Multifrequency electrical impedance tomography with total variation regularization

Abstract

Abstract. Multifrequency Electrical Impedance Tomography (MFEIT) reconstructs the distribution of conductivity by exploiting the dependence of tissue conductivity on frequency. MFEIT can be performed on a single instance of data, making it promising for applications such as stroke and cancer imaging, where it is not possible to obtain a 'baseline' measurement of healthy tissue. A nonlinear MFEIT algorithm able to reconstruct the volume fraction distribution of tissue rather than conductivities has been developed previously. For each volume, the fraction of a certain tissue should be either 1 or 0; this implies that the sharp changes of the fractions, representing the boundaries of tissue, contain all the relevant information However, these boundaries are blurred by traditional regularisation methods employing 2 l norm. The Total Variation (TV) regularisation can overcome this problem, but it is difficult to solve due to its non-differentiability. As the fraction must be between 0 and 1, this imposes a constraint on the MFEIT method based on the fraction model. Therefore, a constrained optimisation method capable of dealing with non-differentiable problems is required. We propose a new constrained TV regularised method, to solve the fraction reconstruction problem, based on the Primal and Dual Interior Point Method (PDIPM) method. The noise performance of the new MFEIT method is analysed using simulations on a 2D cylindrical mesh. Convergence performance is also analysed, through experiments using a cylindrical tank. Finally, simulations on an anatomically realistic head-shaped mesh are demonstrated. The proposed MFEIT method with TV regularisation shows higher spatial resolution, particularly at the edges of the perturbation, and stronger noise robustness, and its image noise and shape error are 20% to 30% lower than the traditional fraction method.

Multi-frequency electrical impedance tomography and neuroimaging data in stroke patients

“…Signal to noise ratio (SNR) is important factor in determining the performance of both conventional “Time difference” and Multi Frequency EIT methods. In simulation, MF images have been successfully reconstructed with an SNR of 30 dB 29 , and with noise equivalent to 44 to 48 dB 19 . However, these MFEIT algorithms are particularly sensitive to spectral errors, or systematic errors across frequency.…”

In Vivo Bioimpedance Spectroscopy Characterization of Healthy, Hemorrhagic and Ischemic Rabbit Brain within 10 Hz–1 MHz

“…Unlike td-EIT, MFEIT does not need any reference or baseline data acquired at other time points. Since there are difference in impedance spectra between normal, hemorrhagic and ischemic brain tissues [8], MFEIT shows promise in becoming an imaging modality that can quickly detect stroke and can also be used to identify stroke subtypes [4,9,10,11]. …”

Exploratory study of a multifrequency EIT-based method for detecting intracranial abnormalities

“…The advantage of frequency-differential EIT over temporal differential EIT is that the former can reflect intracranial abnormalities in a one-time imaging manner without a reference EIT data corresponding to the time before the onset of brain diseases, which is helpful to obtain the patient's intracranial condition in time. The electrical impedance group of UCL University (18,(23)(24)(25) and the electrical impedance group of the Fourth Military Medical University in China (26,27) have conducted in depth studies of cerebral MFEIT, and the research strategy of selecting frequency bands of fdEIT based on the dielectric properties of biological tissues has been proven successful through physical and animal model studies (17). However, studies on the characteristics of fdEIT images and the differences in those characteristics between healthy subjects and patients with brain diseases have not been reported.…”