A numerical investigation on the effect of RF coil feed variability on global and local electromagnetic field exposure in human body models at 64 MHz

Impact of MRI RF coil design on the RF-induced heating of medical implants: fixedB1+rmsexposure versus normal operating mode

A direct comparison of the impact of RF coil design under specific absorption rate andB1+rmslimitations are investigated and quantified using RF coils of different geometries and topologies at 64 MHz and 128 MHz. The RF-inducedin vivoelectric field and power deposition of a 50 cm long pacemaker and 55 cm long deep brain stimulator (DBS) are evaluated within two anatomical models exposed with these RF coils. The associated uncertainty is quantified and analyzed under a fixedB1+rmsincident and normal operating mode. For a fixedB1+rmsincident, thein vivoincident field shows a much higher uncertainty (>5.6 dB) to the RF coil diameter compared to other design parameters (e.g. <2.2 dB for coil length and topology), while the associated uncertainty reduced greatly (e.g. <1.5 dB) under normal operating mode exposure. Similar uncertainties are observed in the power deposition near the pacemaker and DBS electrode. Compared to the normal operating mode, applying a fixedB1+rmsfield to the untested implant will lead to a large variation in the induced incident and power deposition of the implant, as a result, a larger safe margin when different coil designs (e.g. coil diameter) are considered.

Application of Machine learning to predict RF heating of cardiac leads during magnetic resonance imaging at 1.5 T and 3 T: A simulation study

Predicting magnetic resonance imaging (MRI)-induced heating of elongated conductive implants, such as leads in cardiovascular implantable electronic devices, is essential to assessing patient safety. Phantom experiments have traditionally been used to estimate radio-frequency (RF) heating of implants, but they are time-consuming. Recently, machine learning has shown promise for fast prediction of RF heating of orthopaedic implants when the implant position within the MRI RF coil was predetermined. We explored whether deep learning could be applied to predict RF heating of conductive leads with variable positions and orientations during MRI at 1.5 T and 3 T. Models of 600 cardiac leads with clinically relevant trajectories were generated, and electromagnetic simulations were performed to calculate the maximum of the 1 g-averaged specific absorption rate (SAR) of RF energy at the tips of lead models during MRI at 1.5 T and 3 T. Neural networks were trained to predict the maximum SAR at the lead tip from the knowledge of the coordinates of points along the lead trajectory. Despite the large range of SAR values (∼230 W/kg to ∼ 3200 W/kg and ∼ 10 W/kg to ∼ 3300 W/kg), the root- mean-square error of the predicted vs ground truth SAR remained at 223 W/kg and 206 W/kg, with the R² scores of 0.89 and 0.85 on the testing set for 1.5 T and 3 T models, respectively. The results suggest that machine learning is a promising approach for fast assessment of RF heating of lead-like implants when only the knowledge of the lead geometry and MRI RF coil features are in hand.

Effect of field strength on RF power deposition near conductive leads: A simulation study of SAR in DBS lead models during MRI at 1.5 T-10.5 T

BACKGROUND

Since the advent of magnetic resonance imaging (MRI) nearly four decades ago, there has been a quest for ever-higher magnetic field strengths. Strong incentives exist to do so, as increasing the magnetic field strength increases the signal-to-noise ratio of images. However, ensuring patient safety becomes more challenging at high and ultrahigh field MRI (i.e., ≥3 T) compared to lower fields. The problem is exacerbated for patients with conductive implants, such as those with deep brain stimulation (DBS) devices, as excessive local heating can occur around implanted lead tips. Despite extensive effort to assess radio frequency (RF) heating of implants during MRI at 1.5 T, a comparative study that systematically examines the effects of field strength and various exposure limits on RF heating is missing.

PURPOSE

This study aims to perform numerical simulations that systematically compare RF power deposition near DBS lead models during MRI at common clinical and ultra-high field strengths, namely 1.5, 3, 7, and 10.5 T. Furthermore, we assess the effects of different exposure constraints on RF power deposition by imposing limits on either the B1+ or global head specific absorption rate (SAR) as these two exposure limits commonly appear in MRI guidelines.

