AUTHOR=Klein Jan , Gerken Annika , Agethen Niklas , Rothlübbers Sven , Upadhyay Neeraj , Purrer Veronika , Schmeel Carsten , Borger Valeri , Kovalevsky Maya , Rachmilevitch Itay , Shapira Yeruham , Wüllner Ullrich , Jenne Jürgen 

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

JOURNAL=Frontiers in Neuroimaging

VOLUME=Volume 2 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2023.1272061

DOI=10.3389/fnimg.2023.1272061

ISSN=2813-1193

ABSTRACT=\textbf{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.

\textbf{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 which requires the manual definition of the target point and which is based on FSL and other open-source tools.

\textbf{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.

\textbf{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.