Multifrequency Electrical Impedance Tomography Using Spectral Constraints

Malone, Emma; Santos, Gustavo Sato dos; Holder, David; Arridge, Simon

doi:10.1109/tmi.2013.2284966

Cited by 89 publications

(85 citation statements)

References 20 publications

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“…WFD has been proven that it can reconstruct a perturbation in a frequency variant background (Seo et al, 2008, Jun et al, 2009a, Ahn et al, 2011, but as a linear algorithm its application is restricted by the availability of linearised approximation (Jun et al, 2009). For example, WFD cannot reconstruct proper images if the area of perturbation is too large (Malone et al, 2014b). A method for performing MFEIT using Spectral Constraints (MFEITSC) was proposed by Malone et al, (2014bMalone et al, ( , 2014a.…”

Section: Mfeit Methodsmentioning

confidence: 99%

“…For example, WFD cannot reconstruct proper images if the area of perturbation is too large (Malone et al, 2014b). A method for performing MFEIT using Spectral Constraints (MFEITSC) was proposed by Malone et al, (2014bMalone et al, ( , 2014a. The inverse problem was modified by substituting the conductivity with the volume fraction of each tissue in each voxel, describing the physical distribution of tissues in the domain.…”

Section: Mfeit Methodsmentioning

confidence: 99%

“…This method allows for the quantitative recovery of tissue fraction values by a non-linear reconstruction method. This approach differs significantly from linear techniques, and does not require frequency weighting such as that employed in WFD imaging (Malone et al, 2014b).…”

Section: Mfeit Methodsmentioning

confidence: 99%

“…Experiments were undertaken using two algorithms: the existing fraction algorithm (Malone et al, 2014b), and the aforementioned novel TV regularised fraction algorithm. First, a simulation using a cylindrical model was considered, using different noise levels to test the noise performance of the algorithms.…”

Section: Experimental Designmentioning

confidence: 99%

“…As such, MFEIT offers a potential solution, as images can be produced from data collected at a single given point in time. However, MFEIT application is more challenging due to the high sensitivity of its solution to modelling and instrumentation errors (Kolehmainen et al, 1997;McEwan et al, 2007;Malone et al, 2014b) .…”

Section: Introductionmentioning

confidence: 99%

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Multifrequency electrical impedance tomography with total variation regularization

et al. 2015

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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.

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