METHODS

We created 33 unique DBS lead models based on postoperative computed tomography (CT) images of patients with implanted DBS devices and performed electromagnetic simulations to evaluate the SAR of RF energy in the tissue surrounding lead tips during RF exposure at frequencies ranging from 64 MHz (1.5 T) to 447 MHz (10.5 T). The RF exposure was implemented via realistic MRI RF coil models created based on physical prototypes built in our institutions. We systematically examined the distribution of local SAR at different frequencies with the input coil power adjusted to either limit the B1+ or the global head SAR.

RESULTS

The MRI RF coils at higher resonant frequencies generated lower SARs around the lead tips when the global head SAR was constrained. The trend was reversed when the constraint was imposed on B1+.

CONCLUSION

At higher static fields, MRI is not necessarily more dangerous than at lower fields for patients with conductive leads. Specifically, when a conservative safety criterion, such as constraints on the global SAR, is imposed, coils at a higher resonant frequency tend to generate a lower local SAR around implanted leads due to the decreased B1+ and, by proxy, E field levels.

Evaluation of Patient Positioning to Mitigate RF-induced Heating of Cardiac Implantable Electronic Devices for Pediatric MRI Exams

Pediatric patients with cardiac implantable electronic devices (CIEDs) are generally contraindicated for MRI exams. Previous work in the adult population suggests that RF-induced lead-tip heating strongly depends on the patient's position and orientation within the MRI scanner. The objective of this work was to evaluate the local Specific Absorption Rate (local-SAR) in silico for several pediatric patient positions within the MRI scanner as a method to potentially mitigate RF-heating lead-tip heating of CIEDs.

Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models

Interaction of an active electronic implant such as a deep brain stimulation (DBS) system and MRI RF fields can induce excessive tissue heating, limiting MRI accessibility. Efforts to quantify RF heating mostly rely on electromagnetic (EM) simulations to assess individualized specific absorption rate (SAR), but such simulations require extensive computational resources. Here, we investigate if a predictive model using machine learning (ML) can predict the local SAR in the tissue around tips of implanted leads from the distribution of the tangential component of the MRI incident electric field, E_tan. A dataset of 260 unique patient-derived and artificial DBS lead trajectories was constructed, and the 1 g-averaged SAR, 1gSAR_max, at the lead-tip during 1.5 T MRI was determined by EM simulations. E_tan values along each lead's trajectory and the simulated SAR values were used to train and test the ML algorithm. The resulting predictions of the ML algorithm indicated that the distribution of E_tan could effectively predict 1gSAR_max at the DBS lead-tip (R = 0.82). Our results indicate that ML has the potential to provide a fast method for predicting MR-induced power absorption in the tissue around tips of implanted leads such as those in active electronic medical devices.

Induced radiofrequency fields in patients undergoing MR examinations: insights for risk assessment

Purpose. To characterize and quantify the induced radiofrequency (RF) electric (E)-fields andB_1+rmsfields in patients undergoing magnetic resonance (MR) examinations; to provide guidance on aspects of RF heating risks for patients with and without implants; and to discuss some strengths and limitations of safety assessments in current ISO, IEC, and ASTM standards to determine the RF heating risks for patients with and without implants.Methods. InducedE-fields andB_1+rmsfields during 1.5 T and 3 T MR examinations were numerically estimated for high-resolution patient models of the Virtual Population exposed to ten two-port birdcage RF coils from head to feet imaging landmarks over the full polarization space, as well as in surrogate ASTM phantoms.Results. Worst-caseB_1+rmsexposure greater than 3.5μT (1.5 T) and 2μT (3 T) must be considered for all MR examinations at the Normal Operating Mode limit. Representative inducedE-field and specific absorption rate distributions under different clinical scenarios allow quick estimation of clinical factors of high and reduced exposure.B₁shimming can cause +6 dB enhancements toE-fields along implant trajectories. The distribution and magnitude of inducedE-fields in the ASTM phantom differ from clinical exposures and are not always conservative for typical implant locations.Conclusions.Field distributions in patient models are condensed, visualized for quick estimation of risks, and compared to those induced in the ASTM phantom. InducedE-fields in patient models can significantly exceed those in the surrogate ASTM phantom in some cases. In the recent 19^ε2revision of the ASTM F2182 standard, the major shortcomings of previous versions have been addressed by requiring that the relationship between ASTM test conditions andin vivotangentialE-fields be established, e.g. numerically. With this requirement, the principal methods defined in the ASTM standard for passive implants are reconciled with those of the ISO 10974 standard for active implantable medical devices.

