Comput Math Methods Med - Crossing fibers detection with an analytical high order tensor decomposition.

Tópicos

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Resumo

Diffusion magnetic resonance imaging (dMRI) is the only technique to probe in vivo and noninvasively the fiber structure of human brain white matter. Detecting the crossing of neuronal fibers remains an exciting challenge with an important impact in tractography. In this work, we tackle this challenging problem and propose an original and efficient technique to extract all crossing fibers from diffusion signals. To this end, we start by estimating, from the dMRI signal, the so-called Cartesian tensor fiber orientation distribution (CT-FOD) function, whose maxima correspond exactly to the orientations of the fibers. The fourth order symmetric positive definite tensor that represents the CT-FOD is then analytically decomposed via the application of a new theoretical approach and this decomposition is used to accurately extract all the fibers orientations. Our proposed high order tensor decomposition based approach is minimal and allows recovering the whole crossing fibers without any a priori information on the total number of fibers. Various experiments performed on noisy synthetic data, on phantom diffusion, data and on human brain data validate our approach and clearly demonstrate that it is efficient, robust to noise and performs favorably in terms of angular resolution and accuracy when compared to some classical and state-of-the-art approaches.

Resumo Limpo

diffus magnet reson imag dmri techniqu probe vivo noninvas fiber structur human brain white matter detect cross neuron fiber remain excit challeng import impact tractographi work tackl challeng problem propos origin effici techniqu extract cross fiber diffus signal end start estim dmri signal socal cartesian tensor fiber orient distribut ctfod function whose maxima correspond exact orient fiber fourth order symmetr posit definit tensor repres ctfod analyt decompos via applic new theoret approach decomposit use accur extract fiber orient propos high order tensor decomposit base approach minim allow recov whole cross fiber without priori inform total number fiber various experi perform noisi synthet data phantom diffus data human brain data valid approach clear demonstr effici robust nois perform favor term angular resolut accuraci compar classic stateoftheart approach

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