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European Radiology

, Volume 22, Issue 5, pp 998–1007 | Cite as

Novel whole brain segmentation and volume estimation using quantitative MRI

  • J. WestEmail author
  • J. B. M. Warntjes
  • P. Lundberg
Computer Applications

Abstract

Objectives

Brain segmentation and volume estimation of grey matter (GM), white matter (WM) and cerebro-spinal fluid (CSF) are important for many neurological applications. Volumetric changes are observed in multiple sclerosis (MS), Alzheimer’s disease and dementia, and in normal aging. A novel method is presented to segment brain tissue based on quantitative magnetic resonance imaging (qMRI) of the longitudinal relaxation rate R1, the transverse relaxation rate R2 and the proton density, PD.

Methods

Previously reported qMRI values for WM, GM and CSF were used to define tissues and a Bloch simulation performed to investigate R1, R2 and PD for tissue mixtures in the presence of noise. Based on the simulations a lookup grid was constructed to relate tissue partial volume to the R1–R2–PD space. The method was validated in 10 healthy subjects. MRI data were acquired using six resolutions and three geometries.

Results

Repeatability for different resolutions was 3.2% for WM, 3.2% for GM, 1.0% for CSF and 2.2% for total brain volume. Repeatability for different geometries was 8.5% for WM, 9.4% for GM, 2.4% for CSF and 2.4% for total brain volume.

Conclusion

We propose a new robust qMRI-based approach which we demonstrate in a patient with MS.

Key Points

A method for segmenting the brain and estimating tissue volume is presented

This method measures white matter, grey matter, cerebrospinal fluid and remaining tissue

The method calculates tissue fractions in voxel, thus accounting for partial volume

Repeatability was 2.2% for total brain volume with imaging resolution <2.0 mm

Keywords

Brain segmentation Tissue classification Quantitative MRI Brain volume estimation Partial volume 

Notes

Acknowledgements

We acknowledge the valuable contribution and skilful assistance of Yasemin Örter MSc, and Bo Jiang M.D., who reviewed the MS lesions detected as NoN on conventional T1- and T2-weighted images. In addition, we gratefully acknowledge the research funding and support for this project that was obtained from University Hospital Research Funds, CMIV, the Research Council of South-East Sweden (FORSS), the National Research Council (VR/NT) and the Knowledge Foundation (KK).

Supplementary material

330_2011_2336_MOESM1_ESM.doc (60 kb)
ESM 1 (DOC 60 kb)

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Copyright information

© European Society of Radiology 2011

Authors and Affiliations

  • J. West
    • 1
    • 2
    • 3
    Email author
  • J. B. M. Warntjes
    • 2
    • 3
    • 4
  • P. Lundberg
    • 2
    • 5
    • 6
  1. 1.Radiation Physics, Department of Medical and Health Sciences, Faculty of Health SciencesLinköping UniversityLinköpingSweden
  2. 2.Center for Medical Imaging Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
  3. 3.SyntheticMR ABLinköpingSweden
  4. 4.Clinical Physiology, Department of Medical and Health Sciences, Faculty of Health SciencesLinköping University and Department of Clinical Physiology UHL, County Council of ÖstergötlandLinköpingSweden
  5. 5.Radiation Physics, Department of Medical and Health Sciences, Faculty of Health SciencesLinköping University and Department of Radiation Physics UHL, County Council of ÖstergötlandLinköpingSweden
  6. 6.Radiology, Department of Medical and Health Sciences, Faculty of Health SciencesLinköping University and Department of Radiology UHL, County Council of ÖstergötlandLinköpingSweden

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