We present DeepTract, a deep-learning framework for estimation of white matter fibers orientation and streamline tractography. We take a data-driven approach for fiber reconstruction from raw diffusion MRI, without assuming a specific diffusion model. We use a recurrent neural network for mapping sequences of diffusion-weighted imaging (DWI ) values into probabilistic fiber orientation distributions. Based on these estimations, our model can perform both deterministic and probabilistic tractography on unseen DWI datasets . We quantitatively evaluate our method using the Tractometer tool, demonstrating comparable performance to state-of-the-art classical and DL-based methods . We further present qualitative results of bundle-specific probabilistic tractography of our method.
We present DeepTract, a deep-learning framework for estimation of white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion-weighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping sequences of DWI values into probabilistic fiber orientation distributions. Based on these estimations, our model facilitates both deterministic and probabilistic streamline tractography . We quantitatively evaluate our method using the Tractometer tool, demonstrating comparable performance to state-of-the-art classical and machine learning based tractography algorithms . We further present qualitative results of bundle-specific probabilistic tractography obtained using our method.
R
take
adopt
R
raw diffusion MRI, without assuming
diffusion-weighted images (DWI), which does not assume
R
diffusion-weighted imaging (DWI )
DWI
R
can perform
facilitates
R
tractography on unseen DWI datasets
streamline tractography
R
DL-based methods
machine learning based tractography algorithms
R
of
obtained using