Patent Description:
Electrical Impedance Tomography (EIT) is a non-invasive imaging method that may be used to generate images of a region of interest of a domain (e.g., a patient) by collecting data using electrodes disposed along the perimeter of the region of interest. One conventional application of EIT includes clinical applications, in which tomographic images of the human body may be useful. For example, EIT is conventionally used to monitor cardio-respiratory systems, which may be particularly useful in patients under treatment in intensive care unit (ICU) environments.

During EIT procedures, electrical signals (e.g., electric currents) may be injected into a perimeter of the region of interest of the domain being imaged (e.g., a patient's torso). Electrical characteristics (e.g., voltages, electric potentials) resulting from the injected electrical signals may be collected at the perimeter of the region of interest. From the collected data, a map with an estimate of electrical properties (e.g., impedances) may be generated or reconstructed. EIT systems are often susceptible to reconstruction artifacts. In order to mitigate artifacts, the reconstruction of EIT images often employs differential reconstruction techniques, in which the generated images (e.g., differential images) utilize changes between the current property (e.g., impedance) map and a reference impedance map. While differential images are useful in several settings, certain medical diagnoses are limited without knowledge of the absolute property (e.g., impedance) map.

As noted above, current EIT methods generally focus on the production of differential images. The preference toward differential images is likely a result of the fact that direct calculation of absolute impedance values from EIT data relies on a linear or quasi-linear reconstruction method, which often overestimate values of some pixels and underestimates the values of other pixels of the region of interest.

<CIT> relates to the field of instrumentation for monitoring and evaluating patients with heart disease, particularly congestive heart failure.

In document by <NPL>, errors associated with EIT measurements of changes in gas and liquid volumes in the lung in dogs have been estimated.

However, the above-mentioned issues are not solved.

The present invention relates to a method of estimating a fluid volume and a system, as defined in the annexed claims.

Some embodiments include a method of estimate a fluid volume. The method includes receiving electrical tomography data of a portion of a domain, reconstructing an initial impedance image based at least partially on the electrical tomography data, enhancing the initial impedance image to generate an enhanced impedance image, segmenting the enhanced impedance image to identify one or more tissues depicted within the enhance impedance image, selecting a region of interest within the enhanced impedance image, determining a relationship parameter that relates electrical properties represented within the region of interest of the enhanced impedance image with one or more properties of the region of interest, and estimating a fluid volume within the region of interest based at least partially on the relationship parameter and the enhanced impedance image.

Some embodiments include a system for estimating a fluid volume within a domain. The system includes an electrical tomography system and a volume estimation system. The fluid volume estimation system includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the volume estimation system to: receive prior information associated with a domain, receive an initial impedance image of a portion of the domain from the electrical tomography system, enhance an initial impedance image based at least partially on the received prior information to generate an enhanced impedance image, and based at least partially on the enhanced initial impedance image, generate a volumetric image of a region of interest of the enhanced impedance image, wherein the volumetric image represents a plurality of values indicating a volume of a fluid.

The illustrations presented herein are not actual views of any EIT system or volume estimation system but are merely idealized representations employed to describe example embodiments of the disclosure. In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to practice the disclosure. It should be understood, however, that the detailed description and the specific examples, while indicating examples of embodiments of the disclosure, are given by way of illustration only and not by way of limitation.

In accordance with common practice, the various features illustrated in the drawings may not be drawn to scale. Accordingly, the dimensions of the various features may be arbitrarily expanded or reduced for clarity. Thus, the drawings may not depict all of the components of a given apparatus or all operations of a particular method.

Some drawings may illustrate signals as a single signal for clarity of presentation and description. It should be understood by a person of ordinary skill in the art that the signal may represent a bus of signals, wherein the bus may have a variety of bit widths and the disclosure may be implemented on any number of data signals including a single data signal.

The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a special purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be considered a special-purpose processor while the general-purpose processor executes instructions (e.g., software code) stored on a computer-readable medium.

Also, it is noted that embodiments may be described in terms of a process that may be depicted as a flowchart, a flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe operational acts as a sequential process, many of these acts can be performed in another sequence, in parallel, or substantially concurrently. In addition, the order of the acts may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. Furthermore, the methods disclosed herein may be implemented in hardware, software, or both. If implemented in software, the functions may be stored or transmitted as one or more instructions or code on computer-readable media. Computer-readable media include both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another.

As used herein, the term "may" with respect to a material, structure, feature, or method act indicates that such is contemplated for use in implementation of an embodiment of the disclosure, and such term is used in preference to the more restrictive term "is" so as to avoid any implication that other compatible materials, structures, features, and methods usable in combination therewith should or must be excluded.

It should be understood that any reference to an element herein using a designation such as "first," "second," and so forth does not limit the quantity or order of those elements, unless such limitation is explicitly stated. Rather, these designations may be used herein as a convenient method of distinguishing between two or more elements or instances of an element. Thus, a reference to first and second elements does not mean that only two elements may be employed there or that the first element must precede the second element in some manner. Also, unless stated otherwise a set of elements may comprise one or more elements.

As used herein, the term "substantially" in reference to a given parameter, property, or condition means and includes to a degree that one skilled in the art would understand that the given parameter, property, or condition is met with a small degree of variance, such as within acceptable manufacturing tolerances. For example, a parameter that is substantially met may be at least about <NUM>% met, at least about <NUM>% met, or even at least about <NUM>% met.

As used herein, the term "about" used in reference to a given parameter is inclusive of the stated value and has the meaning dictated by the context (e.g., it includes the degree of error associated with measurement of the given parameter, as well as variations resulting from manufacturing tolerances, etc.).

Embodiments of the disclosure include an electrical property tomography (e.g., electrical impedance tomography (EIT)) device and/or system for generating images of a region of a patient's body, and the images may be utilized to determine absolute volume estimations of a biological fluid (e.g., air, blood, water, tissue). For clarity and ease of explanation, EIT systems will be referenced herein throughout the disclosure; however, any electrical property tomography device may be utilized in place of or in addition to the EIT systems and is within the scope of the present disclosure. For example, the electrical property tomography device may include device that measure one or more electrical conductivity, electrical resistivity, electrical permittivity, electrical admitivity, or any other electrical property. In particular, EIT is an imaging technique involving the positioning electrodes via an electrode belt placed around a region of a patient's body (e.g., around the patient's chest for imaging of a lung), injecting electrical excitation signals through a pair of electrodes, and measuring the induced response signals detected by the other electrodes of the electrode belt. The EIT system may generate an image based on the voltage measurements indicating estimated impedance values throughout at least a portion of the region of the patient's body. In contrast with other imaging techniques, EIT is non-invasive and does not present exposure risks (e.g., radiation exposure risks) that can limit the number and frequency of monitoring actions (e.g., as with techniques such as X-rays). As a result, EIT is suitable for continuously monitoring the condition of the patient, with particular application to monitoring the patient's lungs as the measurements may be used to determine respiratory and hemodynamic parameters of the patient and monitor a real-time two/three dimensional image.