Patient Orientation Affects Lead-Tip Heating of Cardiac Active Implantable Medical Devices during MRI

Purpose

To evaluate changes in patient orientation to mitigate radiofrequency-induced lead-tip heating (LTH) during MRI.

Materials and Methods

LTH was evaluated for device type, lead path, and distance to the isocenter of a 1.5-T MRI system. LTH for 378 conditions in both head-first (HF) and feet-first (FF) orientations was measured for nine MRI-unsafe cardiac active implantable medical devices (AIMDs) placed along three (two anatomic, one planar) left-sided lead paths at nine landmark locations. The devices were exposed to 5 minutes of continuous radiofrequency energy at 4 W/kg whole-body specific absorption rate.

Results

LTH was greater in HF than in FF orientation for the planar and one anatomic lead path (P < .05). LTH was significantly affected by lead path, distance to isocenter, and patient orientation (all P < .05), but not by cardiac AIMD device type. Maximum LTH was observed in an HF orientation for the planar lead path when the lead tip was at isocenter (right ventricular [RV] lead: 32.0 °C ± 16.3 [standard deviation], right atrial [RA] lead: 16.1°C ± 9.3). In the FF orientation, LTH was significantly reduced (RV lead: 1.6°C ± 1.4; mean RA lead: 0.5°C ± 1.0; P = .008).

Conclusion

LTH for supine FF patient orientations among patients with anterior left-sided cardiac AIMDs can be significantly lower than LTH for supine HF orientations. There was no scenario in which LTH was significantly worse in the FF position. Changing patient orientation is a simple method to reduce radiofrequency-induced LTH.© RSNA, 2019See also the commentary by Litt in this issue.

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science-the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)-the research arm of CDRH-has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

RF-induced heating in tissue near bilateral DBS implants during MRI at 1.5 T and 3T: The role of surgical lead management

Access to MRI is limited for patients with deep brain stimulation (DBS) implants due to safety hazards, including radiofrequency (RF) heating of tissue surrounding the leads. Computational models provide an exquisite tool to explore the multi-variate problem of RF heating and help better understand the interaction of electromagnetic fields and biological tissues. This paper presents a computational approach to assess RF-induced heating, in terms of specific absorption rate (SAR) in the tissue, around the tip of bilateral DBS leads during MRI at 64MHz/1.5 T and 127 MHz/3T. Patient-specific realistic lead models were constructed from post-operative CT images of nine patients operated for sub-thalamic nucleus DBS. Finite element method was applied to calculate the SAR at the tip of left and right DBS contact electrodes. Both transmit head coils and transmit body coils were analyzed. We found a substantial difference between the SAR and temperature rise at the tip of right and left DBS leads, with the lead contralateral to the implanted pulse generator (IPG) exhibiting up to 7 times higher SAR in simulations, and up to 10 times higher temperature rise during measurements. The orientation of incident electric field with respect to lead trajectories was explored and a metric to predict local SAR amplification was introduced. Modification of the lead trajectory was shown to substantially reduce the heating in phantom experiments using both conductive wires and commercially available DBS leads. Finally, the surgical feasibility of implementing the modified trajectories was demonstrated in a patient operated for bilateral DBS.