Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES

In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. However, the segmentation of different tissues is a tedious task with several associated challenges specially with tissues appear in limited regions and/or low-contrast in anatomical images. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results.

[1]  Wilfried Philips,et al.  MRI Segmentation of the Human Brain: Challenges, Methods, and Applications , 2015, Comput. Math. Methods Medicine.

[2]  R. W. Lau,et al.  The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. , 1996, Physics in medicine and biology.

[3]  Axel Thielscher,et al.  Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS? , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Nassir Navab,et al.  Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control , 2018, NeuroImage.

[5]  Julie M. Baker,et al.  Individualized model predicts brain current flow during transcranial direct-current stimulation treatment in responsive stroke patient , 2011, Brain Stimulation.

[6]  Ilkka Laakso,et al.  Review on biophysical modelling and simulation studies for transcranial magnetic stimulation , 2020, Physics in medicine and biology.

[7]  Yu Huang,et al.  Automated MRI segmentation for individualized modeling of current flow in the human head , 2013, Journal of neural engineering.

[8]  David N. Kennedy,et al.  MRI-based anatomical model of the human head for specific absorption rate mapping , 2008, Medical & Biological Engineering & Computing.

[9]  M. Bikson,et al.  Computational Models of Transcranial Direct Current Stimulation , 2012, Clinical EEG and neuroscience.

[10]  Essam A. Rashed,et al.  Development of accurate human head models for personalized electromagnetic dosimetry using deep learning , 2019, NeuroImage.

[11]  Justin A. Harris,et al.  Neuroscience and Biobehavioral Reviews Modelling Non-invasive Brain Stimulation in Cognitive Neuroscience , 2022 .

[12]  J. Camprodon,et al.  Impact of non-brain anatomy and coil orientation on inter- and intra-subject variability in TMS at midline , 2018, Clinical Neurophysiology.

[13]  Alexander Opitz,et al.  How the brain tissue shapes the electric field induced by transcranial magnetic stimulation , 2011, NeuroImage.

[14]  Fumiko Hoeft,et al.  Noninvasive transcranial brain stimulation and pain , 2009, Current pain and headache reports.

[15]  Yu Huang,et al.  Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully automated open-source pipeline , 2019, Journal of neural engineering.

[16]  Essam A. Rashed,et al.  End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation , 2020, Neural Networks.

[17]  Akimasa Hirata,et al.  Synopsis of IEEE Std C95.1™-2019 “IEEE Standard for Safety Levels With Respect to Human Exposure to Electric, Magnetic, and Electromagnetic Fields, 0 Hz to 300 GHz” , 2019, IEEE Access.

[18]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[19]  D. B. Heppner,et al.  Considerations of quasi-stationarity in electrophysiological systems. , 1967, The Bulletin of mathematical biophysics.

[20]  T. Weiland,et al.  Impact of the displacement current on low-frequency electromagnetic fields computed using high-resolution anatomy models , 2005, Physics in medicine and biology.

[21]  Olivier A. Coubard,et al.  Transcranial magnetic stimulation in basic and clinical neuroscience: A comprehensive review of fundamental principles and novel insights , 2017, Neuroscience & Biobehavioral Reviews.

[22]  B. Fischl,et al.  FastSurfer - A fast and accurate deep learning based neuroimaging pipeline , 2019, NeuroImage.

[23]  D F Stegeman,et al.  The influence of sulcus width on simulated electric fields induced by transcranial magnetic stimulation , 2013, Physics in medicine and biology.

[24]  Terry M. Peters,et al.  The semiotics of medical image Segmentation☆ , 2018, Medical Image Anal..

[25]  Alexander Opitz,et al.  Impact of the gyral geometry on the electric field induced by transcranial magnetic stimulation , 2011, NeuroImage.

[26]  Xavier Lladó,et al.  Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review , 2017, Artif. Intell. Medicine.

[27]  Christian Wachinger,et al.  DeepNAT: Deep convolutional neural network for segmenting neuroanatomy , 2017, NeuroImage.

[28]  W. Paulus Transcranial electrical stimulation (tES – tDCS; tRNS, tACS) methods , 2011, Neuropsychological rehabilitation.

[29]  Satoshi Tanaka,et al.  Inter-subject Variability in Electric Fields of Motor Cortical tDCS , 2015, Brain Stimulation.

[30]  Mark Hallett,et al.  The electric field induced in the brain by magnetic stimulation: a 3-D finite-element analysis of the effect of tissue heterogeneity and anisotropy , 2003, IEEE Transactions on Biomedical Engineering.

[31]  Essam A. Rashed,et al.  Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation , 2019, IEEE Transactions on Medical Imaging.

[32]  Akimasa Hirata,et al.  Confirmation of quasi-static approximation in SAR evaluation for a wireless power transfer system , 2013, Physics in medicine and biology.

[33]  Alexander Opitz,et al.  Electric field calculations in brain stimulation based on finite elements: An optimized processing pipeline for the generation and usage of accurate individual head models , 2013, Human brain mapping.

[34]  Akimasa Hirata,et al.  Fast multigrid-based computation of the induced electric field for transcranial magnetic stimulation , 2012, Physics in medicine and biology.

[35]  M. A. Stuchly,et al.  High-resolution organ dosimetry for human exposure to low-frequency magnetic fields , 1998 .

[36]  Andrew Zisserman,et al.  Estimation of the partial volume effect in MRI , 2002, Medical Image Anal..

[37]  Gaps in Knowledge Relevant to the “Guidelines for Limiting Exposure to Time-Varying Electric and Magnetic Fields (1 Hz–100 kHz)” , 2020, Health physics.

[38]  S. Rossi,et al.  Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. Committee , 2015, Clinical Neurophysiology.

[39]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[40]  Andrea Bergmann,et al.  Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .

[41]  Hartwig R. Siebner,et al.  Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art , 2018, NeuroImage.