RESUMO
Understanding the relationship between fiducial registration error (FRE) and target registration error (TRE) is important for the correct use of interventional guidance systems. Whilst it is well established that TRE is statistically independent of FRE, system users still struggle against the intuitive assumption that a low FRE indicates a low TRE. We present the SciKit-Surgery Fiducial Registration Educational Demonstrator and describe its use. SciKit-SurgeryFRED was developed to enable remote teaching of key concepts in image registration. SciKit-SurgeryFRED also supports research into user interface design for image registration systems. SciKit-SurgeryFRED can be used to enable remote tutorials covering the statistics relevant to image guided interventions. Students are able to place fiducial markers on pre and intra-operative images and observe the effects of changes in marker geometry, marker count, and fiducial localisation error on TRE and FRE. SciKit-SurgeryFRED also calculates statistical measures for the expected values of TRE and FRE. Because many registrations can be performed quickly the students can then explore potential correlations between the different statistics. SciKit-SurgeryFRED also implements a registration based game, where participants are rewarded for complete treatment of a clinical target, whilst minimising the treatment margin. We used this game to perform a remote study on registration and simulated ablation, measuring how user performance changes depending on what error statistics are made available. The results support the assumption that knowing the exact value of target registration error leads to better treatment. Display of other statistics did not have a significant impact on the treatment performance.
RESUMO
OBJECTIVE: Methods have previously been reported for simultaneous EIT and EEG recording, but these have relied on post-hoc signal processing to remove switching artefacts from the EEG signal and require dedicated hardware filters and the use of separate EEG and EIT electrodes. This work aims to demonstrate that an uncorrupted EEG signal can be collected simultaneously with EIT data by using frequency division multiplexing (FDM), and to show that the EIT data provides useful information when compared to EEG source localisation. APPROACH: A custom FDM EIT current source was created and evaluated in resistor phantom and neonatal head tank experiments, where a static and dynamic perturbation was imaged. EEG and EIT source localisation were compared when an EEG dipole was placed in the tank. EEG and EIT data were collected simultaneously in a human volunteer, using both a standard EEG and a visual evoked potential (VEP) paradigms. MAIN RESULTS: Differences in EEG and VEP collected with and without simultaneous EIT stimulation showed no significant differences in amplitude, latency or PSD (p-values >0.3 in all cases). Compared with EEG source localisation, EIT reconstructions were more accurately able to reconstruct both the centre of mass and volume of a perturbation. SIGNIFICANCE: The reported method is suitable for collecting EIT in a clinical setting, without disrupting the clinical EEG or requiring additional measurement electrodes, which lowers the barrier to entry for data collection. EIT collection can be integrated with existing clinical workflows in EEG/ECoG, with minimal disruption to the patient or clinical team.