Exploratory Study on the Methodology of Fast Imaging of Unilateral Stroke Lesions by Electrical Impedance Asymmetry in Human Heads

Stroke has a high mortality and disability rate and should be rapidly diagnosed to improve prognosis. Diagnosing stroke is not a problem for hospitals with CT, MRI, and other imaging devices but is difficult for community hospitals without these devices. Based on the mechanism that the electrical impedance of the two hemispheres of a normal human head is basically symmetrical and a stroke can alter this symmetry, a fast electrical impedance imaging method called symmetrical electrical impedance tomography (SEIT) is proposed. In this technique, electrical impedance tomography (EIT) data measured from the undamaged craniocerebral hemisphere (CCH) is regarded as reference data for the remaining EIT data measured from the other CCH for difference imaging to identify the differences in resistivity distribution between the two CCHs. The results of SEIT imaging based on simulation data from the 2D human head finite element model and that from the physical phantom of human head verified this method in detection of unilateral stroke

Advances of deep learning in electrical impedance tomography image reconstruction

Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future

Observation of Majorana fermions with spin selective Andreev reflection in the vortex of topological superconductor

Majorana fermion (MF) whose antiparticle is itself has been predicted in condensed matter systems. Signatures of the MFs have been reported as zero energy modes in various systems. More definitive evidences are highly desired to verify the existence of the MF. Very recently, theory has predicted MFs to induce spin selective Andreev reflection (SSAR), a novel magnetic property which can be used to detect the MFs. Here we report the first observation of the SSAR from MFs inside vortices in Bi2Te3/NbSe2 hetero-structure, in which topological superconductivity was previously established. By using spin-polarized scanning tunneling microscopy/spectroscopy (STM/STS), we show that the zero-bias peak of the tunneling differential conductance at the vortex center is substantially higher when the tip polarization and the external magnetic field are parallel than anti-parallel to each other. Such strong spin dependence of the tunneling is absent away from the vortex center, or in a conventional superconductor. The observed spin dependent tunneling effect is a direct evidence for the SSAR from MFs, fully consistent with theoretical analyses. Our work provides definitive evidences of MFs and will stimulate the MFs research on their novel physical properties, hence a step towards their statistics and application in quantum computing.Comment: 4 figures 15 page

Angle Dependent Van Hove Singularities in Slightly Twisted Graphene Bilayer

Recent studies show that two low-energy Van Hove singularities (VHSs) seen as two pronounced peaks in the density of states (DOS) could be induced in twisted graphene bilayer. Here, we report angle dependent VHSs of slightly twisted graphene bilayer studied by scanning tunneling microscopy and spectroscopy. We show that energy difference of the two VHSs follows \DeltaEvhs ~ \hbar{\nu}F\DeltaK between 1.0^{\circ} and 3.0^{\circ} (here {\nu}F ~ 1.1\times106 m/s is the Fermi velocity of monolayer graphene, \DeltaK = 2Ksin(\theta/2) is the shift between the corresponding Dirac points of the twisted graphene bilayer). This result indicates that the rotation angle between graphene sheets not results in significant reduction of the Fermi velocity, which quite differs from that predicted by band structure calculations. However, around a twisted angle \theta ~ 1.3^{\circ}, the observed \DeltaEvhs ~ 0.11 eV is much less than the expected value \hbar{\nu}F\DeltaK ~ 0.28 eV at 1.3^{\circ}. The origin of the reduction of \DeltaEvhs at 1.3^{\circ} is discussed.Comment: To appear in Phys. Rev. Lett. (2012

Use of electrical impedance tomography to monitor dehydration treatment of cerebral edema: a clinical study

In this study EIT was used to monitor brain impedance changes due to variations in cerebral fluid content during dehydration treatment of edema patients. 30 patients with cerebral edema were continuously imaged for two hours after the initiation of dehydration treatment. Results show that overall impedance across the brain increased significantly 5 minutes after dehydration treatment started. And different brain tissues have different reactions towards dehydration

Various atomic structures of monolayer silicene fabricated on Ag(111)

Silicene, a monolayer of silicon atoms arranged in honeycomb lattices, can be synthesized on the Ag(111) surface, where it forms several superstructures with different buckling patterns and periodicity. Using scanning tunneling microscopy (STM), we obtained high-resolution images of silicene grown on Ag(111) and revealed its five phases, i.e., 4 × 4 − α, 4 × 4 − β, − α, − β and − γ, some observed for the first time. For each of the phases, we have determined its atomic structure by comparing the atomic-resolution STM images with theoretical simulation results previously reported. We thus eliminate the contradictions of previous studies on the structural models of various silicene phases supported by the Ag(111) surface.EC/FP7/270749/EU/Strongly anisotropic Graphite-like semiconductor/dielectric 2D nanolattices/2D-NANOLATTICE