Detecting cryoablation with EIT and the benefit of including ice front imaging data

F, Jon; Rubinsky, Boris

doi:10.1088/0967-3334/27/5/s15

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…This would also prevent the elevated specific impedance magnitude near the ice front from causing the ice front position to be overestimated (Edd et al 2005) while retaining the sharp transition in properties at the ice front. For imaging viability post-thaw, resolution can be enhanced by incorporating the prior knowledge of the ice front position (Edd and Rubinsky 2006) since unfrozen tissue seems little affected by nearby freezing (see figure 6(d)), however care must be taken that changes in conductivity outside of the frozen region are not prevented but that they are assumed to be less likely to occur.…”

Section: Discussionmentioning

confidence: 99%

“…Functional MRI also presents a possible solution (Preece et al 2001); however, it also incurs a high cost and long acquisition time. EIT has recently been proposed as a method for detecting tissue viability after cryosurgery (Davalos and Rubinsky 2004; Edd and Rubinsky 2006). This is based on the principle that cell membrane integrity, and hence viability, influences greatly the electrical conductivity at frequencies below the β dielectric dispersion (Grimnes and Martinsen 2000).…”

Section: Introductionmentioning

confidence: 99%

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Imaging cryosurgery with EIT: tracking the ice front and post-thaw tissue viability

Ivorra

Horowitz

et al. 2008

Physiol. Meas.

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Cryosurgery employs freezing for targeted destruction of undesirable tissues such as cancer. Ice front imaging has made controlled treatment of deep body tumors possible. One promising method, recently explored for this task, is EIT, which recovers images of electrical impedance from measurements made at boundary electrodes. However, since frozen tissue near the ice front survives, ice front imaging is insufficient. Monitoring treatment effect would enable iterative cryosurgery, where extents of ablation and need for further treatment are assessed upon thawing. Since lipid bilayers are strong barriers to low frequency electrical current and cell destruction implies impaired membranes, EIT should be able to detect the desired effect of cryosurgery: cell death. Previous work has tested EIT for ice front imaging with tank studies while others have simulated EIT in detecting cryoablation, but in vivo tests have not been reported in either case. To address this, we report 3D images of differential conductivity throughout the freeze-thaw cycle in a rat liver model in vivo with histological validation, first testing our system for ice front imaging in a gel and for viability imaging post-thaw in a raw potato slice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Imaging cryosurgery with EIT: tracking the ice front and post-thaw tissue viability

Ivorra

Horowitz

et al. 2008

Physiol. Meas.

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“…This ratio is typical (Smith et al 1986, Miklavcic et al 2006. When the effect of noise was examined we used a noise parameter A of 0.1%, as in Edd and Rubinsky (2006). The results shown here were chosen from a large number of tests for their ability to illustrate the concept and demonstrate its feasibility.…”

Section: Resultsmentioning

confidence: 99%

“…where v is a vector of normally distributed random numbers, with mean zero and standard deviation one (the same dimensions as the V 0 vector), and A is the noise level (Edd and Rubinsky 2006). In our simulations A = 0.1%.…”

Section: Methodsmentioning

confidence: 99%

Multiple biopsy probe sampling enabled minimally invasive electrical impedance tomography

Shini

Rubinsky²

2008

Physiol. Meas.

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Biopsies are a reliable method for examining tissues and organs inside the body, in particular for detection of tumors. However, a single biopsy produces only limited information on the site from which it is taken. Therefore, tumor detection now employs multiple biopsy samplings to examine larger volumes of tissue. Nevertheless, even with multiple biopsies, the information remains discrete, while the costs of biopsy increase. Here we propose and evaluate the feasibility of using minimally invasive medical imaging as a means to overcome the limitations of discrete biopsy sampling. The minimally invasive medical imaging technique employs the biopsy probe as electrodes for measurements of electrical impedance tomography relevant data during each biopsy sampling. The data from multiple samplings are combined and used to produce an EIT image of the tissue. Two- and three-dimensional mathematical simulations confirm that the minimally invasive medical imaging technique can produce electrical impedance tomography images of the tissues between the biopsy probe insertion sites. We show that these images can detect tumors that would be missed with multiple biopsy samplings only, and that the technique may facilitate the detection of tumors with fewer biopsies, thereby reducing the cost of cancer detection.

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“…where v is a vector of normally distributed random numbers, with a mean of zero and a standard deviation of one (the same dimensions as the V 0 vector), and A is the noise level (Edd and Rubinsky 2006). Three noise levels were examined in this study: 0.1%, 1% and 2%.…”

Section: Noise Analysismentioning

confidence: 99%

SVM for prostate cancer using electrical impedance measurements

2011

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Biopsies are currently the 'gold standard' method for identifying cancer of the prostate. While biopsies yield very accurate information regarding the area they sample, they are performed at discrete points and provide no information on the adjacent tissue. To enhance procedural accuracy, biopsies of a large number of sites are routinely carried out. Although more accurate, this method is both more complex and nevertheless remains discrete. In this paper, we evaluate the advantages of using bio-impedance information as the input for a support vector machines (SVMs) classifier to overcome these limitations. In this method, the biopsy probes are used as electrodes to obtain electrical impedance data during each biopsy sample. Using a computer model of the prostate, a SVM was trained and tested. Different tumor shapes and conductivity values, and the classifier's ability to generalize to these different properties, were examined. We demonstrate that by using this classifier the number of biopsies can be reduced and valuable information concerning the adjacent tissue which was not biopsied can be generated.

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Detecting cryoablation with EIT and the benefit of including ice front imaging data

Cited by 11 publications

References 18 publications

Imaging cryosurgery with EIT: tracking the ice front and post-thaw tissue viability

Imaging cryosurgery with EIT: tracking the ice front and post-thaw tissue viability

Multiple biopsy probe sampling enabled minimally invasive electrical impedance tomography

SVM for prostate cancer using electrical impedance measurements

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