Electrical impedance tomography systems and associated methods

Abstract

An EIT system includes an EIT device configured to capture impedance images of a patient, a display configured to display information to a user, and a memory device. The EIT system is configured to generate a ventilation map of lungs of the patient. The EIT system is also configured to generate a perfusion map of the lungs of the patient. The EIT system is further configured to compare ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map and generate a V̇ / Q̇ map. The pixels in the V̇ / Q̇ map are separated into at least three groups including mostly ventilated pixels, mostly perfused pixels, and balanced pixels. The V̇ / Q̇ map is produced on the display where each of the mostly ventilated pixels, the mostly perfused pixels, and the balanced pixels are graphically distinguished.

Description

[0001] ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEMS AND ASSOCIATED METHODS

[0002] PRIORITY CLAIM

[0003] This application claims the benefit of the filing date of United States Provisional Patent Application Serial No. 63 / 688,178, filed August 28, 2024, for “ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEMS AND ASSOCIATED METHODS,” the disclosure of which is hereby incorporated herein in its entirety by this reference.

[0004] TECHNICAL FIELD

[0005] This disclosure relates generally to electrical impedance tomography systems. In particular, this disclosure relates to electrical impedance tomography systems and methods of measuring perfusion in a lung and determining a likelihood of pulmonary emboli.

[0006] BACKGROUND

[0007] 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.

[0008] 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 that often overestimates values of some pixels and underestimates the values of other pixels of the region of interest.

[0009] BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG. 1 is a schematic diagram of a portion of an electrical property tomography system showing a plurality of electrodes positioned around a region of interest of a patient according to one or more embodiments of the present disclosure;

[0011] FIG. 2 is a schematic diagram showing a cross-section of the thorax of the patient along the plane of the electrodes according to one or more embodiments of the present disclosure;

[0012] FIG. 3 is a schematic block diagram of an electrical property tomography system according to an embodiment of the disclosure;

[0013] FIG. 4 illustrates a schematic diagram of an environment in which a volume estimation system can operate according to one or more embodiments of the present disclosure;

[0014] FIG. 5 illustrates graphical representations of ventilation, perfusion, and a comparison between the ventilation and perfusion images in accordance with embodiments of the disclosure;

[0015] FIG. 6 illustrates pulmonary emboli graph in accordance with embodiments of the disclosure; and

[0016] FIG. 7 is a flow chart of a method of determining a likelihood of pulmonary emboli in accordance with embodiments of the disclosure.

[0017] MODE(S) FOR CARRYING OUT THE INVENTION

[0018] 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. From this disclosure, various substitutions, modifications, additions rearrangements, or combinations thereof within the scope of the disclosure may be made and will become apparent to those of ordinary skill in the art.

[0019] 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. In addition, some of the drawings may be simplified for clarity. Thus, the drawings may not depict all of the components of a given apparatus or all operations of a particular method.

[0020] Information and signals described herein may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. 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.

[0021] 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 a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. 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. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0022] 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.

[0023] As used herein, the singular forms following “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0024] 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.

[0025] 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.

[0026] 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 90% met, at least about 95% met, or even at least about 99% met.

[0027] 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.).

[0028] As used herein, the term “ventilation” refers to a representation of air received in the lungs of a patient. It means and includes a depiction of regional air movement and volume change within pulmonary structures as measured by cyclic impedance variations during tidal breathing. For example, ventilation may be characterized by measuring tidal impedance changes (AZ) for each pixel, averaged over a series of breaths to yield a spatial map of relative air distribution.

[0029] As used herein, the term “perfusion” refers to a representation of blood passing through the lungs of a patient. It means and includes a depiction of regional pulmonary blood flow as measured by first-pass kinetics of an injected bolus of a contrast.

[0030] 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 a device that measures one or more electrical conductivity, electrical resistivity, electrical permittivity, electrical admittivity, 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.

[0031] 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.

[0032] FIG. 1 is a schematic diagram of a portion of an electrical impedance tomography (EIT) system 100 showing a plurality of electrodes 110 positioned around a region of interest (e.g., thorax) of a patient 105. The electrodes 110 of the EIT system 100 may be physically held in place by an electrode belt 103. The placement of the electrodes 110 may be transverse to a cranial caudal axis 104 of the patient. Although the electrodes 110 are shown in FIG. 1 as being placed only partially around the patient 105, electrodes 110 may be placed around the entire patient 105 depending on the specific region of interest available or desired for measurement. Furthermore, the electrodes 110 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 110 may be operably coupled to a computing system (not shown) configured to control the operation of the electrodes 110 and perform reconstruction of an EIT image.

[0033] FIG. 2 is a schematic diagram showing a cross-section of the thorax of the patient 105 along the plane (line 102) of the electrodes. A voltage may be applied to a pair of electrodes 110 (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., Vi, V2, V3 . . . Vn) may be detected by the other electrodes and measured by the EIT system 100. Current injection may be performed for a measurement cycle according to a circular pattern using different electrode pairs to generate the excitation current.