Embodiments of the present disclosure relate to electrical property tomography systems and methods of operation thereof that may be used to generate absolute impedance images and/or volumetric measurements (e.g., fluid volume estimations) from EIT data. For example, embodiments of the present disclosure may include systems capable of performing corrections and/or enhancements to absolute impedance images. In some embodiments, correcting and/or enhancing the absolute impedance images may include utilizing priors (e.g., prior information), datasets that include statistical distributions that correlate impedances and EIT data. The systems and methods may be used to provide important diagnostic parameters of, for example, respiratory dynamics that may not be easily obtained from differential EIT images. Conditions and parameters that may be measured with the systems and methods described herein include, but are not limited to, functional residual capacity (FRC), tidal volume, pneumothorax detection, and cellularity on pleural diffusions.

<FIG> is a schematic diagram of a portion of an EIT system <NUM> showing a plurality of electrodes <NUM> positioned around a region of interest (e.g., thorax) of a patient <NUM>. The electrodes <NUM> of the EIT system <NUM> may be physically held in place by an electrode belt <NUM>. The placement of the electrodes <NUM> may be transverse to a cranial caudal axis <NUM> of the patient. Although the electrodes <NUM> are shown in <FIG> as being placed only partially around the patient <NUM>, electrodes <NUM> may by placed around the entire patient <NUM> depending on the specific region of interest available or desired for measurement. Furthermore, the electrodes <NUM> may be oriented relative to one another in one or more parallel rows (e.g., planes), in one or more zigzag patterns (e.g., one or more lines having abrupt alternate turns), or in any combination thereof. The electrodes <NUM> may be operably coupled to a computing system (not shown) configured to control the operation of the electrodes <NUM> and perform reconstruction of an EIT image.

<FIG> is a schematic diagram showing a cross-section of the thorax of the patient <NUM> along the plane of the electrodes. A voltage may be applied to a pair of electrodes <NUM> (shown by the electrodes having a + and - symbol) to inject an excitation current into the patient between an electrode pair. As a result, voltages (e.g., V<NUM>, V<NUM>, V<NUM>. Vn) may be detected by the other electrodes and measured by the EIT system <NUM>. Current injection may be performed for a measurement cycle according to a circular pattern using different electrode pairs to generate the excitation current.

<FIG> is a schematic block diagram of an EIT system <NUM> according to an embodiment of the disclosure. The EIT system <NUM> may include an electrode belt <NUM> operably coupled with a data processing system <NUM>. The electrode belt <NUM> and the data processing system <NUM> may be coupled together via a wired connection (e.g., cables) and/or may have communication modules to communicate wirelessly with each other. The data processing system <NUM> may include a processor <NUM> operably coupled with an electronic display <NUM>, input devices <NUM>, and a memory device <NUM>. The electronic display <NUM> may be constructed with the data processing system <NUM> into a singular form factor for an EIT device coupled with the electrode belt <NUM>. In some embodiments, the electronic display <NUM> and the data processing system <NUM> may be separate units of the EIT device coupled with the electrode belt <NUM>. In yet other embodiments, an EIT system <NUM> may be integrated within another host system configured to perform additional medical measurements and/or procedures, in which the electrode belt <NUM> may couple to a port of the host system already having its own input devices, memory devices, and electronic display. As such, the host system may have the EIT processing software installed therein. Such software may be built into the host system prior field use or updated after installation.

The processor <NUM> may coordinate the communication between the various devices as well as execute instructions stored in computer-readable media of the memory device <NUM> to direct current excitation, data acquisition, data analysis, and/or image reconstruction. As an example, the memory device <NUM> may include a library of finite element meshes used by the processor <NUM> to model the patient's body in the region of interest for performing image reconstruction. Input devices <NUM> may include devices such as a keyboard, touch screen interface, computer mouse, remote control, mobile devices, or other devices that are configured to receive information that may be used by the processor <NUM> to receive inputs from an operator of the EIT system <NUM>. Thus, for a touch screen interface the electronic display <NUM> and the input devices <NUM> receiving user input may be integrated within the same device. The electronic display <NUM> may be configured to receive the data and output the EIT image reconstructed by the processor for the operator to view. Additional data (e.g., numeric data, graphs, trend information, and other information deemed useful for the operator) may also be generated by the processor <NUM> from the measured EIT data alone, or in combination with other non-EIT data according to other equipment coupled thereto. Such additional data may be displayed on the electronic display <NUM>.

The EIT system <NUM> may include components that are not shown in the figures, but may also be included to facilitate communication and/or current excitation with the electrode belt <NUM> as would be understood by one of ordinary skill in the art, such as including one or more analog to digital converter, signal treatment circuits, demodulation circuits, power sources, etc..

<FIG> illustrates a schematic diagram of an environment <NUM> in which a volume estimation system can operate according to one or more embodiments of the present disclosure. As illustrated, the environment <NUM> includes an EIT system <NUM>, a volume estimation system <NUM>, a network <NUM>, and one or more additional system(s) <NUM>. The volume estimation system <NUM>, the EIT system <NUM>, and the additional system(s) <NUM> can communicate via the network <NUM>. The network <NUM> may include one or more networks, such as the Internet, and can use one or more communications platforms or technologies suitable for transmitting data and/or communication signals. Although <FIG> illustrates a particular arrangement of the EIT system <NUM>, the volume estimation system <NUM>, the additional system(s) <NUM>, and the network <NUM>, various additional arrangements are possible. For example, the volume estimation system <NUM> may directly communicate with the EIT system <NUM>, bypassing the network <NUM>.

As illustrated in <FIG>, a user <NUM> can interface with the volume estimation system <NUM> to initiate one or more volume estimations and/or any of the methods described herein. The user <NUM> can be an individual (i.e., human user), a business, a group, or any other entity. Although <FIG> illustrates only one user <NUM> associated with the volume estimation system <NUM>, the environment <NUM> can include any number of a plurality of users that each interact with the environment <NUM> using a corresponding volume estimation system <NUM>.

