Electrical impedance mammography

TECHNOLOGICAL FIELD

Embodiments of the present disclosure relate to electrical impedance spectroscopy, electrical impedance imaging, Electrical impedance mammography.

Embodiments of the present invention relate to an apparatus, computer program and method for electrical impedance imaging. In particular, they relate to an apparatus, computer program and method for electrical impedance imaging of the female human breast to facilitate the detection of changes within the breast mass, including changes such as tumors, abnormalities, and malignant changes, such as carcinomas, in the breast.

BACKGROUND

Electrical impedance mammography (EIM), or Electrical impedance imaging (Ell), also referred to as electrical impedance tomography (EIT)1 electrical impedance scanner (EIS) and applied potential tomography (APT), is an imaging technique that is particularly used in medical applications.

The technique images the spatial distribution of electrical impedance inside an object, such as the human body. The technique is attractive as a medical diagnostic tool because it is non-invasive and does not use ionizing radiation as in X-ray tomography or the generation of strong, highly uniform magnetic fields as in Magnetic Resonance Imaging (MRI).

Typically a two-dimensional (2D) or three-dimensional (3D) array of evenly spaced electrodes is attached to the object to be imaged about the region of interest (ROI). Either input voltages are applied across pairs of 'input' electrodes and output electric currents are measures at the 'output' electrodes or input electric currents are applied between pairs of 'input' electrodes and output voltages are measured between at the 'output' electrodes or between pairs of output electrodes. For example, when a very small alternating electric current is applied between a pair of 'input' electrodes, the potential difference between all other pairs of 'output' electrodes is measured. The current is then applied between a different pair of 'input' electrodes and the potential difference between all other pairs of 'output' electrodes is measured. Image is constructed using an appropriate image reconstruction technique.

Spatial variations revealed in electrical impedance images may result from variations in impedance between healthy and non-healthy tissues, variations in impedance between different tissues and organs or variations in apparent impedance due to anisotropic effects resulting for example from muscle alignment.

Tissue or cellular changes associated with cancer cause significant localized variations in electrical impedance and can be imaged. WO 00/12005 discloses an example of electrical impedance imaging apparatus that can be used to detect breast carcinomas or other carcinoma.

Variations of the individual impedance properties may be used to analyze the structure of an object. For example, in the case of human tissue, variations in the individual impedance properties may be indicative of the presence of an abnormality as this gives rise to electrical characteristics which are different to those exhibited by normal, healthy tissue at the macro level (milli-meter range), or at the micro levels (micro-meter range) showing different special groups of cells, in which will link to abnormality changes in early abnormality changes, particularly malignancy such as carcinoma.

However, the amount of variation of the individual impedance properties either in macro-ranged level or micro-ranged level may be insufficient to enable accurate analysis of the tissue/cellular structures or abnormality either by in-vitro or by in-vivo based EIM technology. For example, the amount of variation of cell membrane capacitance (C) or relaxation frequency (fr) may be insufficient to be readily detectable, for example in images of the object constructed based on those individual impedance properties.

There are therefor advantages to virtually purifying the signals before processing and to be able to identify an abnormal change including benign and malignant lesion. There are therefor advantages to making sure that the signal is primarily determined by variation in the impedance of the target tissue rather than other tissue to allow to identify benign and malignant changes

BRIEF SUMMARY

According to various, but not necessarily all, embodiments there is provided examples as claimed in the appended claims.

This is 1st time to use the histo-pathological guided bio-impedance model to purify and identify a cellular based abnormality tissue on 2D, 3D EIM imaging by using in-vitro database after "tissue purification”.

BRIEF DESCRIPTION

Some examples will now be described with reference to the accompanying drawings in which:

FIG. 1 shows an example of the subject matter described herein;

FIG. 2 shows an example of the subject matter described herein;

FIG. 3 shows an example of the subject matter described herein;

FIG. 4 shows an example of the subject matter described herein;

FIG. 5 shows an example of the subject matter described herein;

FIG. 6 shows an example of the subject matter described herein;

DETAILED DESCRIPTION

Fig. 1 is a diagrammatic illustration of electrical impedance tomography apparatus;

Figs. 2 show graphs of measured electrical impedance as a function of frequency with single or multiple dispersion;

Figs. 3A & 3B shows example electrical impedance circuit models of an object; and Fig. 4 shows a flowchart.

Fig. 5 shows a controller & Fig 6 show a computer program. Fig. 1 illustrates diagrammatically electrical impedance measurement or electrical impedance tomography (EIT) apparatus 10 for measuring impedance data for a load 12. The load 12 comprises an electrically conductive object to which are attached a plurality of electrodes. The term ‘electrically conductive’ means that the object is capable of conducting an electric current but it does not necessarily need to conduct current very well. The apparatus 10 further comprises a signal source 14, a signal detector 16 and a computer 18. In one embodiment, the signal source provides, as an input signal, an electric current and the signal detector detects, as an output signal, voltage. In another embodiment, the signal source provides, as an input signal, a voltage and the signal detector detect, as an output signal, electric current.

The computer typically comprises at least a processor and a memory. The memory stores a computer program which when loaded into the processor controls the computer.

