A computational model for real-time calculation of electric field due to transcranial magnetic stimulation in clinics

The aim of this paper is to propose an approach for an accurate and fast (real-time) computation of the electric field induced inside the whole brain volume during a transcranial magnetic stimulation (TMS) procedure. The numerical solution implements the admittance method for a discretized realistic brain model derived from Magnetic Resonance Imaging (MRI). Results are in a good agreement with those obtained using commercial codes and require much less computational time. An integration of the developed codewith neuronavigation toolswill permit real-time evaluation of the stimulated brain regions during the TMSdelivery, thus improving the efficacy of clinical applications

Brain and Human Body Modelling 2021

This open access book describes modern applications of computational human modelling to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. This is an Open Access book

Can ultrasound be used to stimulate nerve tissue?

BACKGROUND: The stimulation of nerve or cortical tissue by magnetic induction is a relatively new tool for the non-invasive study of the brain and nervous system. Transcranial magnetic stimulation (TMS), for example, has been used for the functional mapping of the motor cortex and may have potential for treating a variety of brain disorders. METHODS AND RESULTS: A new method of stimulating active tissue is proposed by propagating ultrasound in the presence of a magnetic field. Since tissue is conductive, particle motion created by an ultrasonic wave will induce an electric current density generated by Lorentz forces. An analytical derivation is given for the electric field distribution induced by a collimated ultrasonic beam. An example shows that peak electric fields of up to 8 V/m appear to be achievable at the upper range of diagnostic intensities. This field strength is about an order of magnitude lower than fields typically associated with TMS; however, the electric field gradients induced by ultrasound can be quite high (about 60 kV/m(2 )at 4 MHz), which theoretically play a more important role in activation than the field magnitude. The latter value is comparable to TMS-induced gradients. CONCLUSION: The proposed method could be used to locally stimulate active tissue by inducing an electric field in regions where the ultrasound is focused. Potential advantages of this method compared to TMS is that stimulation of cortical tissue could be highly localized as well as achieved at greater depths in the brain than is currently possible with TMS

A machine learning approach to support deep brain stimulation programming

Adjusting the stimulation parameters is a challenge in deep brain stimulation (DBS) therapy due to the vast number of different configurations available. As a result, systems based on the visualization of the volume of tissue activated (VTA) produced by a particular stimulation setting have been developed. However, the medical specialist still has to search, by trial and error, for a DBS set-up that generates the desired VTA. Therefore, our goal is developing a DBS parameter tuning strategy for current clinical devices that allows defining a target VTA under biophysically viable constraints. We propose a machine learning approach that allows estimating the DBS parameter values for a given VTA, which comprises two main stages: i) A K-nearest neighbors-based deformation to define a target VTA preserving biophysically viable constraints. ii) A parameter estimation stage that consists of a data projection using metric learning to highlight relevant VTA properties, and a regression/classification algorithm to estimate the DBS parameters that generate the target VTA. Our methodology allows setting a biophysically compliant target VTA and accurately predicts the required configuration of stimulation parameters. Also, the performance of our approach is stable for both isotropic and anisotropic tissue conductivities. Furthermore, the computational time of the trained system is acceptable for real-world implementations

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte