Body-loop related MRI radiofrequency-induced heating hazards: Observations, characterizations, and recommendations.

PURPOSE: To assess RF-induced heating hazards in 1.5T MR systems caused by body-loop postures. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Effects of patient orientations, landmark positions, and device positions on the MRI RF-induced heating for modular external fixation devices.

PURPOSE: This paper studies the RF-induced heating for modular external fixation devices applied on the leg regions of the human bodies. Through numerical investigations of RF-induced heating related to different patient orientations, landmark positions, and device positions under 1.5T and 3T MRI systems, simple and practical methods to reduce RF-induced heating are recommended. METHODS: Numerical simulations using a full-wave electromagnetic solver based on the finite-difference time-domain method were performed to characterize the effects of patient orientations (head-first/feet-first), landmark positions (the scanning area of the patient), and device positions (device on left or right leg) on the RF-induced heating of the external fixation devices. The G32 coil design and three anatomical human models (Duke model, Ella model, and Fats model) were adopted to model the MRI RF coil and the patients. RESULTS: The relative positions of the patient, device, and coil can significantly affect the RF-induced heating. With other conditions remaining the same, changing the device position or patient orientation can lead to a peak 1-g averaged spatial absorption ratio variation of a factor around four. By changing the landmark position and the patient orientation, the RF-induced heating can be reduced from 1323.6 W/kg to 217.5 W/kg for the specific scanning situations studied. CONCLUSION: Patient orientations, landmark positions, and device positions influence the RF-induced heating of modular external fixation devices at 1.5 T and 3 T. These features can be used to reduce the RF-induced heating during MRI simply and practically.

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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.