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To assess RF‐induced heating hazards in 1.5T MR systems caused by body‐loop postures.

Twelve advanced high‐resolution anatomically correct human body models with different body‐loop postures are created based on poseable human adult male models. Numerical simulations are performed to assess the radiofrequency (RF)‐induced heating of these 12 models at 11 landmarks. A customized phantom is developed to validate the numerical simulations and quantitatively analyze factors affecting the RF‐induced heating, eg, the contact area, the loop size, and the loading position. The RF‐induced heating inside three differently posed phantoms is measured.

The RF‐induced heating from the body‐loop postures can be up to 11 times higher than that from the original posture. The RF‐induced heating increases with increasing body‐loop size and decreasing contact area. The magnetic flux increases when the body‐loop center and the RF coil isocenter are close to each other, leading to increased RF‐induced heating. An air gap created in the body loop or generating a polarized magnetic field parallel to the body loop can reduce the heating by a factor of three at least. Experimental measurements are provided, validating the correctness of the numerical results.

Safe patient posture during MR examinations is recommended with the use of insulation materials to prevent loop formation and consequently avoiding high RF‐induced heating. If body loops cannot be avoided, the body loop should be placed outside the RF transmitting coil. In addition, linear polarization with magnetic fields parallel to the body loop can be used to circumvent high RF‐induced heating.

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 (SAR_1g/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 SAR_1g/10gwith 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 SAR_1gand less than 1.3% for SAR_10gcompared 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.