Magnetic resonance imaging (MRI) on patients with implanted deep brain stimulators (DBSs) can be hazardous because of the antenna-effect of leads exposed to the incident radio-frequency field. This study evaluated electromagnetic field and specific absorption rate (SAR) changes as a function of lead resistivity on an anatomically precise head model in a 3T system. The anatomical accuracy of our head model allowed for detailed modeling of the path of DBS leads between epidermis and the outer table. Our electromagnetic finite difference time domain (FDTD) analysis showed significant changes of 1 g and 10 g averaged SAR for the range of lead resistivity modeled, including highly conductive leads up to highly resistive leads. Antenna performance and whole-head SAR were sensitive to the presence of the DBS leads only within 10%, while changes of over one order of magnitude were observed for the peak 10 g averaged SAR, suggesting that local SAR values should be considered in DBS guidelines. With rho(lead) = rho(copper) , and the MRI coil driven to produce a whole-head SAR without leads of 3.2 W/kg, the 1 g averaged SAR was 1080 W/kg and the 10 g averaged SAR 120 W/kg at the tip of the DBS lead. Conversely, in the control case without leads, the 1 g and 10 g averaged SAR were 0.5 W/kg and 0.6 W/kg, respectively, in the same location. The SAR at the tip of lead was similar with electrically homogeneous and electrically heterogeneous models. Our results show that computational models can support the development of novel lead technology, properly balancing the requirements of SAR deposition at the tip of the lead and power dissipation of the system battery.
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"Multiscale modeling with both milli-and micro-metric resolutions was used in order to calculate in a reasonable computing time (i.e., about three days) a precise solution of the electric field and SAR generated by an MRI head coil at 128 MHz over the entire head. We then compared the results obtained using the multi-scale model with those of the original 1 mm 3 uniform head model used in   in order to assess how the resolution affected the electric solution. "
[Show abstract][Hide abstract] ABSTRACT: Deep brain stimulation (DBS) is an established procedure for the treatment of movement and affective disorders. Patients with DBS may benefit from magnetic resonance imaging (MRI) to evaluate injuries or comorbidities. However, the MRI radio-frequency (RF) energy may cause excessive tissue heating particularly near the electrode. This paper studies how the accuracy of numerical modeling of the RF field inside a DBS patient varies with spatial resolution and corresponding anatomical detail of the volume surrounding the electrodes. A multiscale model (MS) was created by an atlas-based segmentation using a 1 mm(3) head model (mRes) refined in the basal ganglia by a 200 μ m(2) ex-vivo dataset. Four DBS electrodes targeting the left globus pallidus internus were modeled. Electromagnetic simulations at 128 MHz showed that the peak of the electric field of the MS doubled (18.7 kV/m versus 9.33 kV/m) and shifted 6.4 mm compared to the mRes model. Additionally, the MS had a sixfold increase over the mRes model in peak-specific absorption rate (SAR of 43.9 kW/kg versus 7 kW/kg). The results suggest that submillimetric resolution and improved anatomical detail in the model may increase the accuracy of computed electric field and local SAR around the tip of the implant.
Computational and Mathematical Methods in Medicine 07/2013; 2013(3):694171. DOI:10.1155/2013/694171 · 0.77 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This paper presents a virtual model of patients with Deep Brain Stimulation implants. The model is based on Human Connectome and 7 Tesla Magnetic Resonance Imaging (MRI) data. We envision that the proposed virtual patient simulator will enable radio frequency power dosimetry on patients with deep brain stimulation implants undergoing MRI. Results from the proposed virtual patient study may facilitate the use of clinical MRI instead of computed tomography scans. The virtual patient will be flexible and morphable to relate to patient-specific neurological and psychiatric conditions such as Obsessive Compulsive Disorder, which benefit from deep brain stimulation.
International Journal on Advances in Life Sciences 01/2014; 6(3-4):364-372. · 14.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The analysis of high-field RF field-tissue interactions requires high-performance finite-difference time-domain (FDTD) computing. Conventional CPU-based FDTD calculations offer limited computing performance in a PC environment. This study presents a graphics processing unit (GPU)-based parallel-computing framework, producing substantially boosted computing efficiency (with a two-order speedup factor) at a PC-level cost. Specific details of implementing the FDTD method on a GPU architecture have been presented and the new computational strategy has been successfully applied to the design of a novel 8-element transceive RF coil system at 9.4 T. Facilitated by the powerful GPU-FDTD computing, the new RF coil array offers optimized fields (averaging 25% improvement in sensitivity, and 20% reduction in loop coupling compared with conventional array structures of the same size) for small animal imaging with a robust RF configuration. The GPU-enabled acceleration paves the way for FDTD to be applied for both detailed forward modeling and inverse design of MRI coils, which were previously impractical.