Patient’s body composition can significantly affect RF power deposition in the tissue around DBS implants: ramifications for lead management strategies and MRI field-shaping techniques

Abstract

Patients with active implants such as deep brain stimulation (DBS) devices have limited access to magnetic resonance imaging (MRI) due to risks associated with RF heating of implants in MRI environment. With an aging population and increased prevalence of neurodegenerative disease, the indication for MRI exams in patients with such implants increases as well. In response to this growing need, many groups have investigated strategies to mitigate RF heating of DBS implants during MRI. These efforts fall into two main categories: MRI field-shaping methods, where the electric field of the MRI RF coil is modified to reduce the interaction with implanted leads, and lead management techniques where surgical modifications in the trajectory reduces the coupling with RF fields. Studies that characterize these techniques, however, have relied either on simulations with homogenous body models, or experiments with box-shaped single-material phantoms. It is well established, however, that the shape and heterogeneity of human body affects the distribution of RF electric fields, which by proxy, alters the heating of an implant inside the body. In this contribution, we applied numerical simulations and phantom experiments to examine the degree to which variations in patient’s body composition affects RF power deposition. We then assessed effectiveness of RF-heating mitigation strategies under variant patient body compositions. Our results demonstrated that patient’s body composition substantially alters RF power deposition in the tissue around implanted leads. However, both techniques based on MRI field-shaping and DBS lead management performed well under variant body types.

Vertical open‐bore MRI scanners generate significantly less radiofrequency heating around implanted leads: A study of deep brain stimulation implants in 1.2T OASIS scanners versus 1.5T horizontal systems

“…Simulations were considered to be converged when the change in the magnitude of the S‐parameters between two consecutive passes fell below a set threshold of 0.03. Satisfying this convergence criterion on S‐parameters is shown to also guarantee the convergence of local power deposited in the tissue around implanted leads (calculated as ) 30 . All simulations converged with two to four adaptive passes.…”

“…For the full DBS system as well as the 100 cm wire when routed along trajectory III, the temperature increase was higher at 1.2T compared to that at 1.5T, although the values were less than 1°C for each scanner. This trajectory has been shown to minimize RF heating at 1.5T horizontal scanner 29,30 . For the commercial DBS device, the worst‐case RF heating was reduced from ~3°C to <0.4°C, allowing non‐restricted application of MRI imaging.…”

“…Satisfying this convergence criterion on Sparameters is shown to also guarantee the convergence of local power deposited in the tissue around implanted leads (calculated as |E| 2 ). 30 All simulations converged with two to four adaptive passes. Examples of mesh distributions in the SAR box, lead, insulation, and IPG as well as distribution of 0.1 g-SAR for a patient with bilateral lead has been shown in Figure 5.…”

Application of Deep Learning to Predict RF Heating of Cardiac Leads During Magnetic Resonance Imaging at 1.5 T and 3 T

“…As the number of patients with active implantable medical devices increases and MRI becomes more readily available, the number of cases where MRI is indicated in a patient with an existing conductive implant also is increasing rapidly. Consequently, extensive effort has been dedicated to estimating the magnitude of MRinduced RF heating in this patient population via electromagnetic simulations and phantom experiments both of which are known to be notably time consuming 20,[40][41][42][43][44][45] . A fast method for the real-time prediction of MRI-induced RF heating of implants to allow patient-by-patient risk assessment will be highly valuable but is currently missing.…”

“…The phenomenology of MR-induced RF heating in the human body with the presence of conductive implants has a large parameter space with multiple interplaying factors. These include MRI RF coil's geometry and frequency 18,46,47 , the implants' material 16,48 , position, and geometrical features 29,43 , as well as electrical properties of the tissue surrounding the implant 44,45 . In the case of elongated implants, such as leads in neuromodulation and cardiac devices, the distribution of MRI electric field E along the length of the implant has been shown to play a determinant role in the prediction of RF heating at the lead's tip 7,30 .…”