The input signal is applied using the source 14 to the object via electrodes and the resulting output signals present at same or other electrodes are measured using the detector 16. This process is repeated for different frequencies of input signal. For example, the electric signal may be applied by the signal source 14 at a number of frequencies between 0 Hz (direct current) and 100 MHz, to enable frequency dependent electrical impedance data to be obtained for the object.

The separation of the electrodes used for the impedance measurements determines the resolution or scale at which the object is analyzed. The electrical impedance measurements may be obtained at an expected scale of interest (e.g., micro-meter or millimeter range). As an example of the scale of interest, for a biological object, we may be interested in the single cell or in the group cell level or at tissue or histology level, such as lobule or duct in breast tissue. Subsequently the obtained electrical impedance data will be analyzed using a transfer function of an assumed electrical model to determine a plurality of electrical impedance properties for the object. The electrical model used may depend upon the resolution/scale of the impedance measurements.

Referring to Fig. 2, the electrical impedance data obtained using the above method can be plotted as a function of frequency. This plot 22 represents the impedance changes vs frequencies or transfer function for the object. The computer 18 is operable to execute an appropriate algorithm to analyze the obtained impedance transfer function or frequency dependent impedance properties and thereby determine a plurality of electrical impedance properties for the object. The electrical impedance properties typically include one or more of: a) the impedance at the limit co -> 0 (lower limit) b) the impedance at the limit co->°° (upper limit) c) (i) the relaxation frequency at which there is a change in the tissue structure, membrane or cellular related impedance properties

(ii) the impedance at that change frequency

(iii) the gradient of the change of impedance, particularly at the relaxation frequencies relating to cellular membranes' property and the distribution of diversity of the dedicated the group of cell membranes

For example, if there are N dispersions including the Alpha, Beta and Gamma dispersions of biological materials [Cole K S, Permeability and impermeability of cell membranes for ions. Cold Spring Harbor Symp. Quant. Biol. 8 pp110-22, 1940] within the frequency range used, where N>1 , then the dispersion frequencies wi, C02, ... WN-I, CON, are identified and the electrical impedance properties for a particular dispersion m would typically include one or more of: a) For m=1 , the impedance at the lower (global) limit co -> 0

For m>1 , the impedance at the lower (local) limit co -> co _m - a, where a < ( co _m - co _m - 1 ) and may possibly be 1 ( co _m - co _m -i) b) For m=N, the impedance at the upper (global) limit co->°°

For m<N, the impedance at the upper (local) limit co-> co _m + b, where b < ( co _m +i - co _m ) and may possibly be b ~ 1 ( co _m +i - co _m ) c) (i) the relaxation frequency co _m (frm) at which there is a change in the impedance

(ii) the impedance at that change frequency

(iii) the gradient of the change

The amount of variation of one or more of these impedance properties can be used to analyze the structure of the object due to the intra/extra cellular or intra/extra cellular- like related changes in either macro-range or micro-range levels' pathology property. In some embodiments, the object under analysis is modeled using an equivalent electrical impedance circuit. The object may be modeled using an equivalent electrical impedance circuit 20 illustrated in Fig. 3. Objects which may be modeled using the equivalent electrical impedance circuit 20 may, in a non-limiting example, include human or animal tissue, and porous or other cellular or cellular-like materials, in cascaded structures, such as each of Z1 , Z2, Z3 would contain similar 3-element (like Z1-1 , Z1-2, Z1-3 etc to be able to represent single-cell, small group cell at micro-range or large group cell at macro or tissue level....).

In the illustrated embodiment, the equivalent electrical impedance circuit 20 comprises a cell portion 21 in parallel with an extra-cell portion 23. The cell portion 21 has a capacitance C and a resistance Rj in series. The resistance C is associated with the cell membrane/boundary and the resistance Rj, is associated with the interior of the cell. The extra-cell portion 23 has a resistance R _e . The resistance R _e is associated with the structure outside the cell. The resistance R _e is connected in parallel with the series connected capacitance C and resistance Rj.

A non-limiting example of a single dispersion impedance transfer function for this circuit is:

Re (1 + j.ffl.C.Ri )

Z ( CD ) = - l + j.0.C.(Re + Ri)

In the limit co -> 0, Z-> R _e

In the limit co->°°, Z-> R / R _e i.e. Rj R _e /( Rj+ R _e )

There is a change (dispersion) at frequency fr and an impedance Zr that has a gradient a.

The transfer model for multiple dispersion in biological tissue can be modeled by the Cole-Cole equation (Cole K S 1940, Cole K S 1941 , McAdams E T et al, 1995) as follows:

Z = Roc + (R0 - Roc) / (1 + ( jf/ fr) ) (1-a) Usually this equation can be rewritten as the equation below if a three-element electrical equivalent circuit is used for a simple modeling cell suspensions (Fricke and Morse, 1925) or tissues:

Z = Re • Ri / (Re + Ri) + (Re - Re • Ri / (Re + Ri) ) I (1 + ( jf/ fr) )(1-O)

Where Roc is the result of paralleling R _e and Rj.

