Novel whole brain segmentation and volume estimation using quantitative MRI
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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.
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.
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.
We propose a new robust qMRI-based approach which we demonstrate in a patient with MS.
• 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
KeywordsBrain segmentation Tissue classification Quantitative MRI Brain volume estimation Partial volume
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).