In some embodiments, a volume estimation system <NUM> may include one or more types of servers, one or more data stores, one or more interfaces, including but not limited to APIs, one or more web services, one or more content sources, one or more networks, or any other suitable components, e.g., that servers may communicate with. In this sense, the volume estimation system <NUM> may provide a platform, or backbone, which other systems, such as the additional systems <NUM>, may use to initiate fluid volume estimations within regions of interest of a domain (e.g., a patient).

In one or more embodiments, the volume estimation system <NUM> may perform reconstruction algorithms, correction algorithms, and/or volumetric calculation algorithms, as detailed below. In some embodiments, the volume estimation system <NUM> may also include an interface system for controlling the electrical signals going into and coming from electrical leads of an electrode belt of the EIT system <NUM>. In one or more embodiments, the interface system may include, among other things, analog signal generators, analog-to-digital converters, digital-to-analog converters, digital signal processors, filters, and impedance matching circuitry, to improve signal-to-noise ratio and decrease crosstalk.

As shown in <FIG>, in some embodiments, the volume estimation system <NUM> can include a database <NUM>. As is described in greater detail below, the volume estimation system <NUM> can utilize the database <NUM> to store initial impedance images, enhanced impedance images, and/or fluid volume estimations and values.

In some embodiments, the volume estimation system <NUM> further includes a client application <NUM> installed thereon. In one or more embodiments, the client application <NUM> can be associated with the volume estimation system <NUM>. For example, the client application <NUM> allows the user <NUM>, the EIT system <NUM>, and/or the additional systems <NUM> to directly or indirectly interface with the volume estimation system <NUM>, the EIT system <NUM>, and/or the additional systems <NUM>. For example, the client application <NUM> can include a web browsing application and/or a specific volume estimation application.

The additional systems <NUM> may include additional systems that may interface with the volume estimation system <NUM> and/or provide data to the volume estimation system <NUM>. For example, in some embodiments, the additional systems <NUM> may include computed tomography system (e.g., a CT scanner), an x-ray system, a magnetic resonance imaging system, an echocardiogram device, or any other device for producing images and/or data representing internal portions of a domain (e.g., a patient), model (e.g., prior model), and/or simulation.

The volume estimation system <NUM> may represent various types of computing devices with which users (e.g., an administrator) may interact. For example, the volume estimation system <NUM> may be a mobile device (e.g., a cell phone, a smartphone, a PDA, a tablet, a laptop, a watch, a wearable device, etc.). In some embodiments, however, the volume estimation system <NUM> can be a non-mobile device (e.g., a desktop or server). Additional details with respect to volume estimation system <NUM> are discussed below with respect to <FIG>.

<FIG> is a flowchart of a method <NUM> of reconstructing an EIT image according to one or more embodiments of the present disclosure. <FIG> is a schematic block diagram of the method <NUM> of <FIG>. Referring to <FIG> together, the method <NUM> may be performed by one or more of the volume estimation system <NUM>, the EIT system <NUM>, the additional systems <NUM>, or any combination thereof.

In some embodiments, the method <NUM> includes receiving data regarding voltages (e.g., electric potentials) detected by electrodes of an EIT system (e.g., the EIT system <NUM>) during an EIT imaging procedure, as shown in act <NUM> of <FIG>. For instance, receiving data regarding the voltages detected by the electrodes of the EIT system <NUM> may include data regarding voltages detected after applying voltages and/or currents to a domain (e.g., patient) via any of the procedures described above in regard to <FIG>. In some embodiments, the method <NUM> may include the volume estimation system <NUM> receiving the data regarding the detected voltages from the EIT system <NUM>. In one or more embodiments, the volume estimation system <NUM> may detect/measure the voltages directly by passing one or more portions of the EIT system <NUM>.

In some embodiments, the method <NUM> may include utilizing the received data regarding the detected voltages to reconstruct an initial impedance image, as shown in act <NUM>. In some embodiments, reconstructing the initial impedance image may include reconstructing one or more of an initial absolute impedance image or an initial difference impedance image. For example, one or more of the volume estimation system <NUM>, the EIT system <NUM>, the additional systems <NUM>, or any combination thereof may reconstruct the initial impedance image.

As is known in the art, an absolute impedance image represents electrical properties (e.g., represents electrical properties via pixels, voxels, etc.) throughout a respective two-dimensional or three-dimension section of the domain (e.g., a patient) at a given time, and a difference impedance image represents changes in electrical properties throughout a two-dimensional or three-dimension section of the domain (e.g., a patient) from a reference state. For example, each pixel and/or voxel may be associated with and/or may represent an impedance of a corresponding portion of the region of interest. As used herein, an "initial impedance" image may refer to one or more of an absolute impedance image or a different impedance image.

Image reconstruction is formulated as an inverse problem (referred to hereinafter as "the reconstruction problem") which calculates an estimate of a distribution of internal properties (e.g., impedance properties), which best represent the measured electrical potentials. As is discussed in greater detail below, reconstructing the initial impedance image (i.e., act <NUM>) may include utilizing iterative algorithms that attempt to minimize an objective function (e.g., a cost function) and utilizing regularization techniques to reduce error propagation, improve stability of the iterations (e.g., iterative algorithms), and/or conditions of the reconstruction problem. Also, in some embodiments, reconstructing the initial absolute impedance image (i.e., act <NUM>) may include utilizing a Bayesian technique, in which a priori information may be used as a regularization of the problem and the maximum a posteriori of a conditional probability density function may be used as an objective function.

As noted above, in some embodiments, reconstructing an initial impedance image may include reconstructing an initial impedance image via one or more iterative processes. For instance, reconstructing the initial impedance image may include reconstructing an initial impedance image via one or more methods such as, for example, a Gauss-Newton reconstruction approach, a Noser approach, a Simulated Annealing approach, a Kalman Filter approach, a Level Set approach, a Markov chain Monte Carlo approach, a Blackbox approach, a Back Projection approach, a Total Variation approach, or a Non-Linear Programming approach. In additional embodiments, reconstructing the initial impedance image may include reconstructing an initial impedance image via a Landweber iteration approach.

In some embodiments, reconstructing the initial impedance image may include reconstructing the initial impedance image via one or more direct methods. In other words, reconstructing the initial impedance image may include reconstructing the initial impedance image without input from external data sources (e.g., prior information). For instance, reconstructing the initial impedance image may include reconstructing an initial impedance image via one or more methods such as, for example, a direct D-bar reconstruction algorithm, a Calderon's method, or an algebraic reconstruction technique. In additional embodiments, as described below, reconstructing the initial impedance image may include reconstructing the initial impedance image based at least partially on external data sources (e.g., prior information).

