A combined computational and experimental approach to assess the transfer function of real pacemaker leads for MR radiofrequency-induced heating.

OBJECTIVE: To propose and validate a variation of the classic techniques for the estimation of the transfer function (TF) of a real pacemaker (PM) lead. METHODS: The TF of three commercially available PM leads was measured by combining data from experimental measurements and numerical simulations generated by three sources: a) the experimental local SAR at the tip of the PM lead (single measurement point) exposed to a 64 MHz birdcage body coil; b) the experimental current distribution along the PM lead, obtained by directly injecting a 64 MHz signal inside the lead; c) the electric field (E-field) simulated with a computational model of the 64 MHz birdcage body coil adopted in the experimental measurement performed in a). The effect of the lead trajectory on the estimation of the TF was also estimated. RESULTS: The proposed methodology was validated by comparing the SAR obtained from the PM lead TF with experimental measurements: a maximum difference of 2.2 dB was observed. It was also shown that the estimation of the TF cannot be considered independent with the lead trajectory: a variation of the SAR estimation up to 3.4 dB was observed. CONCLUSION: For the three PM lead tested, the error in the SAR estimation is within the uncertainty level of SAR measurements (± 2 dB). Additionally, the estimation of the TF using the reciprocity principle is influenced by the particular lead trajectory adopted, even if the consequent variability in the SAR estimation is still close to the uncertainty level of SAR measurements.

Multimodal integration of high-resolution EEG and functional magnetic resonance imaging data: a simulation study.

Previous simulation studies have stressed the importance of the use of fMRI priors in the estimation of cortical current density. However, no systematic variations of signal-to-noise ratio (SNR) and number of electrodes were explicitly taken into account in the estimation process. In this simulation study we considered the utility of including information as estimated from fMRI. This was done by using as the dependent variable both the correlation coefficient and the relative error between the imposed and the estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were the different values of SNR of the scalp-generated data, the different inverse operators used to estimated the cortical source activity, the strengths of the fMRI priors in the fMRI-based inverse operators, and the number of scalp electrodes used in the analysis. Analysis of variance results suggested that all the considered factors significantly afflict the correlation and the relative error between the estimated and the simulated cortical activity. For the ROIs analyzed with simulated fMRI hot spots, it was observed that the best estimation of cortical source currents was performed with the inverse operators that used fMRI information. When the ROIs analyzed do not present fMRI hot spots, both standard (i.e., minimum norm) and fMRI-based inverse operators returned statistically equivalent correlation and relative error values.