  Susceptibility tensor imaging (STI) is an emerging magnetic resonance imaging
technique that characterizes the anisotropic tissue magnetic susceptibility
with a second-order tensor model. STI has the potential to provide information
for both the reconstruction of white matter fiber pathways and detection of
myelin changes in the brain at mm resolution or less, which would be of great
value for understanding brain structure and function in healthy and diseased
brain. However, the application of STI in vivo has been hindered by its
cumbersome and time-consuming acquisition requirement of measuring
susceptibility induced MR phase changes at multiple (usually more than six)
head orientations. This complexity is enhanced by the limitation in head
rotation angles due to physical constraints of the head coil. As a result, STI
has not yet been widely applied in human studies in vivo. In this work, we
tackle these issues by proposing an image reconstruction algorithm for STI that
leverages data-driven priors. Our method, called DeepSTI, learns the data prior
implicitly via a deep neural network that approximates the proximal operator of
a regularizer function for STI. The dipole inversion problem is then solved
iteratively using the learned proximal network. Experimental results using both
simulation and in vivo human data demonstrate great improvement over
state-of-the-art algorithms in terms of the reconstructed tensor image,
principal eigenvector maps and tractography results, while allowing for tensor
reconstruction with MR phase measured at much less than six different
orientations. Notably, promising reconstruction results are achieved by our
method from only one orientation in human in vivo, and we demonstrate a
potential application of this technique for estimating lesion susceptibility
anisotropy in patients with multiple sclerosis.