In some embodiments, reconstructing the initial impedance image may include reconstructing the initial absolute impedance image using one or more regularization methods. For instance, reconstructing the initial impedance image may include using one or more regularization methods to reconstruct the initial impedance image such as, for example, a Generalized Tikhonov Regularization (e.g., a Gaussian high-pass filter), utilizing prior information (e.g., taking into account a probability density function, as described below) and/or regularization utilizing a total variation (TV) functional.

As noted above, in one or more embodiments, reconstructing the initial impedance image may further include reconstructing the initial impedance image based at least partially on prior information (i.e., one or more priors). As used herein, "prior information" and "priors" may refer to data (e.g., one or more images or scans and/or one or more images of other imaging modalities (e.g., CT-scans) of a population and/or the patient) external to the data provided by the EIT data that may inform (e.g., provide additional information to) an image generation process (e.g., the reconstruction process). For instance, the prior information may include one or more computerized tomography (CT) scans that may be utilized to add prior information into the EIT reconstruction algorithms. In particular, the CT scans can provide anatomy-based and/or physiological-based priors, and the initial impedance image may be reconstructed based at least partially on the information the anatomy-based priors provide. For example, the CT scans (e.g., the priors) may be used to build a regularization term for the regularization methods described above. Additionally, the prior information may be utilized to build a regularization term and determine an approximation error. In some embodiments, the prior information may include images from other imaging modalities and/or data from other sensors (e.g., pneumotachograph, plethysmograph, etc.).

In some embodiments, the prior information may be specific to the domain (e.g., a patient) for which an initial impedance image is being reconstructed. For example, the prior information may include CT-scans (or other data) of the domain (e.g., CT-scans at or proximate where EIT data is measure (e.g., at or proximate the electrode belt of the EIT system)). In other embodiments, the prior information may include CT-scans (or other data) of one or more anatomy or physiological models (e.g., physical models). In further embodiments, the prior information may include data from one or more digital models and/or simulations. In some embodiments, prior information may be selected and/or obtained based at least partially on a class of the domain (e.g., an age, gender, race, weight, etc. For example, in some embodiments, prior information may be selected and/or obtained based at least partially on anthropometric measures. In additional embodiments, the prior information may be selected and/or obtained based at least partially on a type of diagnostic procedure (e.g., a pneumothorax, an FRC, a tidal volume, and/or a cellularity on pleural fusion diagnostic procedure) to be performed with eventual generated data (e.g., a fluid estimation values). In further embodiments, the prior information may be selected and/or obtained based at least partially on a type of volumetric image to be generated (e.g., an air, a blood, and/or a water volumetric image).

In some embodiments utilizing prior information in reconstructing the initial impedance image may include using the prior information (e.g., CT scans) as anatomical atlases in reconstructing the initial impedance image. In such embodiments, the probability density functions of a resistivity distribution in the region of the body, π(ρSW) may be represented as the following Gaussian distribution: <MAT>.

In the above equation, the mean (ρsw) and covariance matrix (Γsw) of the probability density function may be utilized to estimate the initial absolute impedance images. As noted above, the prior information is the regularization term (second term (i.e., the second line of the above equation)) of the following equation (<NUM>): <MAT> where F is an Gaussian high pass filter, Γsw is a covariance matrix of a statistical prior, interpolated to an Finite Element Mesh (FEM), ρsw is an expected vector of resistivities of the statistical prior, which is also interpolated to the FEM mesh, and y is the regularization parameter for the prior information. A third term (i.e., the third line of the above equation) may not be necessary if the prior information is informative enough in all the directions (e.g., x, y, and z directions) in which the first term (i.e., the first line of the above equation) may not be informative.

In equation (<NUM>), the first term (i.e., the first line) originates from a likelihood, the second term (i.e., the second line) is the prior information, and the third term (i.e., the third line) is a smoothness prior. Equation (<NUM>) penalizes spatial high frequency image components of the difference between candidate ρ and the statistically expected (ρsw). As a non-limiting example, reconstructing the initial impedance image utilizing prior information may include reconstructing an initial impedance image via any of the manners described in <NPL>, the disclosure of which is incorporated in its entirety by reference herein.

Upon reconstructing the initial impedance image, the method <NUM> may include generating and/or outputting the reconstructed initial impedance image, as shown in act <NUM> of <FIG>. For example, outputting the reconstructed initial impedance image may include generating a data package including/representing the initial impedance image and/or storing the initial impedance image in a database (e.g., database <NUM>). In some embodiments, the reconstructed initial impedance image may be represented by pixels (e.g., two-dimensional images), by voxels (three-dimensional images), by elements of the FEM, or any combination of the foregoing elements.

Referring to acts <NUM>-<NUM> together, in some embodiments, the method <NUM> may include generating a plurality of initial impedance images. For example, the method <NUM> may include generating a plurality of initial impedance images along a plurality of planes (e.g., slices) or sections along an axial length of the domain (e.g., patient).

Referring still to <FIG>, the initial impedance image and the electrical properties represented therein may be underestimated in one or more regions (e.g., underestimated due to the linearity of, for example, the Gauss-Newton reconstruction method), may have relatively low spatial resolution, may include image artifacts, etc. Therefore, an enhancement (e.g., correction) of the initial impedance image may be desirable to improve a diagnostic quality of the initial impedance image (e.g., an ability to perform a diagnoses from the initial impedance image or resulting volume estimations determined from the initial impedance image).

<FIG> is a flowchart of a method <NUM> of enhancing an initial impedance image (e.g., the reconstructed initial impedance image described above in regard to <FIG>) according to one or more embodiments of the present disclosure.

In one or more embodiments, the method <NUM> may include receiving an initial impedance image, as shown in act <NUM> of <FIG>. In some embodiments, the method <NUM> may include receiving the initial impedance image from an EIT system (e.g., the EIT system <NUM>). In one or more embodiments, the method <NUM> may include the volume estimation system <NUM> receiving the initial impedance image from the EIT system <NUM>. In additional embodiments, the method <NUM> may include receiving the initial impedance image from an additional system (e.g., additional system <NUM>), querying a database (e.g., database <NUM>) to retrieve the initial impedance image, or receiving the initial impedance image from an external source. As noted above, the initial impedance image may be represented by pixels (e.g., two-dimensional images), by voxels (three-dimensional images), by elements of the FEM, or any combination thereof.

Responsive to receiving and/or retrieving the initial impedance image, the method <NUM> may further include directly enhancing (e.g., correcting) the initial impedance image, as shown in act <NUM> of <FIG>. In particular, the method <NUM> may include enhancing the initial impedance image without additional information input (e.g., prior information).

