scholar
  • 18 Carass A, Cuzzocreo J, Wheeler MB, Bazin PL, Resnick SM, Prince JL. Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. Neuroimage 2011;56(4):1982–1992. Crossref, MedlineGoogle Scholar
  • 19 Shiee N, Bazin PL, Ozturk A, Reich DS, Calabresi PA, Pham DL. A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 2010;49(2):1524–1535. Crossref, MedlineGoogle Scholar
  • 20 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33(1):159–174. Crossref, MedlineGoogle Scholar
  • 21 Grabner G, Dal-Bianco A, Schernthaner M, Vass K, Lassmann H, Trattnig S. Analysis of multiple sclerosis lesions using a fusion of 3.0 T FLAIR and 7.0 T SWI phase: FLAIR SWI. J Magn Reson Imaging 2011;33(3):543–549. Crossref, MedlineGoogle Scholar
  • 22 Tan IL, van Schijndel RA, Pouwels PJ, et al.. MR venography of multiple sclerosis. AJNR Am J Neuroradiol 2000;21(6):1039–1042. MedlineGoogle Scholar
  • 23 Tallantyre EC, Brookes MJ, Dixon JE, Morgan PS, Evangelou N, Morris PG. Demonstrating the perivascular distribution of MS lesions in vivo with 7-Tesla MRI. Neurology 2008;70(22):2076–2078. Crossref, MedlineGoogle Scholar
  • 24 Tallantyre EC, Morgan PS, Dixon JE, et al.. A comparison of 3T and 7T in the detection of small parenchymal veins within MS lesions. Invest Radiol 2009;44(9):491–494. Crossref, MedlineGoogle Scholar
  • 25 Volz S, Hattingen E, Preibisch C, Gasser T, Deichmann R. Reduction of susceptibility-induced signal losses in multi-gradient-echo images: application to improved visualization of the subthalamic nucleus. Neuroimage 2009;45(4):1135–1143. Crossref, MedlineGoogle Scholar

Article History

Received January 25, 2012; revision requested March 1; revision received April 5; accepted May 11; final version accepted May 22.
Published online: Dec 2012
Published in print: Dec 2012