There are changes (dispersion) at frequency f _ri and impedance Zn that has a gradient Oj.

.As indicated above, the computer 18 is operable to execute an appropriate algorithm to analyze the measured impedance data and extract a plurality of electrical impedance properties for the object under analysis. For example, based on the measured impedance data, the algorithm may be operable to plot impedance data points as a function of frequency and produce a best fit line 22 using the model to form the transfer function illustrated in Fig. 2. From this transfer function, the computer 18 is capable of determining a plurality of individual impedance properties for the object. These impedance properties may include: a) the impedance at the limit co -> 0, which gives R _e b) the impedance at the limit co->°°, which gives Rj R _e /( Rj+ R _e ) c) (i) the relaxation frequency f _r at which there is a change in the impedance

(ii) the impedance Z _r of the transfer function at that change frequency

(iii) the gradient a of the change which gives the relaxation factor.

The impedance properties may be used to determine further impedance properties using the model.

For example, if both R _e and Rj R _e /( Rj+ R _e ) are known then Rj can be determined.

The impedance Z _r of the transfer function at the change (dispersion) frequency f _r , is where the capacitor dominates the transfer characteristic as with each small increases in frequency it conducts significantly better reducing the impedance. The impedance Z _r at the change (dispersion) frequency f _r , can be modelled as 1/(j.2^ f _r .C). Therefore C can be determined as 1/(j.2 JC f _r . Z _r ).

Variations of the individual impedance properties (R _e , Rj, f _r , Z _r , a, C) may be used to analyze the structure of an object at certain level either at macro or micro range. For example, in the case of human tissue, variations in the individual impedance properties may be indicative of the presence of an abnormality as this gives rise to electrical characteristics which are different to those exhibited by normal, healthy tissue.

However, the amount of variation of the individual impedance properties may be insufficient to enable accurate analysis of the structure. For example, the amount of variation of cell membrane capacitance (C) or relaxation frequency (f _r ) may be insufficient to be readily detectable, for example in images of the object constructed based on those individual impedance properties.

Fig. 3A illustrates a mixed impedance model of the object under analysis for representing a reality different way of mixed tissues including normal stoma, glandular or abnormal tissue growth. In the illustrated embodiment, the equivalent electrical impedance circuit 30A comprises an inclusion portion 31 in parallel with an interinclusion portion 33. The inclusion portion 31 has impedance Z1 and impedance Z3 in series. The inter-inclusion portion 33 has impedance Z3. The impedance Z3 is associated with the structure outside the inclusion. The impedance Z3 is connected in parallel with the series connected impedance Z1 and Z2.

The impedance transfer function for this circuit 30A is:

In this model, Z1 is the impedance of the target tissue. That is, the tissue that is targeted for detection.

Fig. 3B illustrates a mixed impedance model of the object under analysis in the limit of the impedance of the target tissue being zero. In the illustrated embodiment, the equivalent electrical impedance circuit 30b comprises impedance Z3 in parallel with the impedance Z2.

The impedance transfer function for this circuit 30B is:

Z2.Z3

^Zb (") - _{Z2 +} Z3

Or

R is an optimization factor, and the impedance Z2 is a reference impedance for first non-targeted tissue and the impedance Z3 is a reference impedance for second nontargeted tissue that is different to the first non-targeted tissue.

Let

S be experimental data (in frequency domain);

ZA be a model transfer function that includes the target tissue and non-target tissues it is based on a circuit model that includes the electrical impedance of the target tissue and the electrical impedances of the non-target tissues e.g., Z1, Z2, Z3.

ZB be a model transfer function for the non-target tissues based it is based on the same circuit model but which now includes the electrical impedance of the non-target tissues e.g., Z2, Z3 and doe not include the electrical impedance of the target tissue Z1.

N be the noise signal that is the signal arising from the non-target tissue C be the desired signal (the experimental signal S with noise N removed)

S= ZA * s

N= Z _B *S

Substituting Eq 1 N = Z _B * ZA' ¹ *S C= S - N = S- Z _B * ZA' ¹ *S

Therefore, C can be determined from S and knowledge of ZB, ZA. S(t) be experimental data (in time domain);

S be experimental data (in frequency domain);

ZA be a model transfer function that includes the target tissue and non-target tissues it is based on a circuit model that includes the electrical impedance of the target tissue and the electrical impedances of the non-target tissues e.g. Z1 , Z2, Z3.

ZB be a model transfer function for the non-target tissues based it is based on the same circuit model but which now includes the electrical impedance of the non-target tissues e.g. Z2, Z3 and doe not include the electrical impedance of the target tissue Z1. N be the noise signal that is the signal arising from the non-target tissue in frequency domain, and N(t) be the equivalent signa in the time domain.

C be the desired signal (the experimental signal S with noise N removed) in frequency domain, and C(t) be the equivalent signa in the time-space domain. S= Z _A * s

= s= Z _A ' ¹ *S [Eq 1]

N= Z _B *S

Substituting Eq 1

N = Z _B * ZA' ¹ *S N(t)= T' ¹ { Z _B * Z _A ' ¹ *T{S(t)} }

C(t)= S(t) - N(t) = S(t)- T' ¹ { Z _B * ZA' ¹ *T{S(t)} }

Where T{} is the forward transform from time-space to frequency and T' ¹ {} is the reverse transform from frequency to time-space.

