Advertisement

Comparison of whole brain segmentation and volume estimation in children and young adults using SPM and SyMRI

  • Suraj D. Serai
    Correspondence
    Corresponding author at: Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, United States of America.
    Affiliations
    Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States of America

    Department of Radiology, Cincinnati Children's Hospital and Medical Center, Cincinnati, OH, United States of America
    Search for articles by this author
  • Jonathan Dudley
    Affiliations
    Department of Radiology, Cincinnati Children's Hospital and Medical Center, Cincinnati, OH, United States of America
    Search for articles by this author
  • James L. Leach
    Affiliations
    Department of Radiology, Cincinnati Children's Hospital and Medical Center, Cincinnati, OH, United States of America
    Search for articles by this author

      Abstract

      Background

      MRI brain segmentation and volume estimation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) are important for many neurological applications. Signal intensity based measurements, such as the current statistical parametric mapping (SPM) based volume estimation techniques rely on T1W images that involve a series of pre-processing steps, making it impractical for clinical use.

      Purpose

      In this study, we compared Synthetic MRI (SyMRI) generated relaxometry maps based brain segmentation and estimation of brain volumes with SPM image intensity based segmentation and volume estimation in children and young adults.

      Subjects

      176 studies were included for analysis with mean age of 10.9 ± 5.5 years.

      Methods

      Included studies were quantitatively analyzed and segmented using SyMRI® software. In SPM, the segmentation routine segments brain T1W images into GM, WM, and CSF based on image intensity values. SPM and SyMRI segmentation based volume estimates were plotted. Scatter plots comparing the two methods were generated and agreement was assessed using correlation coefficients.

      Results

      Correlation coefficient, r, of agreement between the 2 methods was 0.85 for GM, 0.91 for WM, and 0.38 for CSF (P < 0.0001 for all three volumes).

      Conclusion

      Brain imaging in children using SyMRI can identify and calculate estimates of GM, WM, CSF volumes. With our work, we have shown high similarity of volume estimates in GM and WM using SyMRI with a systematic bias for CSF values. The ease of use of this software can make this quantitative data to be used clinically along with the routine anatomical images.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Clinical Imaging
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Piper R.J.
        • et al.
        Estimating intracranial volume using intracranial area in healthy children and those with childhood status epilepticus.
        Brain Behav. 2014; 4: 936-942
        • Woodward L.J.
        • et al.
        Neonatal white matter abnormalities an important predictor of neurocognitive outcome for very preterm children.
        PLoS One. 2012; 7e51879
        • McAllister A.
        • et al.
        Quantitative synthetic MRI in children: normative intracranial tissue segmentation values during development.
        AJNR Am J Neuroradiol. 2017; 38: 2364-2372
        • Giorgio A.
        • De Stefano N.
        Clinical use of brain volumetry.
        J Magn Reson Imaging. 2013; 37: 1-14
        • Chard D.T.
        • et al.
        The reproducibility and sensitivity of brain tissue volume measurements derived from an SPM-based segmentation methodology.
        J Magn Reson Imaging. 2002; 15: 259-267
        • Cabezas M.
        • et al.
        A review of atlas-based segmentation for magnetic resonance brain images.
        Comput Methods Programs Biomed. 2011; 104: e158-e177
        • Fonov V.
        • et al.
        Unbiased average age-appropriate atlases for pediatric studies.
        Neuroimage. 2011; 54: 313-327
        • Sanchez C.E.
        • Richards J.E.
        • Almli C.R.
        Age-specific MRI templates for pediatric neuroimaging.
        Dev Neuropsychol. 2012; 37: 379-399
        • Artaechevarria X.
        • Munoz-Barrutia A.
        • Ortiz-de-Solorzano C.
        Combination strategies in multi-atlas image segmentation: application to brain MR data.
        IEEE Trans Med Imaging. 2009; 28: 1266-1277
        • Ashburner J.
        SPM: a history.
        Neuroimage. 2012; 62: 791-800
        • Warntjes J.B.
        • Dahlqvist O.
        • Lundberg P.
        Novel method for rapid, simultaneous T1, T2*, and proton density quantification.
        Magn Reson Med. 2007; 57: 528-537
        • Warntjes J.B.
        • et al.
        Brain characterization using normalized quantitative magnetic resonance imaging.
        PLoS One. 2013; 8e70864
        • Betts A.M.
        • et al.
        Brain imaging with synthetic MR in children: clinical quality assessment.
        Neuroradiology. 2016; 58: 1017-1026
        • West H.
        • et al.
        Clinical validation of synthetic brain MRI in children: initial experience.
        Neuroradiology. 2017; 59: 43-50
        • West J.
        • et al.
        Application of quantitative MRI for brain tissue segmentation at 1.5 T and 3.0 T field strengths.
        PLoS One. 2013; 8: e74795
        • Goncalves F.G.
        • Serai S.D.
        • Zuccoli G.
        Synthetic brain MRI: review of current concepts and future directions.
        Top Magn Reson Imaging. 2018; 27: 387-393
        • Altaye M.
        • et al.
        Infant brain probability templates for MRI segmentation and normalization.
        Neuroimage. 2008; 43: 721-730
        • Ashburner J.
        • Friston K.J.
        Unified segmentation.
        Neuroimage. 2005; 26: 839-851
        • Kazemi K.
        • Noorizadeh N.
        Quantitative comparison of SPM, FSL, and brainsuite for brain MR image segmentation.
        J Biomed Phys Eng. 2014; 4: 13-26
        • Wilke M.
        • Schmithorst V.J.
        • Holland S.K.
        Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data.
        Magn Reson Med. 2003; 50: 749-757
        • Mazaika P.K.
        • et al.
        Variations in brain volume and growth in young children with type 1 diabetes.
        Diabetes. 2016; 65: 476-485
        • Despotovic I.
        • Goossens B.
        • Philips W.
        MRI segmentation of the human brain: challenges, methods, and applications.
        Comput Math Methods Med. 2015; 2015450341
        • Wilke M.
        • et al.
        Multidimensional morphometric 3D MRI analyses for detecting brain abnormalities in children: impact of control population.
        Hum Brain Mapp. 2014; 35: 3199-3215
        • Tohka J.
        Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: a review.
        World J Radiol. 2014; 6: 855-864
        • Granberg T.
        • et al.
        Clinical feasibility of synthetic MRI in multiple sclerosis: a diagnostic and volumetric validation study.
        AJNR Am J Neuroradiol. 2016; 37: 1023-1029