HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
This addresses the need for reliable tractography in neuroimaging, offering an incremental improvement over atlas-based methods by using rotation-covariant filters without data augmentation.
The authors tackled the problem of automatically learning fiber tracts from diffusion MRI data by proposing HAMLET, a novel algorithm that maps raw data directly to images indicating tract direction and presence, demonstrating robust performance for twelve bundles and portability across sequences.
In this work we propose HAMLET, a novel tract learning algorithm, which, after training, maps raw diffusion weighted MRI directly onto an image which simultaneously indicates tract direction and tract presence. The automatic learning of fiber tracts based on diffusion MRI data is a rather new idea, which tries to overcome limitations of atlas-based techniques. HAMLET takes a such an approach. Unlike the current trend in machine learning, HAMLET has only a small number of free parameters HAMLET is based on spherical tensor algebra which allows a translation and rotation covariant treatment of the problem. HAMLET is based on a repeated application of convolutions and non-linearities, which all respect the rotation covariance. The intrinsic treatment of such basic image transformations in HAMLET allows the training and generalization of the algorithm without any additional data augmentation. We demonstrate the performance of our approach for twelve prominent bundles, and show that the obtained tract estimates are robust and reliable. It is also shown that the learned models are portable from one sequence to another.