A Hebbian Learning Approach for Diffusion Tensor Analysis and Tractography

The principle significance of an artificial neural network is that it learns and improves through that learning. The definition of the learning process in neural networks is of great importance. The neural network is stimulated and regarding to these stimulations the free parameters of the network change in its internal structure. As a result the neural network replies in a new way. Based on a basic learning algorithm namely Hebbian learning, a solution to the problem of resolving uncertainty areas in diffusion tensor magnetic resonance image (DTMRI) analysis is represented. Diffusion tensor imaging (DTI) is a developing and promising medical imaging modality allowing the determination of in-vivo tissue properties noninvasively upon the random movement of the water molecules. The method is unique in its ability being a noninvasive modality which is a great opportunity to explore various white matter pathologies and healthy brain mapping for neuroanatomy research. In neuroscience applications DTI is mostly used addressing brain’s fiber tractography, reconstructing the connectivity map. Clinical evaluation of fiber tracking results is a major problem in the field. Noise, partial volume effects, inefficiency of numerical implementations by reconstructing the intersecting tracts are some of the reasons for the need of standardized fiber tract atlas. Also misregistration caused by eddy currents, ghosting due to motion artifacts, and signal loss due to susceptibility variations may all affect the calculated tractography results. The proposed method based on the Hebbian learning provides an instance of nonsupervised and competitive learning in a neurobiological aspect as a solution to the tracking problem of the intersecting axonal structures. The main contribution of the study is to describe a tracking approach via a special class of artificial neural networks namely the Hebbian learning with improved reliability.