In one or more embodiments, directly enhancing (e.g., correcting) the initial impedance image may include enhancing the initial impedance image through a D-bar method. For example, enhancing the initial impedance image may include enhancing the initial impedance image through any of the manners described in <NPL>, the disclosure of which is incorporated in its entirety by reference herein. Furthermore, enhancing the initial impedance image may include enhancing the initial impedance image through any conventional D-bar methods.

Enhancing the initial impedance image via a D-bar method (or any other reconstruction method) may estimate true or relatively close values of the electrical properties represented by, for example, the pixels of the initial impedance image of a region of interest of the domain (e.g., patient). Therefore, enhancing the initial impedance image via a D-bar method may correct electrical properties represented in the initial impedance image of the region of interest of the domain (e.g., patient). As a non-limiting example, enhancing the initial impedance image via D-bar method may include identifying organs (e.g., lungs, heart, kidneys) and bones from the initial impedance image and then adjusting the associated electrical properties represented in the areas of the initial impedance identified as organs and/or bones based on the identified organs and/or bones via the D-bar method.

Upon enhancing the initial impedance image, the method <NUM> may include outputting an enhanced impedance image, as show in act <NUM> of <FIG>. For example, the volume estimation system <NUM> may output the enhanced impedance image. In some embodiments, outputting the enhanced impedance image may include outputting an enhanced absolute impedance image. In additional embodiments, outputting the enhanced impedance image may include outputting an enhanced difference impedance image. In one or more embodiments, outputting the enhanced impedance image may include may include generating a data package including the enhanced impedance image and/or storing the enhanced impedance image in a database (e.g., database <NUM>). In some embodiments, the enhanced impedance image may be represented by pixels (e.g., two-dimensional images), by voxels (three-dimensional images), by elements of a FEM, or any combination thereof.

<FIG> is a flowchart of a method <NUM> of enhancing an initial impedance image (e.g., the reconstructed initial impedance described above in regard to <FIG>) according to one or more additional embodiments of the present disclosure. <FIG> is a schematic block diagram of the method <NUM> of <FIG>. The method <NUM> may include utilizing prior information to enhance the initial impedance image.

In one or more additional embodiments, enhancing (e.g., correcting) the initial impedance image may include enhancing the initial impedance image through a Schur complement approach. In particular, the Schur complement approach may utilize Schur complement properties to introduce statistical prior information into a D-bar method and may provide a significant improvement in spatial resolution of the initial impedance image. In other embodiments, the Schur complement may be used as a direct enhancement method as described above in regard to <FIG>. <FIG> is a schematic block diagram that depicts portions of the Schur complement approach utilizing prior information (e.g., information from prior models).

The prior information and/or prior models may include segmented tomographic images of a portion of a body (e.g., a thorax) of the domain (e.g., patient), and the electrical property distribution of the tissues may be utilized to perform numerical phantoms (described in greater detail below). For example, a CT-scan may be segmented into different tissues (e.g., bones, aerated lungs, atelectasis, heart, and/or muscles) according to the respective tissue's characteristics in regard to grey levels. As a result, the correct electrical property distributions within the initial impedance image may be generated (e.g., built, adjusted, and/or corrected) using the electrical property distribution of the tissues (which may have been measured in vivo).

Referring to <FIG> together, in some embodiments, the method may include introducing prior information based on Schur Complement properties. For example, the method may include applying a correction (e.g., post processing) to the initial impedance image by maximizing a conditional probability density function of an image that is consistent with the prior information, considering the initial impedance image.

Based on Schur complement properties, a conditional mean µx|y may be determined by the following equation (<NUM>) <MAT>.

From one or more (e.g., a set of) prior samples <MAT>, an estimate of the probability distribution of a population π(x) may be determined. The one or more prior samples σ3D may be determined by segmenting tomography images/scans, and electrical properties may be established for each region identified in a resulting segmentation.

For each resulting electrical property distribution <MAT> (e.g., segmented prior samples), two images may be determined (e.g., computed). A first determined image ( <MAT>) may represent an electrical property distribution of <MAT>, where the electrode belt is oriented on the thorax of the domain. To obtain the first image <MAT>, a Gaussian interpolation may be applied to σ3D and may account for a specific height and thickness of <MAT>. A second determined image ( <MAT>) may represent a reconstructed image of <MAT>. To obtain the second image <MAT>, first, a vector <MAT>, which represents the forward problem of the electrical property distribution <MAT>, may be determined (e.g., calculated), and second, the second image <MAT> may be estimated by a reconstruction method using the determined vector <MAT> (i.e., the determined voltages).

The two resulting images <MAT> and <MAT> may be converted into vectors and may be represented by xi and yi respectively. Additionally, a new vector zi may be generated by concatenating xi and yi <MAT>.

Additionally, by determining estimates of xi and yi for multiple prior samples <MAT>, an expected value µz and the covariance Γz of the z process may be determined. From the expected value µz and the covariance Γz of the z process, a statistics and relation between a set of images xi and yi may be obtained. <MAT> <MAT> where µx and µy are the expected values of the xi and yi set of images, Γxx and Γyy are the covariance matrices of each set of images, and Γxy and Γyx are their cross-covariance matrices.

An estimate of the vector xi may be determined from yi by using the prior statistics and relation between the set of images xi and yi.

By reordering equation (<NUM>), a first-order correction for the initial impedance image ( <MAT>) from the reconstruction method may be determined. The correction may consider the prior distribution of a population and the forward problem model. <MAT> <MAT>.

In summary, by using the Schur complement approach described herein, regional averages and conditional covariances may be determined, that the regional averages and conditional covariances may be utilized to determine (e.g., calculate) corrected impedances of the initial impedance image. Accordingly, the initial impedance image may be enhanced utilizing the Schur complement approach described above. Additionally, the initial impedance image may be enhanced using any combination of an alternative reconstruction method enhancement (e.g., D-bar method) and Schur complements approach described above.

In additional embodiments, as noted above, the initial impedance image may be enhanced via numerical phantom simulations. In particular CT-scans may be segmented and transformed into electrical property distributions according to the electrical property distribution of each segmented tissue. Subsequently, numerical phantoms may be generated for these electrical property distributions, and reference reconstruction images may be determined based at least partially on these electrical property distributions.

