Deep learning-assisted fully automatic fiber tracking for tremor treatment

Transcranial therapy with focused ultrasound under the control of magnetic resonance imaging (tcMRgFUS) enables targeted thermal ablation of brain tissue, for example for movement disorders such as tremor in Parkinson's disease. Fiber tracking can serve as a tool to delineate therapy-relevant pathways in the brain to determine the ablation target and to avoid damage to neighboring structures. We apply a fiber tracking algorithm which relies on the definition of regions of interest (ROIs) used as seed points or waypoints to optimize the tracking precision. It adapts its parameters locally to the current location of the fiber to be reconstructed within a white matter atlas. We propose to fully automate the fiber tracking pipeline using a deep learning-based segmentation of 20 ROIs, trained on T1 images and color-coded direction maps from diffusion tensor imaging (DTI). The training reference data is generated automatically using ROIs registered from an atlas. The resulting U-Nets can segment all required ROIs, also on independent test data, and are more robust than atlas registration for selected ROIs, i.e. the precentral gyrus. The fiber tracts computed using ROIs from DL segmentation versus atlas registration are similar. We expect to further improve the patient-individual ROIs and resulting fiber tracts by using curated ground truth reference data for future trainings.