Abstract
Purpose.Although full-wave simulations could be used to aid in RF coil design, the algorithms may be too slow for an iterative optimization algorithm. If quasistatic simulations are accurate within the design tolerance, then their use could reduce simulation time by orders of magnitude compared to full-wave simulations. This paper examines the accuracy of quasistatic and full-wave simulations at 3 Tesla.Methods.Three sets of eight coils ranging from 3–10 cm (24 total) were used to measure SNR on three phantoms with conductivities of 0.3, 0.6, and 0.9 S/m. The phantom conductivities were chosen to represent those typically found in human tissues. The range of coil element sizes represents the sizes of coil elements seen in typical coil designs. SNR was determined using the magnetic and electric fields calculated by quasistatic and full-wave simulations. Each simulated SNR dataset was scaled to minimize the root mean squared error (RMSE) when compared against measured SNR data. In addition, the noise values calculated by each simulation were compared against benchtop measured noise values.Results.The RMSE was 0.285 and 0.087 for the quasistatic and full-wave simulations, respectively. The maximum and minimum quotient values, when taking the ratio of simulated to measured SNR values, were 1.69 and 0.20 for the quasistatic simulations and 1.29 and 0.75 for the full-wave simulations, respectively. The ratio ranges, for the calculated quasistatic and full-wave total noise values compared to benchtop measured noise values, were 0.83–1.06 and 0.27–3.02, respectively.Conclusions.Full-wave simulations were on average 3x more accurate than the quasistatic simulations. Full-wave simulations were more accurate in characterizing the wave effects within the sample, though they were not able to fully account for the skin effect when calculating coil noise.
Highlights
IntroductionE process can be improved if, before coil construction and testing on the scanner, simulations are performed to allow the coil designer to estimate signal-to-noise ratio (SNR) in the region of interest (ROI), so fewer or no iterations are required during the physical construction and testing phase of the design process [2]
RF coil design for magnetic resonance imaging (MRI) applications is often an empirical process where the coil designer iteratively modifies and tests an RF-phased array coil design on the scanner until a satisfactory signal-to-noise ratio (SNR) is achieved in the region of interest (ROI) [1].e process can be improved if, before coil construction and testing on the scanner, simulations are performed to allow the coil designer to estimate SNR in the ROI, so fewer or no iterations are required during the physical construction and testing phase of the design process [2]
Quasistatic solutions assume that no radiation or wave effects are present and that “the system is small compared to the electromagnetic wavelength associated with the dominant time scale [9].” ere are three major quasistatic models for electromagnetics. ey are the EQS, MQS, and the Darwin model [9,24]
Summary
E process can be improved if, before coil construction and testing on the scanner, simulations are performed to allow the coil designer to estimate SNR in the ROI, so fewer or no iterations are required during the physical construction and testing phase of the design process [2]. Hoult was the first to publish MRI signal reception theory that demonstrated how to derive MRI signal calculations with phase changes accounted for in the sample [3]. Roemer et al were the first to derive a method for calculating the SNR when reconstructing a composite image from a phased array with multiple coil elements [8]
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