Multi-scale anatomical awareness improves the accuracy of the real-time electric field estimation

Ma, Liang; Zhong, Gangliang; Yang, Zhengyi; Fan, Linzhong; Jiang, Tianzi

doi:10.1109/ijcnn52387.2021.9533894

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…Recent research indicates that deep learning methods have the potential to simulate the E-field statistically [26][27][28][29]. Using individual MRI as input, deep neural networks (DNNs) can produce corresponding E-field distributions in real time, leveraging their efficient extraction of implicit tissue features and sidestepping the dimensional curse encountered in earlier machine learning methods [30,31].…”

Section: Introductionmentioning

confidence: 99%

“…However, the implicit tissue features (as shown in figure 1(a)) extracted by DNNs might result in region-dependent performance when these networks are trained only on specific brain regions (e.g. the motor or Broca's areas) [26,27]. Furthermore, directly mapping Efields from single-site MRIs can make DNN inferences more vulnerable to the acquisition conditions of the input MRI images [31,32].…”

Section: Introductionmentioning

confidence: 99%

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In-vivo verified anatomically aware deep learning for real-time electric field simulation

Ma,

Zhong,

Yang

et al. 2023

J. Neural Eng.

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Objective. Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement. Approach. We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset. In-vivo TMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes. Significance. The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-vivo verified anatomically aware deep learning for real-time electric field simulation

Ma,

Zhong,

Yang

et al. 2023

J. Neural Eng.

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“…Several methods that use deep neural networks (DNN) for E-eld regression have been proposed in recent years [20][21][22][23][24]. Supervised learning [25] was used to construct regressors for the E-eld.…”

Section: Introductionmentioning

confidence: 99%

“…The outputs contained estimation errors that obeyed the probability distributions. Many DNN-based regressors do not evaluate uncertain-ty [20][21][22][23][24]. Once the uncertainty of the regressed E-elds is evaluated, the reliability of the clinical applications using E-eld regression can be improved.…”

Section: Introductionmentioning

confidence: 99%

Electric Field Regression and Error Variance Estimation for Transcranial Magnetic Stimulation using Deep Neural Networks

Maki,

Yokota,

Hirata

et al. 2023

ABE

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Transcranial magnetic stimulation (TMS) is a widely used non-invasive neurostimulation technique in neuroscience and in the treatment of psychiatric disorders. By placing a TMS coil over a patientʼs head, neurons in the brain can be electromagnetically stimulated through the induction of an electric eld (E-eld). Accurate estimation of the E-eld induced in a patientʼs head is crucial for determining the stimulated area of the brain. The electromagnetic simulation for E-eld estimation involves two processes: the development of a volume conductor model (VCM) to determine the electrical conductivity at each position of the brain from a head magnetic resonance (MR) image, and the computation of the E-eld on the VCM. Currently, neither of these processes can be performed in real-time. Achieving real-time estimation would greatly assist in determining the appropriate coil position and direction to stimulate the target regions in the patientʼs brain. In recent years, several methods utilizing deep neural networks (DNNs) have been proposed to estimate E-elds from MR images in real-time. These methods construct a regressor of the E-eld using a set of simulated E-elds as training data to estimate the E-eld. However, the reliability of these regressors in clinical applications could be improved by incorporating uncertainty estimation of the regressed variables, although this has not been reported. In this study, we enhanced the accuracy of E-eld strength estimation by rst regressing the E-eld and then computing the norm of the E-eld vectors, instead of directly regressing the E-eld strength. In addition, we investigated the statistical uncertainty of the regressed E-elds using DNN. It should be noted that the E-elds estimated by the regressors are random variables. To evaluate the uncertainty of this application, we employed MCDropout, a well-known Bayesian estimation method. The uncertainty of the regressed E-eld was evaluated for each anatomical tissue of the brain, to examine the relationship between uncertainty and depth from the coil. The experimental results of this evaluation are presented quantitatively.

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DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation

Jia

Sayed

Hasan³

et al. 2023

IEEE Trans. Biomed. Eng.

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Multi-scale anatomical awareness improves the accuracy of the real-time electric field estimation

Cited by 3 publications

References 23 publications

In-vivo verified anatomically aware deep learning for real-time electric field simulation

In-vivo verified anatomically aware deep learning for real-time electric field simulation

Electric Field Regression and Error Variance Estimation for Transcranial Magnetic Stimulation using Deep Neural Networks

DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation

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