Therefore C(t) can be determined from S(t) and knowledge of ZB, ZA.

FIG 4 illustrates a method comprising using a non-target tissue model (e.g FIG 3B) to virtually purify an experimental electrical impedance signal with respect to target tissue. In some examples, the target tissue is tumor tissue.

The method illustrated comprises: obtaining a purifying impedance transfer function (ZB) based on an electrical impedance model (FIG 3B) with one or more non-target tissue components (Z2, Z3) and without a target tissue component (Z1); purifying an experimental electrical impedance signal (S, S(t)) using the purifying impedance transfer function (ZB) to obtain a virtually purified signal (C, C(t))for the target tissue component.

Purifying an experimental electrical impedance signal (S, S(t)) using the purifying impedance transfer function (ZB) to obtain a virtually purified signal for the target tissue component can be performed by assuming an electrical impedance model (FIG 3A) comprising the target tissue component and the one or more non-target tissue components, wherein the target tissue is tumor tissue and the non-target tissue is non-tumor tissue.

The transfer function (ZA) of the full electrical impedance model (FIG 3A) can be used with a transfer function (ZB) of the reduced electrical impedance model (FIG 3B) to obtain a virtually purified signal (C, C(t)) for the target tissue component.

The a transfer function (ZA) of the full electrical impedance model (FIG 3A) is based on target tissue impedance Z1 and non-target tissue impedances Z2, Z3. The reduced electrical impedance model (FIG 3B) is based on non-target tissue impedances Z2, Z3.

The non-target tissue impedances Z2, Z3 can be based on reference impedance values for the pure non-target tissue modified by an optimization factor (R).

The optimization factor (R) correlates to one or more characteristics of the patient such as age and/or obesity/(different BMI)

The assignment of an appropriate optimization factor (R) to a subjects a classification problem that can be addressed using machine learning.

In at least some examples, the method comprises: in dependence upon age and/or a measure of obesity (or BMI), access an impedance transfer function ZB dependent upon non-tumor tissue (e.g., dependent upon Z2, Z3, R); modify an experimental electrical impedance signal using the obtained impedance transfer function ZB to obtain a signal less dependent upon non-tumor tissue impedance (Z1, Z2) and more dependent upon tumor tissue impedance (Z1).

One measure of obesity is body-mass-index

The non tumor tissue impedance can be based on connective tissue impedance and adipose tissue impedance.

The tumor tissue impedance can be a benign, such as fibroadenoma tissue impedance or malignant, such as ductal carcinoma tissue, or lobular carcinoma tissue or ductal carcinoma in-situ (DCIS) impedance depending on target.

The experimental signal can be an electrical impedance signal from an in vitro measurement. This can be used to analyze an in vitro sample.

The experimental signal can be an electrical impedance signal from an in vivo measurement. This can be used to analyze a live subject.

The electrical impedance signal could be from a 2D or 3D region of interest (ROI) on EIM image with known tissue areas at the ROI, based either on tissue-impedance database or measured BMI from this specific patient of volunteer.

The electrical impedance signals can be used for electrical impedance imaging.

In at least some examples, the electrical impedance model is a mixed impedance model which includes a target tissue impedance (Z1) and non-target tissue impedance (Z2, Z3).

The target tissue impedance Z1 and a first non-target tissue impedance Z3 are in electrical series as a combination, and that combination is in electrical parallel to a second non-target tissue impedance Z2- see FIG 3A.

The target tissue impedance Z1 can be one of a fibroadenoma reference impedance or inductal carcinoma reference impedance depending upon a desired target. The first non-target tissue impedance Z3 is one of connective tissue impedance and adipose tissue impedance and the second non-target tissue impedance Z2 is the other one of connective tissue impedance and adipose tissue impedance.

The method can use in vitro samples to determine the reference impedance of target tissue (Z1) and/or use in vitro samples to determine the reference impedance of non- target tissue. It can use an in vitro sample to determine the first non-target tissue reference impedance Z3. It can use an in vitro sample to determine the second non- target tissue reference impedance Z2.

The electrical impedance model can be determined by best fit to in vitro data for one or more samples. For example, a relative ratio R between first non-target tissue reference impedance Z3 and second non-target tissue reference impedance for the electrical impedance model is determined by optimization (best fit), where the impedance of target tissue (Z1) is known.

The electrical impedance model (FIG 3A) is converted to a non-target model (FIG 3B) for non-target tissue by setting the impedance of target tissue (Z1) to zero in the electrical impedance model (FIG 3A).

The non-target tissue model (FIG 3B) is used to estimate a non-target component, and the non-target component is removed from an experimental electrical impedance signal to estimate a target signal. The removal occurs in time domain or frequency domain.

The non-target tissue model (FIG 3B) is used to estimate a non-target signal and the non-target signal is subtracted from an experimental signal to estimate a target signal.

The in vivo target signal can be analyzed to identify and position a spatial electrical impedance variation that represents tumor tissue.

