Abstract
Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determination of fiber connections which leads to brain mapping. The success of DTMRI is very much algorithm dependent, and its verification is of great importance due to limited availability of a gold standard in the literature. In this study, unsupervised artificial neural network class, namely, self-organizing maps, is employed to discover the underlying fiber tracts. A common artificial diffusion tensor resource, named “phantom images for simulating tractography errors” (PISTE), is used for the accuracy verification and acceptability of the proposed approach. Four different tract geometries with varying SNRs and fractional anisotropy are investigated. The proposed method, SOFMAT, is able to define the predetermined fiber paths successfully with a standard deviation of (0.8–1.9) × 10−3depending on the trajectory and the SNR value selected. The results illustrate the capability of SOFMAT to reconstruct complex fiber tract configurations. The ability of SOFMAT to detect fiber paths in low anisotropy regions, which physiologically may correspond to either grey matter or pathology (abnormality) and uncertainty areas in real data, is an advantage of the method for future studies.
Highlights
Diffusion tensor magnetic resonance imaging (DTMRI, called DTI) is a fundamental technique that allows in vivo structural brain imaging by white matter estimation [1, 2]
The results illustrate the capability of self-organizing feature mapping tractography (SOFMAT) to reconstruct complex fiber tract configurations
The fractional anisotropy (FA) maps serve as filters where the routinely applied homogeneous anisotropic background can be extracted from the image
Summary
Diffusion tensor magnetic resonance imaging (DTMRI, called DTI) is a fundamental technique that allows in vivo structural brain imaging by white matter estimation [1, 2]. Differing from the weighted MR images, DTI provides directional information that could be used to compute nerve pathways. The accurate estimation of white matter fibers is highly dependent on the tractography algorithm used. DTI is advantageous in clinical neuroscience, for quantitative comparison of specific white matter pathways in disease, in guided interventions, for the exploration of the normal brain anatomy [3]. The diffusion tensor estimation can be obtained by taking the arithmetic average of the diffusion images in all possible directions [1]. The 3 × 3 symmetric diffusion tensor D is calculated from a set of these diffusion weighted images for each pixel as in the following [1].
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