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Figure 8 provides an example with three different models exposed to the same field from a head coil. In the models shown in Figs. 8(a) and (b), the SAR distributions are quite uneven, with some noticeable hotspots. In these images, the most critical SAR value is the 10 g averaged SAR, and the maximum permissible average power is 25 W for the model in Fig. 8(a) and 33 W for the model in Fig. 8(b). In the model in Fig. 8(c), the SAR distribution is very different. There are no hotspots: the power loss throughout the imaging region is fairly constant. In this case, both the SAR averaged across the entire head and the 10 g averaged SAR are more important, and the maximum permissible average power is 35 W.

8. These images show SAR distributions in three different voxel models in the same head coil, in transverse (top) and sagittal (bottom) planes.

Based on these results, the coil’s average power over the cycle should be kept below 25 W, the lowest of the permissible power values. At this level, it is unlikely that the coil will ever produce enough RF power to harm a patient. But it also means that the image quality may be reduced or the duration needed to take the image may increase. The use of CAE simulation can help to determine the maximum permissible field input power for different patients, so that every scan can produce a good quality image without exposing the patient to unnecessarily high RF fields.

SAR was originally introduced as a way to estimate heating within the body from RF fields, without taking into account the complex thermal properties of living tissues. Cells generate heat, while blood flow disperses temperature hotspots and sweating promotes heat loss through the skin. As Fig. 9 shows, a phantom cannot replicate these so-called “bioheat” effects, and it is impractical to measure the temperature distribution within a living patient undergoing an MRI scan. It is often more appropriate to use a bioheat solver to calculate temperature distributions inside the human body. A tissue temperature to +39°C is generally considered acceptable. Practical experience shows that temperature is typically a less critical criterion than SAR, so more power can be applied to an MRI coil while still maintaining patient safety.

9. These images show simulated temperature distributions for a homogeneous phantom (left) and a heterogeneous model (right), taking bioheat into account.

If it is not possible to achieve a perfect EM field distribution, it can be useful to evaluate the impact of an inhomogeneous EM field on the final image, and if compensation can be provided by the MRI sequence. Such compensation may require a dedicated MRI simulator such as the Juelich Extensible MRI simulator (JEMRIS).4 MRI simulators solve the Bloch equation, which describes the behavior of spins exposed to magnetic fields in a macroscopic way. In this way, MRI imaging can be simulated and relevant quantities such as a coil’s field of view (FOV), its g-factor for parallel imaging, and the noise covariance between the different channels can be calculated (Fig. 10). Integration between EM, thermal, and spin simulations provides a complete MRI simulation workflow. Of course, a simulation is only useful if it can be applied to the real world. Once a prototype of a coil has been constructed, it can be compared to actual measurements for that design. Homogeneous phantoms are widely available in MRI labs and easy to test, and their simple structure makes them simple to simulate, as well.

10. These images are outputs from JEMRIS for an eight-channel head coil, showing (a) full simulated MR images of the brain, (b) the coupling between each coil, and (c) its g-factor across a transverse slice.

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