The in vivo target signal can be analyzed to image the tumor tissue, such as on 2D, or 3D EIM images. In some examples, a computer program 406 comprises computer code that when executed by one or more processors performs one or more of the methods described.

In some examples, an imaging system comprises a controller or other means for performing one or more of the methods described.

Referring back to FIG 4.

At block 42, 44, 46:

Use in vitro samples to determine the impedance parameter Z1 of a mixed model (Z1 , Z2, Z3) which includes target tissue (Z1) and non-target tissue (Z2, Z3).

Obtain Z values for: connective tissue, adipose tissue, fibroadenoma, ductal carcinoma lobular carcinoma and DCIS.

The impedance for target tissue (fibroadenoma or ductal carcinoma or lobular carcinoma or DCIS) is used in the modelling process as Z1.

Z2 and Z3 are connective tissue and adipose tissue and the relative ratios are determined by optimization (best fit).

At block 42:

Collect EIS data with following types of tissues:

At block 44, 46: determine Z1 as IDC or fibroadenoma, and Z2 and Z3 as adipose or connective. At blocks 48, 50, 52: For a particular sample, the associated mixed model (Z1 , Z2, Z3) is determined by best fit to in vitro data for the sample (finding Z2, Z3 for the model, Z1 known) e.g., find the optimization parameter R.

Images on the pathology slides of the tissues, are inspected and used to verify how reliable this “best fitted” ratio R is.

At step 54:

The optimized mixed model (Z1 , Z2, Z3 e.g., FIG 3A) is converted to a non-target model for non-target tissue (Z2, Z3, Z1->0, e.g., FIG 3B).

At block 56:

The non-target tissue model (Z2, Z3; e.g., FIG 3B) is used to estimate a non-target signal. The non-target signal is subtracted from an experimental signal to estimate a target signal (Z1).

The experimental signal can be an in vitro signal 43 or can be a Range of Interest (ROI) in vivo signal 55.

Analysis of the in vitro impedance data 43 to identify presence of a malignancy such as IDC. In this case, the experimental signal is associated with the particular sample. Spatial variation of the impedance of the target signal is indicative of IDC in the sample.

Analysis of in vivo data 55 for identification and positioning of the spatial variation AZ that represents a carcinoma or other targeted tissue. In this case, the experimental signal is an in vivo signal 55. Spatial variation of the impedance of the target signal is indicative of a carcinoma in vivo.

Repeat the method blocks 40 to 54 to get appropriate non-target tissue models for different combination of BMI and age.

The composition of tissue will be similar for the particular sample and for the in vivo subject. The ratio R between Z2 and Z3 can be categorized statistically by different age groups or BMI groups. Which means, since Z1 is a reference impedance, it doesn’t change in any classification, however the values of Z2 and Z3 have different range identified by R when we classify them, for example by different age and/or BMI.

The non-target tissue model (FIGH 3B) is made appropriate for use with an experimental in vivo signal by look up by age/BMI or other classification parameters.

The non-target tissue model is then used at block 56 to obtain a better data signal 57 for subsequent analysis e.g., imaging.

Fig 5 illustrates an example of a controller 400. Implementation of a controller 400 may be as controller circuitry. The controller 400 may be implemented in hardware alone, have certain aspects in software including firmware alone or can be a combination of hardware and software (including firmware).

As illustrated in Fig 5 the controller 400 may be implemented using instructions that enable hardware functionality, for example, by using executable instructions of a computer program 406 in a general-purpose or special-purpose processor 402 that may be stored on a computer readable storage medium (disk, memory etc) to be executed by such a processor 402.

The processor 402 is configured to read from and write to the memory 404. The processor 402 may also comprise an output interface via which data and/or commands are output by the processor 402 and an input interface via which data and/or commands are input to the processor 402.

The memory 404 stores a computer program 406 comprising computer program instructions (computer program code) that controls the operation of the apparatus 10 when loaded into the processor 402. The computer program instructions, of the computer program 406, provide the logic and routines that enables the apparatus to perform the methods illustrated in Figs. The processor 402 by reading the memory 404 is able to load and execute the computer program 406.

The apparatus 10 therefore comprises: at least one processor 402; and at least one memory 404 including computer program code the at least one memory 404 and the computer program code configured to, with the at least one processor 402, cause the apparatus 10 at least to perform: any one or more of the methods described.

As illustrated in Fig 6, the computer program 406 may arrive at the apparatus 10 via any suitable delivery mechanism 408. The delivery mechanism 408 may be, for example, a machine readable medium, a computer-readable medium, a non- transitory computer-readable storage medium, a computer program product, a memory device, a record medium such as a Compact Disc Read-Only Memory (CD- ROM) or a Digital Versatile Disc (DVD) or a solid-state memory, an article of manufacture that comprises or tangibly embodies the computer program 406. The delivery mechanism may be a signal configured to reliably transfer the computer program 406. The apparatus 10 may propagate or transmit the computer program 406 as a computer data signal.

Computer program instructions for causing an apparatus to perform at least the following or for performing any one or more of the methods described.

The computer program instructions may be comprised in a computer program, a non- transitory computer readable medium, a computer program product, a machine readable medium. In some but not necessarily all examples, the computer program instructions may be distributed over more than one computer program.

