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In this work we propose HAMLET, a novel tract learning algorithm, which,after training, maps raw diffusion weighted MRI directly onto an image whichsimultaneously indicates tract direction and tract presence. The automaticlearning of fiber tracts based on diffusion MRI data is a rather new idea,which tries to overcome limitations of atlas-based techniques. HAMLET takes asuch an approach. Unlike the current trend in machine learning, HAMLET has onlya small number of free parameters HAMLET is based on spherical tensor algebrawhich 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 suchbasic image transformations in HAMLET allows the training and generalization ofthe algorithm without any additional data augmentation. We demonstrate theperformance of our approach for twelve prominent bundles, and show that theobtained tract estimates are robust and reliable. It is also shown that thelearned models are portable from one sequence to another.

HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI