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Related Experiment Video

Updated: Oct 4, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Uncertainty quantification of TMS simulations considering MRI segmentation errors.

Hao Zhang¹, Luis Gomez², Johann Guilleminot³

¹Department of Civil and Environmental Engineering, Duke University, 121 Hudson Hall, Durham, 27708-0187, UNITED STATES.

Journal of Neural Engineering

|February 8, 2022

Summary

This summary is machine-generated.

Accurate Transcranial Magnetic Stimulation (TMS) electric field (E-field) prediction requires precise cerebrospinal fluid (CSF) segmentation. Averaging E-fields over regions, rather than point-wise estimates, improves robustness against segmentation errors.

Keywords:

TMS simulation non-Gaussian random ﬁeld patient-speciﬁc brain geometry uncertainty quantiﬁcation

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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Published on: December 18, 2016

Area of Science:

Neuroscience
Biomedical Engineering
Computational Modeling

Background:

Transcranial Magnetic Stimulation (TMS) is a key tool for brain research and therapy.
Accurate computational models of TMS electric fields (E-fields) are crucial for effective application.
Current modeling methods face challenges due to geometric errors from medical image segmentation.

Purpose of the Study:

To investigate the impact of segmentation accuracy on TMS E-field prediction fidelity.
To identify which tissue boundaries most significantly affect E-field simulation accuracy.
To explore methods for improving the robustness of TMS dosimetry.

Main Methods:

Modeled segmentation errors as geometric uncertainty in tissue boundaries.
Utilized an in-house boundary element method for forward propagation analysis.
Quantified the effect of boundary uncertainties on induced cortical E-fields.

Main Results:

E-field predictions showed low sensitivity to segmentation errors in scalp, skull, and white matter.
High sensitivity was observed for cerebrospinal fluid (CSF) segmentation errors.
CSF-gray matter interface errors impacted gyral crown E-fields; CSF-white matter errors affected sulci.
Averaging E-fields over cortical regions reduced uncertainty compared to point-wise estimates.

Conclusions:

Cerebrospinal fluid (CSF) segmentation accuracy is a primary limitation for current cortical E-field simulations.
Averaging E-fields across cortical regions offers a more robust dosimetry measure, less susceptible to segmentation inaccuracies.