A comparative study of MRI-induced RF heating in pediatric and adult populations with epicardial and endocardial implantable electronic devices

RF-induced heating of capped and uncapped abandoned epicardial leads during MRI at 1.5 T and 3 T

There is a paucity of data regarding the safety of magnetic resonance imaging (MRI) in patients with abandoned epicardial leads. Few studies have reported temperature rises up to 76 °C during MRI at 1.5 T in gel phantoms implanted with epicardial leads; however, lead trajectories used in these experiments were not clinically relevant. This work reports patient-specific RF heating of both capped and uncapped abandoned epicardial lead configurations during MRI at both 1.5 T and 3 T field strengths. We found that leads routed along realistic, patient-derived trajectories generated substantially lower RF heating than the previously reported worst-case phantom experiments. We also found that MRI at the head imaging landmark leads to substantially lower RF heating compared to MRI at the chest or abdomen landmarks at both 1.5 T and 3 T. Our results suggest that patients with abandoned epicardial leads may safely undergo MRI for head imaging, but caution is warranted during chest and abdominal imaging.Clinical Relevance- Patients with abandoned epicardial leads may safely undergo MRI for head imaging, but caution is warranted during chest and abdominal imaging.

Implants talk to each-other: RF heating changes when two DBS leads are present simultaneously during MRI

Deep brain stimulation (DBS) has proven to be an effective treatment for Parkinson's disease and other brain disorders. The procedure often involves implanting two elongated leads aimed at specific brain nuclei in both the left and right hemispheres. However, evaluating the safety of MRI in patients with such implants has only been done on an individual lead basis, ignoring the possibility of crosstalk between the leads. This study evaluates the impact of crosstalk on power deposition at the lead tip through numerical simulation and phantom experiments. We used CT images to obtain patient-specific lead trajectories and compared the power deposition at the lead tip in cases with bilateral and unilateral DBS implants. Our results indicate that the RF power deposition at the lead tip can vary by up to 6-fold when two DBS leads are present together compared to when only one lead is present. Experimental measurements in a simplified case of two lead-only DBS systems confirmed the existence of crosstalk.Clinical Relevance-Our results indicate that RF heating of implanted leads during MRI can be affected by the presence of another lead in the body, which may increase or decrease the power deposition in the tissue depending on the position and configuration of the leads.

Rapid prediction of MRI-induced RF heating of active implantable medical devices using machine learning

The interaction between an active implantable medical device and magnetic resonance imaging (MRI) radiofrequency (RF) fields can cause excessive tissue heating. Existing methods for predicting RF heating in the presence of an implant rely on either extensive phantom experiments or electromagnetic (EM) simulations with varying degrees of approximation of the MR environment, the patient, or the implant. On the contrary, fast MR thermometry techniques can provide a reliable real-time map of temperature rise in the tissue in the vicinity of conductive implants. In this proof-of-concept study, we examined whether a machine learning (ML) based model could predict the temperature increase in the tissue near the tip of an implanted lead after several minutes of RF exposure based on only a few seconds of experimentally measured temperature values. We performed phantom experiments with a commercial deep brain stimulation (DBS) system to train a fully connected feedforward neural network (NN) to predict temperature rise after ~3 minutes of scanning at a 3 T scanner using only data from the first 5 seconds. The NN effectively predicted ΔTmax-R2 = 0.99 for predictions in the test dataset. Our model also showed potential in predicting RF heating for other various scenarios, including a DBS system at a different field strength (1.5 T MRI, R2 = 0.87), different field polarization (1.2 T vertical MRI, R2 = 0.79), and an unseen implant (cardiac leads at 1.5 T MRI, R2 = 0.91). Our results indicate great potential for the application of ML in combination with fast MR thermometry techniques for rapid prediction of RF heating for implants in various MR environments.Clinical Relevance- Machine learning-based algorithms can potentially enable rapid prediction of MRI-induced RF heating in the presence of unknown AIMDs in various MR environments.

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.