[0034] FIG. 3 is a schematic block diagram of an EIT system 300 according to an embodiment of the disclosure. The EIT system 300 may include an electrode belt 310 operably coupled with a data processing system 320. The electrode belt 310 and the data processing system 320 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 320 may include a processor 322 operably coupled with an electronic display 324, input devices 326, and a memory device 328. The electronic display 324 may be constructed with the data processing system 320 into a singular form factor for an EIT device coupled with the electrode belt 310. In some embodiments, the electronic display 324 and the data processing system 320 may be separate units of the EIT device coupled with the electrode belt 310. In yet other embodiments, an EIT system 300 may be integrated within another host system configured to perform additional medical measurements and / or procedures, in which the electrode belt 310 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 to field use or updated after installation.

[0035] The processor 322 may coordinate the communication between the various devices as well as execute instructions stored in computer-readable media of the memory device 328 to direct current excitation, data acquisition, data analysis, and / or image reconstruction. As an example, the memory device 328 may include a library of finite element meshes used by the processor 322 to model the patient's body in the region of interest for performing image reconstruction. Input devices 326 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 322 to receive inputs from an operator of the EIT system 300. Thus, for a touch screen interface the electronic display 324 and the input devices 326 receiving user input may be integrated within the same device. The electronic display 324 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 322 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 324.

[0036] The EIT system 300 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 310 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.

[0037] FIG. 4 illustrates a schematic diagram of an environment 400 in which a volume estimation system can operate according to one or more embodiments of the present disclosure. As illustrated, the environment 400 includes an EIT system 402, a volume estimation system 404, a network 406, and one or more additional system(s) 408. The volume estimation system 404, the EIT system 402, and the additional system(s) 408 can communicate via the network 406. The network 406 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. 4 illustrates a particular arrangement of the EIT system 402, the volume estimation system 404, the additional system(s) 408, and the network 406, various additional arrangements are possible. For example, the volume estimation system 404 may directly communicate with the EIT system 402, bypassing the network 406.

[0038] As illustrated in FIG. 4, a user 410 can interface with the volume estimation system 404 to initiate one or more volume estimations and / or any of the methods described herein. The user 410 can be an individual (i.e., human user), a business, a group, or any other entity. Although FIG. 4 illustrates only one user 410 associated with the volume estimation system 404, the environment 400 can include any number of a plurality of users that each interact with the environment 400 using a corresponding volume estimation system 404.

[0039] In some embodiments, a volume estimation system 404 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 404 may provide a platform, or backbone, which other systems, such as the additional systems 408, may use to initiate fluid volume estimations within regions of interest of a domain (e.g., a patient).

[0040] In one or more embodiments, the volume estimation system 404 may perform reconstruction algorithms, correction algorithms, and / or volumetric calculation algorithms, as detailed below. In some embodiments, the volume estimation system 404 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 402. 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.

[0041] As shown in FIG. 4, in some embodiments, the volume estimation system 404 can include a database 414. As is described in greater detail below, the volume estimation system 404 can utilize the database 414 to store initial impedance images, enhanced impedance images, and / or fluid volume estimations and values.

[0042] In some embodiments, the volume estimation system 404 further includes a client application 412 installed thereon. In one or more embodiments, the client application 412 can be associated with the volume estimation system 404. For example, the client application 412 allows the user 410, the EIT system 402, and / or the additional systems 408 to directly or indirectly interface with the volume estimation system 404, the EIT system 402, and / or the additional systems 408. For example, the client application 412 can include a web browsing application and / or a specific volume estimation application.

[0043] The additional systems 408 may include additional systems that may interface with the volume estimation system 404 and / or provide data to the volume estimation system 404. For example, in some embodiments, the additional systems 408 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.

[0044] The volume estimation system 404 may represent various types of computing devices with which users (e.g., an administrator) may interact. For example, the volume estimation system 404 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 404 can be a non-mobile device (e.g., a desktop or server).

[0045] Lungs are responsible for gas exchange within a person’s body. Gas exchange involves moving oxygen from the air into the person’s blood and removing carbon dioxide from the blood into the air. This process happens in the lungs' air sacs, or alveoli, which are wrapped in tiny blood vessels called capillaries. For an efficient transfer of oxygen to the blood, each region of the lung should have access to air (e.g., ventilation) and blood (e.g., perfusion). For example, if a region of a patient’s lungs has access to air but not blood, little to no oxygen will be transferred to the blood from the air in that region. Similarly, if a region of the patient’s lungs has access to blood but not air, the blood will receive little to no oxygen from the air. Ventilation (e.g., a representation of air received in the lungs of a patient) may be measured by an EIT system as described above with relation to FIGS. 1-4 and imaged through the EIT system.

[0046] Perfusion (e.g., a representation of blood passing through the lungs of a patient) may also be measured by an EIT system. To measure a patient’s perfusion using an EIT system, a bolus (e.g., a dose, a volume) of a contrast may be injected into the patient. The contrast may include one or more of a saline solution, a gadolinium-based contrast agent, an iodine-based contrast agent, and a microbubble ultrasound contrast agent, or another contrast agent that has a conductivity different than blood. Therefore, the contrast may appear differently in the images generated by the EIT system 100. In other words, during injection of the contrast into the patient’s bloodstream, the blood’s conductivity will temporarily increase. The change in impedance during the injection reflects the distribution of blood flow within the lungs. The EIT system 100 may be used to acquire a voltage signal during at least a first change in impedance caused by the passage of the bolus. The voltage signal may be used to calculate or measure the perfusion in the lungs. Lung perfusion may also be estimated by using high-pass filters on the impedance signal, or by gating the impedance signal with the heartbeat.