Furthermore, based on a comparison between the electrical property distributions represented in the initial impedance image and the reference reconstructed images, a relationship function (e.g., linear function or non-linear function) may be determined and utilized to adjust the electrical properties within a region of interest of the initial impedance image. In some instances, various machine learning models may be utilized within the process of enhancing the initial impedance images. For instance, enhancing the initial impedance image may include machine learning and/or deep learning techniques that include providing training corpora to a matching learning algorithm or neural network to train a machine to aid or perform enhancing the initial impedance image or portions of the initial impedance image. In some embodiments, the volume estimation system <NUM> may enhance at least a portion of the initial impedance image one or more of regression models (e.g., a set of statistical processes for estimating the relationships among variables), classification models, and/or phenomena models. Additionally, the machine-learning models may include a quadratic regression analysis, a logistic regression analysis, a support vector machine, a Gaussian process regression, ensemble models, or any other regression analysis. Furthermore, in yet further embodiments, the machine-learning models may include decision tree learning, regression trees, boosted trees, gradient boosted tree, multilayer perceptron, one-vs-rest, Naive Bayes, k-nearest neighbor, association rule learning, a neural network, deep learning, pattern recognition, or any other type of machine-learning.

In some embodiments, enhancing the impedance image may include any combination of the methods described above in regard to <FIG>. For example, one or more portions (e.g., pixels and/or regions) of the initial impedance image may be enhanced via a first method and one or more other portions (e.g., pixels and/or regions) of the initial impedance image may be enhanced via a second, different method.

Upon enhancing the initial impedance image, the method <NUM> may include outputting an enhanced impedance image, as show in act <NUM> of <FIG>. For example, the volume estimation system <NUM> may output the enhanced impedance image. In some embodiments, outputting the enhanced impedance image may include outputting an enhanced absolute impedance image. In additional embodiments, outputting the enhanced impedance image may include outputting an enhanced difference impedance image. In one or more embodiments, outputting the enhanced impedance image may include generating a data package including/representing the enhanced impedance image and/or storing the enhanced impedance image in a database (e.g., database <NUM>). In some embodiments, the enhanced impedance image may be represented by pixels (e.g., two-dimensional images), by voxels (three-dimensional images), by elements of a FEM, or any combination thereof.

<FIG> is a flowchart of a method <NUM> of determining a fluid volume estimation within a region of a body. <FIG> is a schematic block diagram showing the method <NUM> of <FIG>.

Referring to <FIG> and <FIG> together, the method <NUM> may include receiving an enhanced impedance image, as shown in act <NUM> of <FIG>. For example, the method <NUM> may include receiving any of the enhanced (e.g., corrected) impedance images discussed above in regard to <FIG>. In particular, the volume estimation system <NUM> may receive the enhanced impedance image. As noted above, in some embodiments, the enhanced impedance image may include an enhanced absolute impedance image or an enhanced difference impedance image. In some embodiments, receiving an enhanced impedance image may include receiving a data package representing the enhanced impedance image and/or querying the database <NUM> to retrieve the enhanced impedance image.

Responsive to receiving the enhanced impedance image, the method <NUM> may include segmenting the enhanced impedance image, as shown in act <NUM> of <FIG>. For example, the method <NUM> may include applying one or more segmentation processes to the enhanced impedance image. For instance, the volume estimation system <NUM> may partition the enhanced impedance image into multiple segments (e.g., sets of pixel and/or image object).

Segmenting the enhanced impedance image may include locating objects (e.g., lungs, heart, kidneys, bones, and/or other organs) and boundaries (e.g., lines, curves, etc.) within the images. For instance, the result of the image segmentation may include a set of segments that collectively cover the entire enhanced image and/or a set of contours extracted from the image (e.g., edge detection). Segmenting the enhanced impedance image may include applying any conventional image segmentation process to the enhanced impedance images.

In some embodiments, the image segmentation process may be informed by prior information. For example, in some embodiments, segmenting the enhanced impedance image may include utilizing one or more priors (e.g., CT-scans) that have also been segmented to identify objects (e.g., organs and/or bones) and/or regions of interest within the enhanced impedance image. As another non-limiting example, segmenting the enhanced impedance image may include utilizing a mean contour of a segmented object (e.g., organ) within the one or more priors (e.g., CT-scans) to select a region of interest of as a region of the segmented object.

Upon segmenting the enhanced impedance image, the method <NUM> may include selecting a region of interest within the enhanced impedance image, as shown in act <NUM> of <FIG>. For instance, the method <NUM> may include selecting a region of interest within the enhanced impedance image via any of the manners described above in regard to act <NUM> of <FIG> (e.g., utilizing prior information). In some embodiments, selecting a region of interest within the enhanced impedance image may include selecting a region representing one or more organs or one or more bones or any other part of a body of a patient. For example, selecting a region of interest within the enhanced impedance image may include selecting a region representing a lungs region of the patient. In one or more embodiments, selecting a region of interest within the enhanced impedance image may include selecting a region representing a portion of the body to be analyzed (e.g., a portion of the body where a measurement of a fluid volume is desired).

The selected region of interest of the enhanced impedance image may provide (e.g., connote, represent, define) an electrical property distribution through the selected region via values represented by the pixels, voxels, and/or FEM elements of the enhanced impedance image.

Responsive to selecting the region of interest of the enhanced impedance image, the method <NUM> may include determining a relationship parameter (VEIT) that relates the electrical properties (e.g., impedances) represented in the selected region of interest with one or more other properties of the patient within the region of interest, as shown in act <NUM> of <FIG>. For example, the relationship parameter (VEIT) may include a linear or nonlinear function that relates fluid content and electrical properties of the region of interest. In some embodiments, the one or more other properties may include air content (e.g., a total volume of air and/or air volume indicated by one or more pixels, voxels, and/or elements of a FEM) in the region, blood content in the region, tissue content in the region.

In some embodiments, the relationship parameter (VEIT) may be determined from the electrical properties (e.g., impedances) represented in the selected region of interest. For instance, the relationship parameter (VEIT) may be determined via a weighted sum model. For example, the relationship parameter (VEIT) may be determined via the following weighted sum: <MAT> where ρi is an electrical resistivity distribution represented in the enhanced impedance image of the region of interest, and vi is a tetrahedron volume of a Finite Element Mesh representation of the region of interest. Alternatively, vi is pixel area and/or voxel volume represented in the enhanced impedance image. In view of the foregoing, the relationship parameter (VEIT) may be unique to the selected region of interest. In some embodiments, a relationship parameter may be determined for each pixel, voxel, and/or FEM element of the enhanced impedance image.

Upon determining the relationship parameter (VEIT), the method <NUM> may include estimating a fluid volume (Vfluid (e.g., Vair)) within the region of interest, as shown in act <NUM> of <FIG>. For example, the volume estimation system <NUM> may estimate a fluid volume within the region of interest. An example method of estimating the fluid volume is described below.

