MRI-Based Multiscale Model for Electromagnetic Analysis in the Human Head with Implanted DBS

Iacono, Maria Ida; Makris, Nikos; Mainardi, Luca; Angelone, Leonardo M.; Bonmassar, Giorgio

doi:10.1155/2013/694171

Cited by 23 publications

(28 citation statements)

References 65 publications

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“…Using a large memory (1.5 TB), 30 processors machine, the EM simulation step was ~6 hours for all DBS models simulated in this. This is an order or magnitude faster than simulation times reported by other authors who used FDTD (the reported simulation times for this type of problem with FDTD are 24–48 hours, even when using GPU acceleration (Iacono et al , 2013; Cabot et al , 2013)).…”

Section: Discussionmentioning

confidence: 68%

“…This is due to electromagnetic (EM) coupling between the MRI radiofrequency (RF) coil and the DBS metallic leads (Yeung and Atalar, 2001; Finelli et al , 2002; Park et al , 2005; Park et al , 2007; Angelone et al , 2008; Neufeld et al , 2009; Bottomley et al , 2010; Acikel and Atalar, 2011; Eryaman et al , 2011; Bonmassar et al , 2013; Iacono et al , 2013; Acikel et al , 2016; Golestanirad et al , 2016a). As a result, MRI is seldom used in DBS patients.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the relatively large body of literature on this topic, to our knowledge most previously published studies have simulated highly simplified DBS implant models. For example, the geometrical path modeled often does not include lead loops (whereas in reality DBS leads are coiled over the skull) (Park et al , 2005; Neufeld et al , 2009; Angelone et al , 2010; Eryaman et al , 2011; Mohsin, 2011; Eryaman et al , 2012; Bonmassar et al , 2013; Cabot et al , 2013; Iacono et al , 2013; Eryaman et al , 2014; Serano et al , 2014), no extension cable (Mohsin et al , 2008; Neufeld et al , 2009; Angelone et al , 2010; Eryaman et al , 2011; Mohsin, 2011; Eryaman et al , 2012; Iacono et al , 2013; Cabot et al , 2013; Eryaman et al , 2014; Golestanirad et al , 2016b; Golestanirad et al , 2016a) and have straight internal wires instead of the helicoidal wiring used in commercially available systems (Park et al , 2005; Mohsin et al , 2008; Neufeld et al , 2009; Angelone et al , 2010; Mohsin, 2011; Eryaman et al , 2011; Eryaman et al , 2012; Bonmassar et al , 2013; Iacono et al , 2013; Eryaman et al , 2014; Serano et al , 2014; Golestanirad et al , 2016b; Golestanirad et al , 2016a). These simplifications may lead to incorrect estimates of the implant electrical length, and ultimately of the SAR and temperature predictions.…”

Section: Introductionmentioning

confidence: 99%

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Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies

et al. 2018

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We propose a framework for electromagnetic (EM) simulation of deep brain stimulation (DBS) patients in radiofrequency (RF) coils. We generated a model of a DBS patient using post-operative head and neck computed tomography (CT) images stitched together into a “virtual CT” image covering the entire length of the implant. The body was modeled as homogeneous. The implant path extracted from the CT data contained self-intersections, which we corrected automatically using an optimization procedure. Using the CT-derived DBS path, we built a model of the implant including electrodes, helicoidal internal conductor wires, loops, extension cables, and the implanted pulse generator. We also built four simplified models with straight wires, no extension cables and no loops to assess the impact of these simplifications on safety predictions. We simulated EM fields induced by the RF birdcage body coil in the body model, including at the DBS lead tip at both 1.5 Tesla (64 MHz) and 3 Tesla (123 MHz). We also assessed the robustness of our simulation results by systematically varying the EM properties of the body model and the position and length of the DBS implant (sensitivity analysis). The topology correction algorithm corrected all self-intersection and curvature violations of the initial path while introducing minimal deformations (open-source code available at http://ptx.martinos.org/index.php/Main_Page). The unaveraged lead-tip peak SAR predicted by the five DBS models (0.1 mm resolution grid) ranged from 12.8 kW/kg (full model, helicoidal conductors) to 43.6 kW/kg (no loops, straight conductors)at 1.5 T (3.4-fold variation) and 18.6 kW/kg (full model, straight conductors) to 73.8 kW/kg (no loops, straight conductors)at 3 T (4.0-fold variation). At 1.5 T and 3 T, the variability of lead-tip peak SAR with respect to the conductivity ranged between 18% and 30%. Variability with respect to the position and length of the DBS implant ranged between 9.5% and 27.6%.

