Purpose: Patients who have medical metallic implants, e.g. orthopaedic implants and pacemakers, often cannot undergo an MRI exam. One of the largest risks is tissue heating due to the radio frequency (RF) fields. The RF safety assessment of implants is computationally demanding. This is due to the large dimensions of the transmit coil compared to the very detailed geometry of an implant. Methods: In this work, we explore a faster computational method for the RF safety assessment of implants that exploits the small geometry. The method requires the RF field without an implant as a basis and calculates the perturbation that the implant induces. The inputs for this method are the incident fields and a library matrix that contains the RF field response of every edge an implant can occupy. Through a low-rank inverse update, using the Sherman–Woodbury–Morrison matrix identity, the EM response of arbitrary implants can be computed within seconds. We compare the solution from full-wave simulations with the results from the presented method, for two implant geometries. Results: From the comparison, we found that the resulting electric and magnetic fields are numerically equivalent (maximum error of 1.35%). However, the computation was between 171 to 2478 times faster than the corresponding GPU accelerated full-wave simulation. Conclusions: The presented method enables for rapid and efficient evaluation of the RF fields near implants and might enable situation-specific scanning conditions.

BACKGROUND AND PURPOSE: In recent years, several high-resolution vessel wall MR imaging techniques have emerged for the characterization of intracranial atherosclerotic vessel wall lesions in vivo. However, a thorough validation of MR imaging results of intracranial plaques with histopathology is still lacking. The aim of this study was to characterize atherosclerotic plaque components in a quantitative manner by obtaining the MR signal characteristics (T1, T2, T2∗, and proton density) at 7T in ex vivo circle of Willis specimens and using histopathology for validation. MATERIALS AND METHODS: A multiparametric ultra-high-resolution quantitative MR imaging protocol was performed at 7T to identify the MR signal characteristics of different intracranial atherosclerotic plaque components, and using histopathology for validation. In total, 38 advanced plaques were matched between MR imaging and histology, and ROI analysis was performed on the identified tissue components. RESULTS: Mean T1, T2, and T2∗ relaxation times and proton density values were significantly different between different tissue components. The quantitative T1 map showed the most differences among individual tissue components of intracranial plaques with significant differences in T1 values between lipid accumulation (T1 = 838 ± 167 ms), fibrous tissue (T1 = 583 ± 161 ms), fibrous cap (T1 = 481 ± 98 ms), calcifications (T1 = 314 ± 39 ms), and the intracranial arterial vessel wall (T1 = 436 ± 122 ms). CONCLUSIONS: Different tissue components of advanced intracranial plaques have distinguishable imaging characteristics with ultrahigh-resolution quantitative MR imaging at 7T. Based on this study, the most promising method for distinguishing intracranial plaque components is T1-weighted imaging.