Brain tissue segmentation based on DTI data

Abstract

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradientecho (SPGR) data are also provided.

Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering

“…We adopted R-fMRI data to construct the connectivity matrix of functional networks as follows. First, we performed brain tissue segmentation directly on DTI data [9], and used the gray matter segmentation map as a constraint for R-fMRI BOLD signal extraction. A principal component analysis was then conducted for the R-fMRI time series of all gray matter voxels within a ROI, and the first principal component was adopted as its representative R-fMRI BOLD signal.…”

Diffusion tensor imaging of deep gray matter brain structures: Effects of age and iron concentration

“…Although greater presence of free water should result in increased MD and reduced R 2 (c.f., Pfefferbaum et al, 1999), these fluid-related age effects on MD may be more reflective of spin-spin R 2 changes than are R 2 estimates from multi-echo spin-echo MRI data. Despite DTI's role in revealing age-related changes in quality of brain white matter and suggesting mechanisms of degradation, the utility of DTI metrics in furthering our understanding of age-related changes in gray matter is relatively unexplored, with a few exceptions (Huang et al, 2006;Kochunov et al, 2007;Liu et al, 2007). Theoretically, DTI could provide quantitative data about changes in magnitude and orientation of diffusivity resulting from compaction due to local tissue shrinkage or dilation of adjacent CSF-filled spaces, such as sulcal expansion on the cortex or ventricular enlargement on adjacent structures, such as the caudate or thalamus.…”

Fiber-Centered Granger Causality Analysis

“…Then fiber tracking was performed using MEDINRIA. Brain tissue segmentation was conducted on DTI data by a similar method in [8] and the cortical surface was reconstructed using the marching cubes algorithm. FMRI preprocessing steps included motion correction, spatial smoothing, temporal prewhitening, slice time correction, global drift removal, and band pass filtering.…”