A variation of the electrical resistivity distribution represented in the region of interest in the enhanced impedance image may correlate substantially linearly to a fluid content variation in the region of interest of the domain (e.g., the selected region of interest). Accordingly, based on the determined relationship parameter (VEIT), estimating the fluid volume (Vfluid) may include estimating the fluid volume (Vfluid) via a linear model. For example, estimating the fluid volume (Vfluid) may include estimating the fluid volume (Vfluid) via the following model: <MAT>.

Variables a and b may be calculated (e.g., estimated) by relating an air content estimated from prior information (e.g., CT-scans), as described above in regard to act <NUM> of <FIG>, and the relationship parameter (VEIT). Estimating variables a and b is described in greater detail below in regard to <FIG> and Tables <NUM>-<NUM>.

In some embodiments, estimating the fluid volume (Vfluid) may be informed by prior information, as shown in <FIG>. For example, in some embodiments, estimating the fluid volume (Vfluid) include utilizing one or more priors (e.g., CT-scans) to determine a fluid content by correlating the Hounsfield unit scale and a fluid volume in a tissue of the region of interest (e.g., the tissue of lungs). As is known in the art, the Hounsfield unit scale is a linear transformation of an original linear attenuation coefficient measurement into one in which a radiodensity of water (e.g., distilled water) at standard pressure and temperature (STP) is defined as zero Hounsfield units (HU), while a radiodensity of air at STP is defined as -<NUM> HU. In a voxel with an average linear attenuation coefficient µ, the corresponding HU value is therefore given by: <MAT> where µwater and µair are respectively the linear attenuation coefficients of water and air. In view of the foregoing, a change of one Hounsfield unit (HU) represents a change of <NUM>% of the attenuation coefficient of water because the attenuation coefficient of air is about zero.

In view of the foregoing, fluid content (e.g., air content) indicated by the prior information (e.g., CT-scan) can be correlated to the relationship parameter (VEIT) determined from the enhanced impedance image. Furthermore, based on the correlation be between the fluid content indicated by the prior information and the relationship parameter (VEIT), the relationship parameter (VEIT) may be refined, and the ultimate estimation of the fluid volume (Vfluid) may be refined. Additionally, in some embodiments, (VEIT) and (Vfluid) may be correlated through, for example, linear or non-linear regression, machine learning, etc. Furthermore, in some embodiments, parameters a and b may be estimated by minimizing a mean square error between the priors and the (VEIT) estimation. Additionally, (Vfluid) may be estimated by computing the maximum a posteriori of a conditional mean of (Vfluid) based at least partially on the enhanced impedance image.

As mentioned above, in some embodiments, estimating a fluid volume (Vfluid) may include estimating a volume of one or more of air, blood, water, and/or tissue. Responsive to estimating the fluid volume (Vfluid), the method <NUM> may include outputting a representation (e.g., value, image, etc.) of the estimated fluid volume (Vfluid), as shown in act <NUM> of <FIG>.

For example, the volume estimation system <NUM> may output the representation of the estimated fluid volume. In some embodiments, outputting the representation of the estimated fluid volume may include outputting an overall value representative of the estimated volume of a given fluid. In additional embodiments, outputting the representation of the estimated fluid volume may include outputting a volumetric image where each pixel, voxel, and/or element of a FEM represents a volume of a given fluid in the region associated with the pixel, voxel, and/or element. In some embodiments, outputting the representation of the estimated fluid volume may include outputting a representation of an absolute volume estimate. In additional embodiments, outputting the representation of the estimated fluid volume may include outputting a representation of a difference volume estimate. In one or more embodiments, outputting the representation of the estimated fluid volume may include generating a data package including the representation of the estimated fluid volume and/or storing the representation of the estimated fluid volume in a database (e.g., database <NUM>).

The following description referring to <FIG> describes a plurality of tests performed by the inventors for estimating fluid volumes within regions of a plurality of domains per the methods and processes described herein. The experiments included utilizing an EIT system to acquire voltage measurements. The electrodes for applying and detecting voltages were oriented in a zig-zag pattern around the domains. In the experiments, the domains included twelve piglets. The applied current patterns included sinusoidal, 10mA, <NUM>, and adjacent current patterns (skip-<NUM>).

Ten piglets were utilized to build anatomical atlases (mean and covariance) (e.g., to build prior information). The fluid estimation methods described herein were performed on the two remaining piglets, which were not included in the prior information.

Four CT-scans were taken of each of the ten piglets being utilized to build anatomical atlases. Each of the CT-scans were segmented and processed per the method described above in regard to <FIG>. <FIG> shows example segmented and processed images.

Additionally, small perturbations simulating abnormal lungs were introduced to prior models (e.g., artificial models), CT-scans were taken of the prior models, and additional segmented images were artificially generated. The small perturbations were introduced by removing spherical objects from random positions in the modeled lungs and/or the modeled hearts. Table <NUM> shows the determined mean and standard deviations of the segmented tissues of <FIG> based on the measured electrical properties.

<FIG> shows the generated prior information used to reconstruct impedance images of the ten piglets using a Gauss-Newton method. The anatomical atlases generated from the ten piglets and a high-pass Gaussian filter were used to regularize the inverse problem, and the conductivity distribution was estimated (e.g., reconstructed).

The initial impedance image (the conductivity electrical distribution) is estimated by using a finite element mesh, as depicted in <FIG>, and by utilizing the prior information to regularize the inverse problem. Additionally, an approximation error theory was applied using a refined finite element mesh, as depicted in <FIG>.

Additionally, voltage measurements were acquired for the two remaining piglets. <FIG> shows an example mean of the voltage measurements (e.g., mean of all electrode measurements) of a first pig of the two piglets obtained during an EIT process (e.g., described above in regard to <FIG>), and the window during which at least one initial impedance image was reconstructed via any of the manners described above. The same process was applied to the second pig of the two piglets. Furthermore, CT-scans were acquired from each of the two piglets. <FIG> show an example CT-scan at a center of the electrode belt of one of the first piglet, and a reconstructed initial impedance image of the first piglet, respectively. <FIG> show additional reconstructed impedance images of the first piglet at different applied positive end expiratory pressure (PEEP) levels. <FIG> show reconstructed impedance images of the second piglet at various applied PEEP levels.

Referring to <FIG>, for the first piglet, the heart was displaced compared to the anatomical atlas information; therefore, the reconstructed initial impedance image followed a true position instead of the prior information position.

Table <NUM> shows the functional residual capacity (FRC) of the first and second piglets determined from the CT-scans of the first and second piglets.

