Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct 26:2:1272061.
doi: 10.3389/fnimg.2023.1272061. eCollection 2023.

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

Jan Klein  1 , Annika Gerken  1 , Niklas Agethen  1 , Sven Rothlübbers  1 , Neeraj Upadhyay  2 , Veronika Purrer  3 , Carsten Schmeel  4 , Valeri Borger  5 , Maya Kovalevsky  6 , Itay Rachmilevitch  6 , Yeruham Shapira  6 , Ullrich Wüllner  3 , Jürgen Jenne  1
Affiliations

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

Jan Klein et al. Front Neuroimaging. .

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.

Keywords: essential tremor; fiber tractography; segmentation; therapy planning; transcranial focused ultrasound.

PubMed Disclaimer

Conflict of interest statement

MK, IR, and YS are employed by INSIGHTEC Ltd., Tirat Carmel, Israel. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic overview of the automatic planning pipeline to compute the standard coordinate for treatment and fiber tracts. DWI, diffusion-weighted imaging; AC, anterior commissure; PC, posterior commissure; DTI, diffusion tensor imaging; ROI, region of interest.
Figure 2
Figure 2
Precise segmentation of the red nucleus constitutes the basis for tracking the correct part of cerebellothalamic tract. M, midbrain; SN, substantia nigra; CP, cerebral peduncle.
Figure 3
Figure 3
Results of tracking the pallidothalamic tract using both approaches (blue/cyan: FSL, red/yellow/green: adapted fiber tracking).
Figure 4
Figure 4
Distances (in mm) between core of fiber bundle and (a) average treatment point (plots in left column) and (b) calculated standard coordinate (plots in right column). As the CTT is the target bundle for treatment, distances are clearly smaller for this specific bundle compared to no-go bundles of the CST and the ML. CTT, cerebellothalamic tract; CST, corticospinal tract; ML, medial lemniscus; AFT, adapted fiber tracking; FSL, FMRIB software library.
Figure 5
Figure 5
Distances (in mm) between core of fiber bundle and lesion center (6 months' postoperative). CTT, cerebellothalamic tract; CST, corticospinal tract; ML, medial lemniscus; AFT, adaptive fiber tracking; FSL, FMRIB software library.
Figure 6
Figure 6
Representative fiber tracking results for the two pipelines. Green: CTT, blue: ML, red: CST. Upper row: FSL, bottom row: AFT. Treatment position and standard coordinate are marked by a red circle. The visualized thalamus and the red nuclei are identical in both rows for a better comparison of the fiber tracts; however, ROIs for fiber tracking have been differently determined for the manual and automatic planning pipeline as described in Sections 2.4.1 and 2.5.5. This explains, for example, the fact that only a part of the CTT (tracked by FSL) passes the red nucleus. CTT, cerebellothalamic tract; ML, medial lemniscus; CST, corticospinal tract; FSL, FMRIB software library; AFT, adapted fiber tracking; ROI, region of interest.
Figure 7
Figure 7
Three-dimensional visualization corresponding to Figure 6. Green: CTT, blue: ML, red: CST. Upper row: FSL, bottom row: AFT. The calculated standard coordinate is shown as a bright sphere. The DL-based segmented thalamus is identical in both rows for a better comparison of the fiber tracts. CTT, cerebellothalamic tract; ML, medial lemniscus; CST corticospinal tract; FSL, FMRIB software library; AFT, adapted fiber tracking; DL, deep learning.
See this image and copyright information in PMC

References

    1. Akram H., Dayal V., Mahlknecht P., Georgiev D., Hyam J., Foltynie T., et al. . (2018). Connectivity derived thalamic segmentation in deep brain stimulation for tremor. NeuroImage: Clinical 18:130–142. 10.1016/j.nicl.2018.01.008 - DOI - PMC - PubMed
    1. Behrens T., Berg H. J., Jbabdi S., Rushworth M., Woolrich M. (2007). Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34(1):144–155. 10.1016/j.neuroimage.2006.09.018 - DOI - PMC - PubMed
    1. Bond A. E., Shah B. B., Huss D. S., Dallapiazza R. F., Warren A., Harrison M. B., et al. . (2017). Safety and efficacy of focused ultrasound thalamotomy for patients with medication-refractory, tremor-dominant parkinson disease: a randomized clinical trial. JAMA Neurol. 74, 1412–1418. 10.1001/jamaneurol.2017.3098 - DOI - PMC - PubMed
    1. Chlebus G., Schenk A., Hahn H. K., Van Ginneken B., Meine H. (2022). Robust segmentation models using an uncertainty slice sampling-based annotation workflow. IEEE Access 10, 4728–4738. 10.1109/ACCESS.2022.3141021 - DOI
    1. Çiçek Ö., Abdulkadir A., Lienkamp S. S., Brox T., Ronneberger O. (2016). “3d u-net: Learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016, eds. S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells. Cham. Springer International Publishing, 424–432.

LinkOut - more resources