{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T01:35:46Z","timestamp":1782351346413,"version":"3.54.5"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroimaging"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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)\u2013supported 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnimg.2023.1272061","type":"journal-article","created":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T12:06:05Z","timestamp":1698321965000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic planning of MR-guided transcranial focused ultrasound treatment for essential tremor"],"prefix":"10.3389","volume":"2","author":[{"given":"Jan","family":"Klein","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annika","family":"Gerken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niklas","family":"Agethen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sven","family":"Rothl\u00fcbbers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neeraj","family":"Upadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronika","family":"Purrer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten","family":"Schmeel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valeri","family":"Borger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maya","family":"Kovalevsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itay","family":"Rachmilevitch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeruham","family":"Shapira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ullrich","family":"W\u00fcllner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Jenne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.nicl.2018.01.008","article-title":"Connectivity derived thalamic segmentation in deep brain stimulation for tremor","volume":"18","author":"Akram","year":"2018","journal-title":"NeuroImage: Clinical"},{"key":"B2","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neuroimage.2006.09.018","article-title":"Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?","volume":"34","author":"Behrens","year":"2007","journal-title":"NeuroImage"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.1001\/jamaneurol.2017.3098","article-title":"Safety and efficacy of focused ultrasound thalamotomy for patients with medication-refractory, tremor-dominant parkinson disease: a randomized clinical trial","volume":"74","author":"Bond","year":"2017","journal-title":"JAMA Neurol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"4728","DOI":"10.1109\/ACCESS.2022.3141021","article-title":"Robust segmentation models using an uncertainty slice sampling-based annotation workflow","volume":"10","author":"Chlebus","year":"2022","journal-title":"IEEE Access"},{"key":"B5","first-page":"424","article-title":"\u201c3d u-net: Learning dense volumetric segmentation from sparse annotation,\u201d","volume-title":"Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"B6","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1136\/jnnp-2018-318240","article-title":"Outcomes from stereotactic surgery for essential tremor","volume":"90","author":"Dallapiazza","year":"2019","journal-title":"J. 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