Classification Scheme of Heating Risk during MRI Scans on Patients with Orthopaedic Prostheses

Orthopedic implants affect the electric field induced by switching gradients in MRI

PURPOSE

To investigate whether the risk of peripheral nerve stimulation increases in the presence of bulky metallic prostheses implanted in a patient's body.

METHODS

A computational tool was used to calculate the electric field (E-field) induced in a realistic human model due to the action of gradient fields. The calculations were performed both on the original version of the anatomical model and on a version modified through "virtual surgery" to incorporate knee, hip, and shoulder prostheses. Five exam positions within a body gradient coil and one position using a head gradient coil were simulated, subjecting the human model to the readout gradient from an EPI sequence. The induced E-field in models with and without prostheses was compared, focusing on the nerves and all other tissues (both including and excluding the bones from the analysis).

RESULTS

In the nerves, the most pronounced increase in the E-field (+24%) was observed around the knee implant during an abdominal MRI (Y axis readout). When extending the analysis to encompass all tissues (excluding bones), the greatest amplification (+360%) occurred around the knee implant during pelvic MRI (Z axis readout). Notable increases in E-field peaks were also identified around the shoulder and hip implants in multiple scenarios.

CONCLUSION

Based on the presented results, further investigations aimed at quantifying the threshold of nerve stimulation in the presence of bulky implants are desirable.

An in silico testbed for fast and accurate MR labeling of orthopedic implants

One limitation on the ability to monitor health in older adults using magnetic resonance (MR) imaging is the presence of implants, where the prevalence of implantable devices (orthopedic, cardiac, neuromodulation) increases in the population, as does the pervasiveness of conditions requiring MRI studies for diagnosis (musculoskeletal diseases, infections, or cancer). The present study describes a novel multiphysics implant modeling testbed using the following approaches with two examples: (1) an in silico human model based on the widely available Visible Human Project (VHP) cryo-section dataset; (2) a finite element method (FEM) modeling software workbench from Ansys (Electronics Desktop/Mechanical) to model MR radio frequency (RF) coils and the temperature rise modeling in heterogeneous media. The in silico VHP-Female model (250 parts with an additional 40 components specifically characterizing embedded implants and resultant surrounding tissues) corresponds to a 60-year-old female with a body mass index of 36. The testbed includes the FEM-compatible in silico human model, an implant embedding procedure, a generic parameterizable MRI RF birdcage two-port coil model, a workflow for computing heat sources on the implant surface and in adjacent tissues, and a thermal FEM solver directly linked to the MR coil simulator to determine implant heating based on an MR imaging study protocol. The primary target is MR labeling of large orthopedic implants. The testbed has very recently been approved by the US Food and Drug Administration (FDA) as a medical device development tool for 1.5 T orthopedic implant examinations.

Efficient prediction of MRI gradient-induced heating for guiding safety testing of conductive implants

PURPOSE

To propose an efficient numerical method to predict the temperature increase of an implantable medical device induced by any linearly polarized homogeneous magnetic field, according to the ISO 10974 methodology for testing of gradient-induced device heating.

THEORY AND METHODS

The concepts of device-specific power and temperature tensors are introduced to mathematically describe the electromagnetic and thermal anisotropic behavior of the device, from which the device heating for an arbitrary exposure direction can be predicted. The proposed method is compared to a brute-force approach based on simulations, and validated by applying it to four reference orthopedic implants with a commercial simulation software.

RESULTS

The proposed method requires about 5% $$ \% $$ of the time required by the brute-force approach, and 30% $$ \% $$ of the memory occupancy. The temperature increase predicted by the proposed method over a range of incident magnetic field exposures deviated from brute-force direct simulations by less than± $$ \pm $$ 0.3% $$ \% $$ .

CONCLUSION

The proposed method allows efficient prediction of the heating of an implantable medical device induced by any linearly polarized homogeneous magnetic field using a small fraction of the simulations required by the brute-force approach. The results can be used to predict the worst-case orientation of the gradient field, for subsequent experimental characterization according to the ISO 10974 standard.