Application of Self-Organizing Artificial Neural Networks on Simulated Diffusion Tensor Images

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.