The impact of variations in subject geometry, respiration and coil repositioning on the specific absorption rate in parallel transmit abdominal imaging at 7 T.

Abstract

Parallel transmit MRI at 7 T has increasingly been adopted in research projects and provides increased signal-to-noise ratios and novel contrasts. However, the interactions of fields in the body need to be carefully considered to ensure safe scanning. Recent advances in physically flexible body coils have allowed for high-field abdominal imaging, but the effects of increased variability on energy deposition need further exploration. The aim of this study was to assess the impact of subject geometry, respiration phase and coil positioning on the specific absorption rate (SAR). Ten healthy subjects (body mass index[BMI] = 25 ± 5 kg m-2 ) were scanned (at 3 T) during exhale breath-hold and images used to generate body models. Seven of these subjects were also scanned during inhale. Simplifications of the coil and body models were first explored, and then finite-difference time-domain simulations were run with a typical eight-channel parallel transmit coil positioned over the abdomen. Simulations were used to generate 10 g averaged SAR (SAR10g ) maps across 100,000 phase settings, and the worst-case scenario 10 g averaged SAR (wocSAR10g ) was identified using trigonometric maximisation. The average maximum SAR10g across the 10 subjects with 1 W input power per channel was 1.77 W kg-1 . Hotspots were always close to the body surface near the muscle wall boundary. The wocSAR10g across the 10 subjects ranged from 2.3 to 3.2 W kg-1 and was inversely correlated to fat volume percentage (R = 8) and BMI (R = 0.6). The coefficient of variation values in SAR10g due to variations in subject geometry, respiration phase and realistic coil repositioning were 12%, 4% and 12%, respectively. This study found that the variability due to realistic coil repositioning was similar to the variability due to differing healthy subject geometries for abdominal imaging. This is important as it suggests that population-based modelling is likely to be more useful than individual modelling in setting safe thresholds for abdominal imaging.