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Genetic algorithm search for the worst-case MRI RF exposure for a multiconfiguration implantable fixation system
Jianfeng Zheng1, Qianlong Lan1, Wolfgang Kainz2
1Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, USA.
Magnetic Resonance in Medicine
|May 28, 2020
Summary
This study introduces a novel method using artificial neural networks and genetic algorithms to find the highest radiofrequency (RF) exposure from implantable devices during MRI scans. This approach efficiently identifies the worst-case scenarios with high accuracy.
Area of Science:
- Biomedical Engineering
- Medical Imaging Physics
- Computational Electromagnetics
Background:
- Implantable medical devices require safety assessments for Magnetic Resonance Imaging (MRI) environments.
- Radiofrequency (RF) exposure, quantified by specific absorption rate (SAR), is a critical safety parameter.
- Multiconfiguration systems present complex challenges in determining worst-case RF exposure due to numerous possible configurations.
Purpose of the Study:
- To develop and validate a method for identifying the worst-case configuration of multiconfiguration implantable fixation systems.
- To accurately predict the peak 1-g and/or 10-g averaged specific absorption rate (SAR1g/10g) under MRI conditions.
- To efficiently search for the highest RF exposure configurations within both discrete and continuous sample spaces.
Main Methods:
- A two-step computational approach combining artificial neural networks (ANN) and genetic algorithms (GA).
- ANN is employed for predicting SAR1g/10g levels across various configurations.
- GA is utilized to search the multidimensional, nonlinear problem space for the configuration yielding maximum SAR1g/10g.
Main Results:
- The ANN-GA method effectively identifies worst-case configurations and predicts SAR1g/10g using a limited subset of samples (less than 20% of the discrete space).
- Accurate worst-case prediction in the generalized continuous sample space with errors below 1.6% for SAR1g and 1.3% for SAR10g.
- Demonstrated efficacy using a generic plate system with 576 configurations across 1.5T and 3T MRI systems.
Conclusions:
- The combined ANN and GA approach provides a robust and data-efficient technique for determining worst-case RF exposure.
- This method significantly simplifies the safety assessment of multiconfiguration implantable systems in MRI.
- Requires only a small amount of training data, making it practical for complex device evaluations.
Keywords:
RF-induced heatingartificial neural networkgenetic algorithmmagnetic resonant imaging safetyspecific absorption rate
