Anatomical Model Uncertainty for RF Safety Evaluation of Metallic Implants Under MRI Exposure

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.

Exposure Optimization Trial for Patients With Medical Implants During MRI Exposure: Balance Between the Completeness and Efficiency

Elongated conductors, such as pacemaker leads, can couple to the MRI radio-frequency (RF) field during MRI scan and cause dangerous tissue heating. By selecting proper RF exposure conditions, the RF-induced power deposition can be suppressed. As the RF-induced power deposition is a complex function of multiple clinical factors, the problem remains how to perform the exposure selection in a comprehensive and efficient way. The purpose of this work is to demonstrate an exposure optimization trail that allows a comprehensive optimization in an efficient and traceable manner. The proposed workflow is demonstrated with a generic 40 cm long cardio pacemaker, major components of the clinical factors are decoupled from the redundant data set using principle component analysis, the optimized exposure condition can not only reduce the in vivo power deposition but also maintain good image quality.

Comparison of SAR distribution of hip and knee implantable devices in 1.5T conventional cylindrical-bore and 1.2T open-bore vertical MRI systems

PURPOSE

There is increasing use of open-bore vertical MR systems that consist of two planar RF coils. A recent study showed that the RF-induced heating of a neuromodulation device was much lower in the open-bore system at the brain and the chest imaging landmarks. This study focused on the hip and knee implants and compared the specific absorption rate (SAR) distribution in human models in a 1.2T open-bore coil with that of a 1.5T conventional birdcage coil.

METHODS

Computational modeling results were compared against the measurement values using a saline phantom. The differences in RF exposure were examined between a 1.2T open-bore coil and a 1.5T conventional birdcage coil using SAR in an anatomical human model.

RESULTS

Modeling setups were validated. The body placed closed to the coil elements led to high SAR values in the birdcage system compared with the open-bore system.

CONCLUSION

Our computational modeling showed that the 1.2T planar system demonstrated a lower intensity of SAR distribution adjacent to hip and knee implants compared with the 1.5T conventional birdcage system.

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.

Radiofrequency-induced heating of broken and abandoned implant leads during magnetic resonance examinations

Purpose

The risks of RF‐induced heating of active implantable medical device (AIMD) leads during MR examinations must be well understood and realistically assessed. In this study, we evaluate the potential additional risks of broken and abandoned (cut) leads.

Methods

First, we defined a generic AIMD with a metallic implantable pulse generator (IPG) and a 100‐cm long lead containing 1 or 2 wires. Next, we numerically estimated the deposited in vitro lead‐tip power for an intact lead, as well as with wire breaks placed at 10 cm intervals. We studied the effect of the break size (wire gap width), as well as the presence of an intact wire parallel to the broken wire, and experimentally validated the numeric results for the configurations with maximum deposited in vitro lead‐tip power. Finally, we performed a Tier 3 assessment of the deposited in vivo lead‐tip power for the intact and broken lead in 4 high resolution virtual population anatomic models for over 54,000 MR examination scenarios.

Results

The enhancement of the deposited lead‐tip power for the broken leads, compared to the intact lead, reached 30‐fold in isoelectric exposure, and 16‐fold in realistic clinical exposures. The presence of a nearby intact wire, or even a nearby broken wire, reduced this enhancement factor to <7‐fold over the intact lead.

Conclusion

Broken and abandoned leads can pose increased risk of RF‐induced lead‐tip heating to patients undergoing MR examinations. The potential enhancement of deposited in vivo lead‐tip power depends on location and type of the wire break, lead design, and clinical routing of the lead, and should be carefully considered when performing risk assessment for MR examinations and MR conditional labeling.

Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model

Numerical body models of children are used for designing medical devices, including but not limited to optical imaging, ultrasound, CT, EEG/MEG, and MRI. These models are used in many clinical and neuroscience research applications, such as radiation safety dosimetric studies and source localization. Although several such adult models have been reported, there are few reports of full-body pediatric models, and those described have several limitations. Some, for example, are either morphed from older children or do not have detailed segmentations. Here, we introduce a 29-month-old male whole-body native numerical model, "MARTIN", that includes 28 head and 86 body tissue compartments, segmented directly from the high spatial resolution MRI and CT images. An advanced auto-segmentation tool was used for the deep-brain structures, whereas 3D Slicer was used to segment the non-brain structures and to refine the segmentation for all of the tissue compartments. Our MARTIN model was developed and validated using three separate approaches, through an iterative process, as follows. First, the calculated volumes, weights, and dimensions of selected structures were adjusted and confirmed to be within 6% of the literature values for the 2-3-year-old age-range. Second, all structural segmentations were adjusted and confirmed by two experienced, sub-specialty certified neuro-radiologists, also through an interactive process. Third, an additional validation was performed with a Bloch simulator to create synthetic MR image from our MARTIN model and compare the image contrast of the resulting synthetic image with that of the original MRI data; this resulted in a "structural resemblance" index of 0.97. Finally, we used our model to perform pilot MRI safety simulations of an Active Implantable Medical Device (AIMD) using a commercially available software platform (Sim4Life), incorporating the latest International Standards Organization guidelines. This model will be made available on the Athinoula A. Martinos Center for Biomedical Imaging website.