Automatic planning of MR-guided transcranial focused ultrasound treatment for essential tremor.

Abstract

Introduction: Transcranial focused ultrasound therapy (tcFUS) offers precise thermal ablation for treating Parkinson's disease and essential tremor. However, the manual fine-tuning of fiber tracking and segmentation required for accurate treatment planning is time-consuming and demands expert knowledge of complex neuroimaging tools. This raises the question of whether a fully automated pipeline is feasible or if manual intervention remains necessary.

Methods: We investigate the dependence on fiber tractography algorithms, segmentation approaches, and degrees of automation, specifically for essential tremor therapy planning. For that purpose, we compare an automatic pipeline with a manual approach that requires the manual definition of the target point and is based on FMRIB software library (FSL) and other open-source tools.

Results: Our findings demonstrate the high feasibility of automatic fiber tracking and the automated determination of standard treatment coordinates. Employing an automatic fiber tracking approach and deep learning (DL)-supported standard coordinate calculation, we achieve anatomically meaningful results comparable to a manually performed FSL-based pipeline. Individual cases may still exhibit variations, often stemming from differences in region of interest (ROI) segmentation. Notably, the DL-based approach outperforms registration-based methods in producing accurate segmentations. Precise ROI segmentation proves crucial, surpassing the importance of fine-tuning parameters or selecting algorithms. Correct thalamus and red nucleus segmentation play vital roles in ensuring accurate pathway computation.

Conclusion: This study highlights the potential for automation in fiber tracking algorithms for tcFUS therapy, but acknowledges the ongoing need for expert verification and integration of anatomical expertise in treatment planning.