Computational dosimetry in MRI in presence of hip, knee or shoulder implants: do we need accurate surgery models?

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.

An In-Silico Testbed for Fast and Accurate MR Labeling of Orthopaedic 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: - an in-silico human model based on the widely available Visible Human Project (VHP) cryo-section dataset; - 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 (BMI) 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 orthopaedic implants. The testbed has very recently been approved by the US Food and Drug Administration (FDA) as a medical device development tool (MDDT) for 1.5 T orthopaedic implant examinations.