Determination of Neural Fiber
Connections Based on Data
Structure Algorithm
Dilek Göksel Duru and Mehmed Özkan
e brain activity during perception or cognition is mostly examined by func-
tional magnetic resonance imaging (fMRI). However, the cause of the detect-
ed activity relies on the anatomy. Diusion tensor magnetic resonance im-
aging (DTMRI) as a noninvasive modality providing in vivo anatomical
information allows determining neural ber connections which leads to brain
mapping. Still a complete map of ber paths representing the human brain is
missing in literature. One of the main drawbacks of reliable ber mapping is
the correct detection of the orientation of multiple bers within a single im-
aging voxel. In this study a method based on linear data structures is proposed
to dene the ber paths regarding their diusivity. Another advantage of the
D  N F C 43
proposed method is that the analysis is applied on entire brain diusion ten-
sor data. e implementation results are promising, so that the method will
be developed as a rapid ber tractography algorithm for the clinical use as fu-
ture study.
Functional magnetic resonance imaging (fMRI) serves to determine the brain
activity during perception or cognition. BOLD contrast for fMRI is remarkable
in cognitive neuroscience, surgical treatment planning, and preclinical studies in
examining the main parameters such as the blood ow, blood volume, resting
state connectivity, and anatomical connectivity within the brain [1]. To dene
the cause of the detected activity, the anatomy of the underlying tissue must
be analyzed. e functional properties of the region of interests (ROIs) in the
brain can be investigated by combination of dierent modalities such as diu-
sion tensor magnetic resonance imaging (DTMRI or DTI), ADC fMRI, and
BOLD fMRI [2]. As a noninvasive imaging modality DTMRI helps identica-
tion and visualization of the ber connections in the anatomy [3–5]. DTMRI
is unique in its ability providing in-vivo anatomical information noninvasively.
e potential of DTI is to make the determination of anatomical connectivity in
the investigated brain regions by mapping the axonal pathways in white matter
noninvasively [6].
e lack of a complete neural ber map in literature makes the post processing
of the data very important. Methods and updates are to be researched to dene
the ber trajectories in the uncertainty regions where multiple ber orientations
cross within a single imaging voxel [7, 8]. Our proposed technique aims to track
the white matter bers according to data structure algorithm noniteratively and
depending on the structural information of the underlying tissue. e proposed
algorithm is based on two major processes. One is decision making and the other
one is storing process. Decision making process is basically an operation based
on comparison between the orientations of diusivities of adjacent voxel pairs.
In other words, it is the determination of the path to be traced for computing
the neural pathways. e decision making involves setting a similarity measure
having a constant scalar value for a subject. e voxels which succeeded to pass
the threshold is stored in a data structure. is process is performed for all the
adjacent voxel pairs in the examined brain MR images. So the study applies the
method to the entire human brain DT images to construct maps of neural bers
in uncertainty regions.

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