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Low-frequency conductivity tensor imaging with a single current injection using DT-MREIT

Sadighi, Mehdi; Sisman, Mert; Acikgoz, Berk C.; Eroglu, Hasan H.; Eyuboglu, B. Murat


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        "name": "Sadighi, Mehdi"
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      {
        "affiliation": "Middle East Tech Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey", 
        "name": "Sisman, Mert"
      }, 
      {
        "affiliation": "Middle East Tech Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey", 
        "name": "Acikgoz, Berk C."
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      {
        "name": "Eroglu, Hasan H."
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      {
        "affiliation": "Middle East Tech Univ, Dept Elect & Elect Engn, TR-06800 Ankara, Turkey", 
        "name": "Eyuboglu, B. Murat"
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    "description": "Diffusion tensor-magnetic resonance electrical impedance tomography (DT-MREIT) is an imaging modality to obtain low-frequency anisotropic conductivity distribution employing diffusion tensor imaging and MREIT techniques. DT-MREIT is based on the linear relationship between the conductivity and water self-diffusion tensors in a porous medium, like the brain white matter. Several DT-MREIT studies in the literature provide cross-sectional anisotropic conductivity images of tissue phantoms, canine brain, and the human brain. In these studies, the conductivity tensor images are reconstructed using the diffusion tensor and current density data acquired by injecting two linearly independent current patterns. In this study, a novel reconstruction algorithm is devised for DT-MREIT to reconstruct the conductivity tensor images using a single current injection. Therefore, the clinical applicability of DT-MREIT can be improved by reducing the total acquisition time, the number of current injection cables, and contact electrodes to half by decreasing the number of current injection patterns to one. The proposed method is evaluated utilizing simulated measurements and physical experiments. The results obtained show the successful reconstruction of the anisotropic conductivity distribution using the proposed single current DT-MREIT.", 
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