Abstract
An effective technique for investigating human brain connectivities, is the reconstruction of fiber orientation distribution functions based on diffusion-weighted MRI. To reconstruct fiber orientations, most current approaches fit a simplified diffusion model, resulting in an approximation error. We present a novel approach for estimating the fiber orientation directly from raw data, by converting the model fitting process into a classification problem based on a convolutional Deep Neural Network, which is able to identify correlated diffusion information within a single voxel. Wevaluate our approach quantitatively on realistic synthetic data as well as on real data and achieve reliable results compared to a state-of-the-art method. This approach is even capable to relieable reconstruct three fiber crossing utilizing only 10 gradient acquisitions.
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Koppers, S., Merhof, D. (2016). Direct Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_7
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DOI: https://doi.org/10.1007/978-3-319-47157-0_7
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