Genetic algorithm search for the worst-case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks.

Abstract

This paper presents a method to search for the worst-case configuration leading to the highest RF exposure for a multiconfiguration implantable fixation system under MRI. A two-step method combining an artificial neural network and a genetic algorithm is developed to achieve this purpose. In the first step, the level of RF exposure in terms of peak 1-g and/or 10-g averaged specific absorption rate (SAR1g/10g ), related to the multiconfiguration system, is predicted using an artificial neural network. A genetic algorithm is then used to search for the worst-case configuration of this multidimensional nonlinear problem within both the enumerated discrete sample space and generalized continuous sample space. As an example, a generic plate system with a total of 576 configurations is used for both 1.5T and 3T MRI systems. The presented method can effectively identify the worst-case configuration and accurately predict the SAR1g/10g with no more than 20% of the samples in the studied discrete sample space, and can even predict the worst case in the generalized continuous sample space. The worst-case prediction error in the generalized continuous sample space is less than 1.6% for SAR1g and less than 1.3% for SAR10g compared with the simulation results. The combination of an artificial neural network with genetic algorithm is a robust technique to determine the worst-case RF exposure level for a multiconfiguration system, and only needs a small amount of training data from the entire system.