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Glassy carbon microelectrodes minimize induced voltages, mechanical vibrations, and artifacts in magnetic resonance imaging.

ABSTRACT: The recent introduction of glassy carbon (GC) microstructures supported on flexible polymeric substrates has motivated the adoption of GC in a variety of implantable and wearable devices. Neural probes such as electrocorticography and penetrating shanks with GC microelectrode arrays used for neural signal recording and electrical stimulation are among the first beneficiaries of this technology. With the expected proliferation of these neural probes and potential clinical adoption, the magnetic resonance imaging (MRI) compatibility of GC microstructures needs to be established to help validate this potential in clinical settings. Here, we present GC microelectrodes and microstructures-fabricated through the carbon micro-electro-mechanical systems process and supported on flexible polymeric substrates-and carry out experimental measurements of induced vibrations, eddy currents, and artifacts. Through induced vibration, induced voltage, and MRI experiments and finite element modeling, we compared the performances of these GC microelectrodes against those of conventional thin-film platinum (Pt) microelectrodes and established that GC microelectrodes demonstrate superior magnetic resonance compatibility over standard metal thin-film microelectrodes. Specifically, we demonstrated that GC microelectrodes experienced no considerable vibration deflection amplitudes and minimal induced currents, while Pt microelectrodes had significantly larger currents. We also showed that because of their low magnetic susceptibility and lower conductivity, the GC microelectrodes caused almost no susceptibility shift artifacts and no eddy-current-induced artifacts compared to Pt microelectrodes. Taken together, the experimental, theoretical, and finite element modeling establish that GC microelectrodes exhibit significant MRI compatibility, hence demonstrating clear clinical advantages over current conventional thin-film materials, further opening avenues for wider adoption of GC microelectrodes in chronic clinical applications.

SUBMITTER: Nimbalkar S

PROVIDER: S-EPMC6859162 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSegmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).MethodsIn this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability.ResultsIn the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF - 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s).ConclusionsExperimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.

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Glassy carbon microelectrodes minimize induced voltages, mechanical vibrations, and artifacts in magnetic resonance imaging.

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