Use of 3-D magnetic resonance electrical impedance tomography in detecting human cerebral stroke: a simulation study

Abstract

Abstract:We have developed a new three dimensional (3-D) conductivity imaging approach and have used it to detect human brain conductivity changes corresponding to acute cerebral stroke. The proposed Magnetic Resonance Electrical Impedance Tomography (MREIT) approach is based on the J-Substitution algorithm and is expanded to imaging 3-D subject conductivity distribution changes. Computer simulation studies have been conducted to evaluate the present MREIT imaging approach. Simulations of both types of cerebral stroke, hemorrhagic stroke and ischemic stroke, were performed on a four-sphere head model. Simulation results showed that the correlation coefficient (CC) and relative error (RE) between target and estimated conductivity distributions were 0.9245±0.0068 and 8.9997%±0.0084%, for hemorrhagic stroke, and 0.6748±0.0197 and 8.8986%±0.0089%, for ischemic stroke, when the SNR (signal-to-noise radio) of added GWN (Gaussian White Noise) was 40. The convergence characteristic was also evaluated according to the changes of CC and RE with different iteration numbers. The CC increases and RE decreases monotonously with the increasing number of iterations. The present simulation results show the feasibility of the proposed 3-D MREIT approach in hemorrhagic and ischemic stroke detection and suggest that the method may become a useful alternative in clinical diagnosis of acute cerebral stroke in humans.

Induced current magnetic resonance electrical impedance tomography of brain tissues based on the J-substitution algorithm: a simulation study

“…Accordingly, the conductivity distribution reconstructed from equation (11) differs from the true distribution by a scale factor. In order to image the absolute conductivity distribution, the boundary voltage data must be introduced into the update formula (Kwon et al 2002, İder et al 2003, Gao et al 2005): $$\sigma =\frac{\mid {\mathbf{J}}^{\ast }\mid }{\mid \mathbf{E}\mid }\frac{V_{\sigma }}{V_{{\sigma }^{\ast }}}$$ where V σ* is the measured voltage difference and V σ is the calculated voltage difference when the conductivity is σ .…”

Advances in electrical impedance tomography-based brain imaging

“…MREIT enables the generation of high-resolution electrical conductivity images and the measurement of magnetic flux density in three-dimensional space, without being severely affected by the low conductivity of the skull. Various studies have applied MREIT in cancer imaging, assessed the feasibility of three-dimensional MREIT for detecting hemorrhagic and ischemic strokes, and employed non-invasive magnetic flux density measurements to estimate intracranial conductivity [105,106,[137][138][139][140]. As commonly practiced in medical imaging, cross-modal approaches have become a trend in bioelectrical impedance imaging research.…”

Noninvasive Imaging of Head-Brain Conductivity Profiles

“…In this short paper, we review our efforts in MREIT imaging of electrical conductivity of human head and brain [15–16, 19–20, 22–26]. Our research was motivated by the need of non-invasively estimating conductivity profiles of the head tissue for solving the forward and inverse problems of electroencephalogram (EEG) and magnetoencephalogram (MEG) [1–2, 27].…”

“…The standard deviation of noise s B was given by s B = 1/2 γT c SNR , where γ is the gyromagnetic ratio of hydrogen, T is the duration of the injection current pulse, and SNR is the signal-to-noise ratio of the MR magnitude image which was set to be 80, 60, 40, 20 and 15, respectively. In order to assess the clinical applicability, GWNs with standard deviation of 5mm and 10mm [15–16] were also added to the electrode positions to simulate the effects of electrode position uncertainty.…”

“…For example, cell swelling reduces the extracellular space and increases the bulk tissue impedance as much as 50%, therefore the ischemic tissue can be characterized by abnormally high impedance [16]. We have examined the feasibility of using MREIT to detect conductivity changes associated with brain pathological conditions, and the two-step method [22] was utilized: the inhomogeneous conductivity distribution σ of the encephalic pathological tissue, which was assumed to be homogeneous, was first estimated by the RBF-MREIT algorithm; then the genetic algorithm (GA) was applied to estimate ${\sigma }_{\mathit{tissue}}^i$ for each element within each tissue of the FEM head model [24].…”