Although the memory 404 is illustrated as a single component/circuitry it may be implemented as one or more separate components/circuitry some or all of which may be integrated/removable and/or may provide permanent/semi-permanent/ dynamic/cached storage.

Although the processor 402 is illustrated as a single component/circuitry it may be implemented as one or more separate components/circuitry some or all of which may be integrated/removable. The processor 402 may be a single core or multi-core processor. References to ‘computer-readable storage medium’, ‘computer program product’, ‘tangibly embodied computer program’ etc. or a ‘controller’, ‘computer’, ‘processor’ etc. should be understood to encompass not only computers having different architectures such as single /multi- processor architectures and sequential (Von Neumann)/parallel architectures but also specialized circuits such as field- programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other processing circuitry. References to computer program, instructions, code etc. should be understood to encompass software for a programmable processor or firmware such as, for example, the programmable content of a hardware device whether instructions for a processor, or configuration settings for a fixed-function device, gate array or programmable logic device etc.

The blocks illustrated in the Figs may represent steps in a method and/or sections of code in the computer program 406. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some blocks to be omitted.

Where a structural feature has been described, it may be replaced by means for performing one or more of the functions of the structural feature whether that function or those functions are explicitly or implicitly described.

The systems, apparatus, methods and computer programs may use machine learning which can include statistical learning. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. The computer learns from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. The computer can often learn from prior training data to make predictions on future data. Machine learning includes wholly or partially supervised learning and wholly or partially unsupervised learning. It may enable discrete outputs (for example classification, clustering) and continuous outputs (for example regression). Machine learning may for example be implemented using different approaches such as cost function minimization, artificial neural networks, support vector machines and Bayesian networks for example. Cost function minimization may, for example, be used in linear and polynomial regression and K-means clustering. Artificial neural networks, for example with one or more hidden layers, model complex relationship between input vectors and output vectors. Support vector machines may be used for supervised learning. A Bayesian network is a directed acyclic graph that represents the conditional independence of a number of random variables.

The term ‘comprise’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising Y indicates that X may comprise only one Y or may comprise more than one Y. If it is intended to use ‘comprise’ with an exclusive meaning then it will be made clear in the context by referring to “comprising only one.” or by using “consisting”.

In this description, reference has been made to various examples. The description of features or functions in relation to an example indicates that those features or functions are present in that example. The use of the term ‘example’ or ‘for example’ or ‘can’ or ‘may’ in the text denotes, whether explicitly stated or not, that such features or functions are present in at least the described example, whether described as an example or not, and that they can be, but are not necessarily, present in some of or all other examples. Thus ‘example’, ‘for example’, ‘can’ or ‘may’ refers to a particular instance in a class of examples. A property of the instance can be a property of only that instance or a property of the class or a property of a sub-class of the class that includes some but not all of the instances in the class. It is therefore implicitly disclosed that a feature described with reference to one example but not with reference to another example, can where possible be used in that other example as part of a working combination but does not necessarily have to be used in that other example.

Although examples have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the claims.

Features described in the preceding description may be used in combinations other than the combinations explicitly described above.

Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not. Although features have been described with reference to certain examples, those features may also be present in other examples whether described or not.

The term ‘a’ or ‘the’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising a/the Y indicates that X may comprise only one Y or may comprise more than one Y unless the context clearly indicates the contrary. If it is intended to use ‘a’ or ‘the’ with an exclusive meaning then it will be made clear in the context. In some circumstances the use of ‘at least one’ or ‘one or more’ may be used to emphasis an inclusive meaning but the absence of these terms should not be taken to infer any exclusive meaning.

The presence of a feature (or combination of features) in a claim is a reference to that feature or (combination of features) itself and also to features that achieve substantially the same technical effect (equivalent features). The equivalent features include, for example, features that are variants and achieve substantially the same result in substantially the same way. The equivalent features include, for example, features that perform substantially the same function, in substantially the same way to achieve substantially the same result.

In this description, reference has been made to various examples using adjectives or adjectival phrases to describe characteristics of the examples. Such a description of a characteristic in relation to an example indicates that the characteristic is present in some examples exactly as described and is present in other examples substantially as described.

Whilst endeavoring in the foregoing specification to draw attention to those features believed to be of importance it should be understood that the Applicant may seek protection via the claims in respect of any patentable feature or combination of features hereinbefore referred to and/or shown in the drawings whether or not emphasis has been placed thereon.

ANNEX The attached annex provides further details and forms part of the description.

