An investigation into the minimum number of tissue groups required for 7T in‐silico parallel transmit electromagnetic safety simulations in the human head

Abstract

Purpose Safety limits for the permitted specific absorption rate (SAR) place restrictions on pulse sequence design, especially at ultrahigh fields (≥ 7 tesla). Due to intersubject variability, the SAR is usually conservatively estimated based on standard human models that include an applied safety margin to ensure safe operation. One approach to reducing the restrictions is to create more accurate subject‐specific models from their segmented MR images. This study uses electromagnetic simulations to investigate the minimum number of tissue groups required to accurately determine SAR in the human head. Methods Tissue types from a fully characterized electromagnetic human model with 47 tissue types in the head and neck region were grouped into different tissue clusters based on the conductivities, permittivities, and mass densities of the tissues. Electromagnetic simulations of the head model inside a parallel transmit head coil at 7 tesla were used to determine the minimum number of required tissue clusters to accurately determine the subject‐specific SAR. The identified tissue clusters were then evaluated using 2 additional well‐characterized electromagnetic human models. Results A minimum of 4‐clusters‐plus‐air was found to be required for accurate SAR estimation. These tissue clusters are centered around gray matter, fat, cortical bone, and cerebrospinal fluid. For all 3 simulated models, the parallel transmit maximum 10g SAR was consistently determined to within an error of <12% relative to the full 47‐tissue model. Conclusion A minimum of 4‐clusters‐plus‐air are required to produce accurate personalized SAR simulations of the human head when using parallel transmit at 7 tesla.

Specific absorption rate and temperature in neonate models resulting from exposure to a 7T head coil

“…Nevertheless, the differences caused were on the order of 1%‐2%, suggesting that this would not be a major cause of error. Others have reported a similar robustness of EM simulations to reduced segmentation quality 32,33 …”

Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1‐weighted dataset

“…The peak SAR 10g values obtained in the network‐generated body models were within 3.0% of those obtained in the corresponding ground truth body models, for all subjects. This is considerably lower than the intersubject variability in peak SAR 10g of 37.2% (i.e., absolute range divided by the mean value) and practical uncertainty levels associated with RF exposure assessments 6,15,33 . The head‐averaged SAR values obtained in the network‐generated models were within 1.8% of those obtained in the ground‐truth models.…”

“…Several groups have previously demonstrated subject‐specific approaches to SAR prediction by establishing a subject‐specific anatomical model from MR data which is then evaluated in an electromagnetic solver 12,13 . This builds on the principle that local SAR depends predominantly on the geometry of electrically distinct tissues, rather than their exact dielectric properties 14,15 . To address the time‐consuming process of image segmentation, techniques based on semi‐automatic segmentation, 12 image registration, 13 computer vision 16,17 and deep learning have been proposed 18 .…”

Safety for Human MR Scanners at 7T

“…The RF transmit (B1+) field inhomogeneity also raises additional SAR-related issues. 48 Due to constructive interference of a single-transmit RF pulse, B1+ is highest at the central area of the brain, whereas it is lower at the periphery. 49 The relative standard deviation of the B1+ field over the whole cerebrum is 11% and 22% at 3T and 7T, respectively.…”