Electrical excitation and detection of magnetic dynamics with impedance matching

Magnetic detection electrical impedance tomography with total variation regularization.

Magnetic detection electrical impedance tomography (MDEIT) is an imaging modality that aims to reconstruct the cross-sectional conductivity distribution of a volume from the magnetic flux density surrounding an object. The MDEIT inverse problem is inherently ill-posed, necessitating the use of regularization. The most commonly used L(2) norm regularizations generate the minimum energy solution,...

Induced current magnetic resonance-electrical impedance tomography.

Magnetic resonance-electrical impedance tomography (MR-EIT) is a conductivity imaging method based on injecting currents into the object. In this study, a new MR-EIT method, whereby currents are induced inside the object by using external coils, is proposed. This new method is called induced current magnetic resonance-electrical impedance tomography. In induced current MR-EIT surface electrodes...

Electrical conductivity imaging by magnetic resonance electrical impedance tomography (MREIT).

Magnetic resonance electrical impedance tomography (MREIT) is a recently developed imaging technique that combines MRI and electrical impedance tomography (EIT). In MREIT, cross-sectional electrical conductivity images are reconstructed from the internal magnetic field density data produced inside an electrically conducting subject when an electrical current is injected into the subject. In thi...

Applications of Electrical Impedance Tomography in Neurology

Introduction: Electrical impedance tomography (EIT) is a non-invasive technique utilized in various medical applications, including brain imaging and other neurological diseases. Recognizing the physiological and anatomical characteristics of organs based on their electrical properties is one of the main applications of EIT, as each variety of tissue structure has its own electrical characteris...

Fast Magnetic Resonance Electrical Impedance Tomography with Highly Undersampled Data