Proceedings of the International Conference of Bioelectromagnetism, Electrical Bioimpedance, and Electrical Impedance Tomography June 28 - July 1, 2022 / Kyung Hee University, Seoul, Korea

Impedivity of various human breast tissues

Gerald Sze ¹ , Zhao Song ¹ ’ ³ , Huijuan Zhang ² , Jie Wang ² , Qi He ² , Rui Guo ¹ , Barry Bueles ³ and

Wei Wang ¹ ’ ³ ’ ⁴

'Micro Image Biotech Ltd, Ningbo, China

² Shanghai International Peace Maternity and Child Health Hospital of China Welfare Institute, China 3Micro Image Biotech International Ltd, Cambridge, UK

⁴ Weilian Biotech Ltd, Ningbo, China

Correspondence: Wei Wang, e-mail: w97wang@yahoo.co.uk

Abstract-A series of Electrical Impedance Spectroscopy (EIS) experiment have been carried out to extract unique characteristics of benign and malignant human breast tissue in-vitro in a joint clinical study with the Pathology Department. Digital images of pathology slides for each paraffin-sectioned breast tissues have been taken, so that the effects of unwanted tissue types mixing with the targeted tissues types can be studied. By recognizing mixed tissues in digital pathology slide images, the unwanted mixed tissues can be removed from the EIS data, and the impedance patterns of the target tissue ocused on such as cancer cells. The impedance patterns of the following four types of tissue are initially extracted: adipose tissue, connective tissue, inductal carcinoma (IDC), fibroadenoma. The information within this data will help to achieve the goal of being able to predict the likely different diagnostic results according to each patient’s different measureable life parameters such as BMI and age.

Keywords', human breast tissues; impedivity; electrical impedance spectroscopy; EIS; breast cancer.

1. Introduction

Different types of breast tissue were isolated in-vitro from surgically excised pathology from breast tissue and the EIS data for each tissue type was digitally recorded before the tissue was paraffin-sectioned as pathology slides. Based on the characteristics of the impedance patterns of human tissue published by Cole and Cole (1941), and the human breast tissue published by Jossinet (1998) and Wang (2001) as the reference for guidance, the impedance pattern characteristics of several tissue types within human breast tissue were determined using the EIS data.

One of the challenges of EIS studies of in-vitro breast tissues is the mixing of different tissue types in the same biopsy or excised pathology sample which causes great variances in the measured overall impedance. The approach adopted was to analyse the digital pathology slide images to determine the different tissue types present in the sample breast specimen. The direction of the electric current inside the specimen during the EIS measurement was also analysed. The Cole-Cole impedance parameters for the different pure tissue types were used to determine the different tissue types present within the same test pathology specimen. The EIS data sets of the different pure tissue types present in the overall tissue were then extracted from the cleaned pure tissue specimen data sets being examined at a cellular level. The real EIS impedance was compared to the simulated EIS impedance and the ratio of mixed tissues in simulated data are highly matched with the ratio found from the digital pathology slides.

2. Data collection

2.1 Sampling size

68 EIS data sets have been taken from the hospital, of which 31 sets were valid for analysis. 11 sets (35% out of 31) were taken from patients diagnosed with a benign tumour, and 21 sets (68% out of 31) were taken from patients diagnosed with a malignant tumour. Note that some cases had both benign and malignant tissues diagnosed.

2.2 Quality control of EIS specimen

All the sets of data (“set of data” means all EIS measurements from all tissues taken from one patient in a surgical operating room to the pathology laboratory) must satisfy with following criteria to ensure that their quality is sufficient to be “valid”. There are two types of data comparison: One compares the different types of tissues taken from the same patient; Another compares the same types of tissues taken from different patients

Timing after surgery

The EIS data must be collected within 15 minutes of surgery with the tissue stored in a in a temperature controlled box (6°C). The electrical impedance properties in human cells deteriorate rapidly after this period.

Background saline reference data set

The EIS background saline reference data set (filling the testing chamber with lOOOpS/cm saline water) must be collected before the human tissue EIS measurement. The saline EIS data set is used as a reference to calibrate the EIS testing unit’s internal impedance difference.

Size of tissue according to volume of testing chamber

Tissues are cut to fit the exact size of the cylindrical testing chamber (diameter 5mm and length 8mm). Saline is injected to ensure good contact between the tissue and the electrodes that are located at the two ends of the chamber. The size of tissue sample must not less than the volume of the chamber to avoid the current by -passing the tissue sample through the saline. The saline must not fill more than 10% of the chamber’s volume. Otherwise the impedance patterns of the test tissues will be distorted invalidating the impedance measurements of the sample.

Data comparison

There are two types of data comparison: One compares different type of tissues taken from the same patient, another compares same type of tissues taken from different patients.

2.3 Digital pathology slide images

The illustrations below outline the procedures used to capture the digital pathology slide images of breast tissues with different fields of views (FOV):

Figure 1. Procedures to capture digital pathology slide images.

2.4 Age and BMI distribution

The 31 valid data sets are collected from 31 patients accordingly: aged below 40 = 7, aged between 40 and 60 = 14, aged above 60 = 10; BMI below 23 = 12, BMI between 23 and 25 = 10, BMI above 25 = 9. 21 patient has been diagnosed with IDC tumour, 11 patients has been diagnosed with fibroadenoma tumour.

3. Methods

3.1 Procedures

There are three steps in verifying pure tissue impedance: i. Categorize tissues into “pure” and “mixed” groups according to the pathology slide images (example of mixed and pure tissues shown in figure 2); ii. From “pure” tissues, determine the impedance data and extract the Cole-Cole parameters as described in section 3.2; iii. Using the patented cascaded model (Wang’s US patent), iv. Eliminate the mixed tissues impedance component from the real EIS data to produce the ‘clean’ data, and the impedance are converged to the ‘pure’ tissue impedance.