The lungs regions within the reconstructed initial impedance images of the first and second piglets were identified by applying a threshold on the mean conductivity distribution of the prior information and removing portions of the reconstructed initial impedance images not meeting the threshold, as depicted in <FIG>. An additional threshold was applied to remove the heart region from the reconstructed initial impedance images.

Furthermore, the weighted sum model of the lungs region can be determined via: <MAT> as discussed above in regard to <FIG> and <FIG>, where ρi is an electrical resistivity distribution represented in an enhanced impedance image of the region of interest, and vi is a tetrahedron volume of a Finite Element Mesh representation of the region of interest.

Continuing with methods described above in regard to <FIG> and <FIG>, a first order correction was applied to estimate a fluid volume (Vfluid) via the following linear model: <MAT>.

Variables a and b may be calculated (e.g., estimated) by relating an air content (Vfluid) estimated from prior information (e.g., CT-scans of another piglet (e.g., the second piglet)). In particular, the VEIT(first) of the first piglet may be determined and linear correction applied (e.g., variables a and b) to align with the Vflui (first) determined from the CT-scans of the first piglet. Thereafter, the Vfluid(second) is determined by using VEIT(second)and the determined linear correction (e.g., variables a and b) from the first piglet. Additionally, the above process was repeated using the second piglet to determine variables a and b and the associated linear correction.

Table <NUM> shows the air volume estimation of the first piglet when using the first piglet to determine variables a and b, where a = <NUM>e + <NUM> and b = -<NUM>e + <NUM>.

Table <NUM> shows the air volume estimation of the second piglet when using the first piglet to determine variables a and b.

Table <NUM> shows the air volume estimation of the second piglet when using the second piglet to determine variables a and b, where a = <NUM>e + <NUM> and b = -<NUM>e + <NUM>.

Referring to <FIG> together, the methods of determining volume estimations described herein may be advantageous. In particular, by enhancing the impedance images per the methods described herein, more accurate absolute impedance images may be generated in comparison to conventional absolute impedance images. For example, due to the anatomical atlases and prior information and enhancement methods described herein, the enhanced absolute impedance images described herein may be more accurate than conventional absolute impedance images. Additionally, the methods described herein using prior information improves organ localization during D-bar, iterative (e.g., Monte Carlo), and Kalman filter methods.

Determining the absolute impedance images and fluid volume estimates in the methods described herein may enable air volume estimation, cellularity on pleural effusions and pneumothorax volume determination, heart stroke volume estimation, total thoracic air volume determination, FRC, etc..

<FIG> illustrates a block diagram of an example volume estimation system <NUM> that may be configured to perform one or more of the processes described above. One will appreciate that one or more computing devices may form the volume estimation system <NUM>. As shown by <FIG>, the volume estimation system <NUM> can comprise a processor <NUM>, a memory <NUM>, a storage device <NUM>, an I/O interface <NUM>, and a communication interface <NUM>, which may be communicatively coupled by way of a communication infrastructure. While an example volume estimation system <NUM> is shown in <FIG>, the components illustrated in <FIG> are not intended to be limiting. Additional or alternative components may be used in other embodiments. Furthermore, in certain embodiments, the volume estimation system <NUM> can include fewer components than those shown in <FIG>. Components of the volume estimation system <NUM> shown in <FIG> will now be described in additional detail.

In one or more embodiments, the processor <NUM> includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, the processor <NUM> may retrieve (or fetch) the instructions from an internal register, an internal cache, the memory <NUM>, or the storage device <NUM> and decode and execute them. In one or more embodiments, the processor <NUM> may include one or more internal caches for data, instructions, or addresses. As an example, and not by way of limitation, the processor <NUM> may include one or more instruction caches, one or more data caches, and one or more translation look aside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in the memory <NUM> or the storage device <NUM>.

The volume estimation system <NUM> includes memory <NUM>, which is coupled to the processor(s) <NUM>. The memory <NUM> may be used for storing data, metadata, and programs for execution by the processor(s). The memory <NUM> may include one or more of volatile and non-volatile memories, such as Random-Access Memory ("RAM"), Read-Only Memory ("ROM"), a solid state disk ("SSD"), Flash, Phase Change Memory ("PCM"), or other types of data storage. The memory <NUM> may be internal or distributed memory.

The volume estimation system <NUM> includes a storage device <NUM> that includes storage for storing data or instructions. As an example, and not by way of limitation, storage device <NUM> can comprise a non-transitory storage medium described above. The storage device <NUM> may include a hard disk drive (HDD), a floppy disk drive, Flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. The storage device <NUM> may include removable or non-removable (or fixed) media, where appropriate. The storage device <NUM> may be internal or external to the volume estimation system <NUM>. In one or more embodiments, the storage device <NUM> is non-volatile, solid-state memory. In other embodiments, the storage device <NUM> includes read-only memory (ROM). Where appropriate, this ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or Flash memory or a combination of two or more of these.

The volume estimation system <NUM> also includes one or more input or output ("I/O") devices/interfaces <NUM> (e.g., a touch display), which are provided to allow a user to provide input to, receive output from, and otherwise transfer data to and receive data from volume estimation system <NUM>. The I/O devices/interfaces <NUM> may include a mouse, a keypad or a keyboard, a touch screen, a camera, an optical scanner, network interface, modem, other known I/O devices or a combination of such I/O device/interfaces. The touch screen may be activated with a stylus or a finger.

The I/O devices/interfaces <NUM> may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain embodiments, the I/O interface <NUM> is configured to provide graphical data to a display for presentation to a user.

The volume estimation system <NUM> can further include a communication interface <NUM>. The communication interface <NUM> can include hardware, software, or both. The communication interface <NUM> can provide one or more interfaces for communication (such as, for example, packet-based communication) between the volume estimation system <NUM> and one or more other computing devices or networks. As an example, and not by way of limitation, the communication interface <NUM> may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI and/or Bluetooth. The volume estimation system <NUM> can further include a bus <NUM>. The bus <NUM> can comprise hardware, software, or both that couples components of volume estimation system <NUM> to each other.

Claim 1:
A computer-implemented method of estimating a fluid volume, the method comprising:
receiving electrical tomography data of a portion of a domain;
reconstructing an initial impedance image based at least partially on the electrical tomography data;
enhancing the initial impedance image to generate an enhanced impedance image;
segmenting the enhanced impedance image to identify one or more tissues depicted within the enhanced impedance image;
selecting a region of interest within the enhanced impedance image;
determining a relationship parameter that relates electrical properties represented within the region of interest of the enhanced impedance image with one or more properties of the region of interest; and
estimating an absolute fluid volume within the region of interest based at least partially on the relationship parameter and the enhanced impedance image.