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Section: Discussionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies

et al. 2018

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“…[7][8][9][10] imaging (CDI) methods, which can image the electrical current pathway by injected currents, was proposed by using a clinical MRI scanner. 7,9,11 This method measures the magnetic flux density (B) induced by the injection current, and recovers the corresponding current density distribution of J = (J x , J y , J z ) from the Ampere law J = (1/µ 0 )∇ × B, where µ 0 is the magnetic permeability of free space.…”

Section: Deep Brain Stimulation (Dbs)mentioning

confidence: 99%

In vivo mapping of current density distribution in brain tissues during deep brain stimulation (DBS)

Sajib

Kim

et al. 2017

AIP Advances

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New methods for in vivo mapping of brain responses during deep brain stimulation (DBS) are indispensable to secure clinical applications. Assessment of current density distribution, induced by internally injected currents, may provide an alternative method for understanding the therapeutic effects of electrical stimulation. The current flow and pathway are affected by internal conductivity, and can be imaged using magnetic resonance-based conductivity imaging methods. Magnetic resonance electrical impedance tomography (MREIT) is an imaging method that can enable highly resolved mapping of electromagnetic tissue properties such as current density and conductivity of living tissues. In the current study, we experimentally imaged current density distribution of in vivo canine brains by applying MREIT to electrical stimulation. The current density maps of three canine brains were calculated from the measured magnetic flux density data. The absolute current density values of brain tissues, including gray matter, white matter, and cerebrospinal fluid were compared to assess the active regions during DBS. The resulting current density in different tissue types may provide useful information about current pathways and volume activation for adjusting surgical planning and understanding the therapeutic effects of DBS.

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“…One unilateral implant was modeled as an insulated lead with an array of four cylindrical electrode contacts at the distal tip [11]. The contacts were modeled as conductive cylindrical rings (1.5mm in height, 1.27mm in external diameter, 0.8mm internal diameter) separated by fully insulated cylinders (1.5mm in height, 1.27mm diameter) (Fig.…”

Section: Model Of Implantmentioning

confidence: 99%

A computational model for bipolar deep brain stimulation of the subthalamic nucleus

Iacono¹,

Neufeld²,

Bonmassar³

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been shown to reduce some of the symptoms of advanced, levodopa-responsive Parkinson's disease that are not adequately controlled with medication. However, the precise mechanism of the therapeutic action of DBS is still unclear. Stimulation-induced side effects are not uncommon and require electrical "dose" adjustments. Quantitative methods are needed to fully characterize the electric field in the deep brain region that surrounds the electrodes in order to help with adjustments and maximize the efficacy of the device.Herein we report a magnetic resonance imaging (MRI)-based head model proposed for analysis of fields generated by deep brain stimulation (DBS). The model was derived from multimodal image data at 0.5mm isotropic spatial resolution and distinguishes 142 anatomical structures, including the basal ganglia and 38 nuclei of the thalamus. Six bipolar electrode configurations (1-2, 1-3, 1-4, 2-3, 2-4, 3-4) were modeled in order to assess the effects of the inter-electrode distance of the electric field. Increasing the distance between the electrodes results in an attenuated stimulation, with up to 25% reduction in electric field amplitude delivered (2-3 vs. 1-4). The map of the deep brain structures provided a highly precise anatomical detail which is useful for the quantitative assessment of current spread around the electrode and a better evaluation of the stimulation setting for the treatment optimization.

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MRI-Based Multiscale Model for Electromagnetic Analysis in the Human Head with Implanted DBS

Cited by 23 publications

References 65 publications

Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies

Realistic modeling of deep brain stimulation implants for electromagnetic MRI safety studies

In vivo mapping of current density distribution in brain tissues during deep brain stimulation (DBS)

A computational model for bipolar deep brain stimulation of the subthalamic nucleus

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