3.2 Equations

The Cole-Cole equation (Cole 1940, Cole and Cole 1941, McAdams and Jossinet 1995) displays the change in impedance at different frequencies:

Z = R _m + (R ₀ + R _m )/(1 + (j /F _r )) ^K (1) where R^ and Ro = resistance at infinite or zero frequency

F _r = relaxation frequency a= relaxation time.

When we assuming the measuring object as a simplified cell suspension model with three element (R. S. C) electrical equivalent circuit (Fricke and Morse 1925), the following equation can be used:

Z = RS/(F + S) + (F - RS/(R + S))/(l + (j//F _r )) ^K where R= extra-cellular resistance, equals to Ro in (1) S= intra-cellular resistance 7?oo = paralleling R and S.

Four Cole-Cole parameters: R. S, F _r , o' are calculated from the EIS bio-impedivity curves using these two equations.

3.3 Integrated model

Below diagram shows an integrated model composed by Zl, Z2 and Z3 tissues for calculating the simulated resistivity: where Z1 = Impedivity of targeted “pure” tissue

Z2 and Z3 = Impedivity of the “mixed” tissue that in series and parallel with the targeted tissue, values are various according to the pathology slide images

4. Results

4.1 Digital pathology slide images of mixed and pure tissues

Below pathology slide images show connective tissues that are affected by different compositions of adipose tissues:

Figure 2. Connective tissues with different composition of adipose tissue (top-left: heavy; top-right: medium; bottom-left: mild; bottom-right: none).

4.2 Calculated Cole-Cole parameters of pure tissues

Base on the selected EIS data sets, the impedivity curves of four “pure” tissues: adipose, connective tissue, IDC and fibroadenoma are calculated and plotted as below:

Figure 3. The plotting of real (left) and Imaginary (right) impedivity against frequency from the averaged impedance of pure tissues.

The Cole-Cole parameters for each type of tissue are then calculated and listed as below:

Table 1. Calculated Cole-Cole parameters for the four types of pure tissues

R in Q cm S in Q cm Fr in kHz a

Connective tissue 2.97 3.22 4515 0.14

Adipose tissue 16.51 38.93 13379 0.48

IDC 5.40 2.19 641 0.47

Fibroadnoma 3.57 3.45 6308 0.31 Table 2 lists the Cole-Cole parameters published in Jossinet’s paper about the impedance of freshly excised human breast tissue.

Table 2. Calculated Cole-Cole parameters for the four types of pure tissues

R in Q cm S in Q cm Fr in kHz a

Connective tissue 12.63 28.80 448 0.51

Adipose tissue 23.90 67.79 216 0.57

IDC 3.89 0.991 477 0.46

Fibroadnoma 2.54 10.11 497 0.59

4.3 Comparing between simulated and real measurement

Below is an example of real measured and simulated EIS impedance (real-part) data:

Figure 4. Real (left) and simulated (right) EIS impedance with 34% IDC and 66% connective tissue against frequency.

4.4 Standard deviation after purification

The Cole-Cole parameters for each type of tissue are then calculated and listed as below:

Figure 5. Standard deviation of connective tissue (green), IDC (red) and fibroadenoma (blue) before (left figure) and after (right figure) purification. Dotted lines above and below the solid lines are the +/- of standard deviation.

5. Discussion

The R and S calculated from the Shanghai data set of connective tissue drops from above 20 Q m from Jossinet data sets to below 4 Q m. This may be caused by the existence of adipose tissue within the measured connective tissues.

The relaxation frequencies of connective, adipose and fibroadenoma tissues from the Shanghai data sets are much higher than the Jossinet data sets. At this point we need more data to verify these new findings. However, IDC is the tissue type that has the most similar Cole-Cole parameters extracted from the EIS data sets.

6. Conclusions

The work presented is essential before the Electrical Impedance Mammography (EIM) images could be diagnosed because each pixel in 3D EIM images are mixed with surrounding tissues, and the impedance characteristics of mixed tissues must be identified with solid evidence, which could be by using pathology slide images and EIS data on the same tissue. Acknowledgments

We acknowledge Shanghai International Peace Maternity and Child Health Hospital of China Welfare Institute who provided all data and the opportunities to run the clinical trial during the hard time during CO VID pandemic.

References

Cole K 1940 Permeability and impermeability of cell membranes for ions Cold Spring Harbor Symp., Quant. Biol. 8 110-22

Cole K and Cole R 1941 Dispersion and absorption in dielectrics J. of Chem. Phys. 5 341-350

Jossinet J 1998 The impedivity of freshly excised human breast tissue Physiol. Meas. 19 61-75

McAdams E and Jossinet J 1995 Tissue impedance: a historical overview Physiol. Meas. 16 (suppl) A1-A13

Wang W, et al 2001 Preliminary results from an EIT breast imaging simulation system Physiol. Meas 22(1) 39-48

Wang W, 2005.02 Apparatus and method for detecting abnormalities in bodily matter US6856842(B1)