[0047] FIG. 5 illustrates examples of images created by an EIT system. The images are separated into series 502a, 502b, 502c. Maps or images may be created from the impedance frames representing impedance changes caused by ventilation (e.g., a ventilation map 504) and impedance changes caused by the passage of the bolus (e.g., a perfusion map 506). The EIT images or maps of ventilation and perfusion may then be normalized, for example to obtain a maximum pixel value of 1.0 within each map. Pixels with values less than 5% of the maximum may be excluded. In some embodiments, pixels with values below a threshold within a range of from about 3 % to about 10 % of the maximum may be excluded. For perfusion maps, an impedance-versus-time curve of each pixel may be fitted with a curve-fitting routine such as a gamma function, mono-exponential, or polynomial model, and any suitable characteristic (e.g., maximum slope, area under the curve, or time- to-peak) may be assigned to the pixel.

[0048] The ventilation and perfusion maps may be illustrated as a global map (e.g., the entire map or cross-section of a patient), or separated into regions of interest (ROI), such as two sections (e.g., right lung and left lung), four quadrants (e.g., upper-right (UR), upperleft (UL), lower-right (LR), and lower-left (LL)), or as other regions of interest. In some embodiments, additional ROIs such as anterior / posterior halves or customized clinician- selected polygons may be defined to focus on pathology-specific regions.

[0049] The pixels of the ventilation map and the pixels of the perfusion map may be added together in a ROI to obtain mathematical values (e.g., pixel-wise values) for the ventilation and the perfusion within each ROI. The mathematical values for the ventilation and the perfusion are then divided by the sum of the respective ventilation value of the global map and the perfusion value of the global map. Thus, percentage values of regional ventilation and perfusion directed to each ROI are calculated.

[0050] The pixel-wise ventilation maps 504 and the pixel-wise perfusion maps 506 may be compared. For example, the mathematical value attributed to each pixel in a ventilation map 504 may be compared to the mathematical value attributed to each pixel in an associated perfusion map 506 to obtain a comparison value for each respective pixel. The comparison value may be the ventilation value (V) divided by the perfusion values (Q) or a V / Q matching value. The V / Q value is used to generate a V / Q map 508. In some embodiments, alternative similarity metrics such as the difference D = V - Q, the logarithm of the V / Q ratio, or a machine-leaming-derived feature vector may be employed to generate the V / Q map 508.

[0051] The V / Q map 508 may be separated into regions based on the V / Q values. The regions may be a mostly ventilated region 510 ( high V / Q), a mostly perfused region 512 (low V / Q), and a balanced region 514 (where V / Q is close to one). For example, the mostly ventilated region 510 may have values of V / Q > an upper threshold value (e.g., about 2.0), the balanced region 514 may have V / Q values within a balanced range (e.g., between about 0.5 - about 2.0), and the mostly perfused region 512 may have values of V / Q < a lower threshold value (e.g., about 0.5). In some embodiments, the upper threshold may be within a range of from about 1.5 to about 2.5, and the lower threshold may be within a range of from about 0.4 to about 0.6. Color or texture coding may be assigned using a user- selectable lookup table so that the display can comply with diverse clinical-workflow preferences.

[0052] In embodiments where the difference D = V - Q is employed, the V / Q map 508 may be separated into regions based on the signed difference values. The regions may be a mostly ventilated region 510 (positive D), a mostly perfused region 512 (negative D), and a balanced region 514 (where D is close to zero). For example, the mostly ventilated region 510 may have D values > an upper threshold value (e.g., about +0.30), the balanced region 514 may have D values within a balanced range (e.g., between about -0.20 to about +0.20), and the mostly perfused region 512 may have D values < a lower threshold value (e.g., about -0.30). In some embodiments, the positive upper threshold may be within a range of from about +0.25 to about +0.35, and the negative lower threshold may be within a range of from about -0.35 to about -0.25.

[0053] For example, with reference to series 502a, the V / Q map 508 displays a small mostly perfused region 512 and a slightly larger mostly ventilated region 510, while a majority of the V / Q map 508 is classified as a balanced region 514. With reference to series 502b, the V / Q map 508 shows a large mostly ventilated region 510 with an even larger balanced region 514. Substantially no mostly perfused region 512 is present. Finally, with reference to series 502c, a little less than half of the V / Q map 508 is classified as a mostly balanced region 514, with the rest of the V / Q map 508 classified as a mostly ventilated region 510. Substantially no mostly perfused regions 512 are present.

[0054] Within each ROI (including the global ROI or specific ROI of the V / Q map 508), a regional index of wasted ventilation may be calculated. The term “wasted ventilation” refers to the percentage of ventilation directed to pixels classified in the mostly ventilated region 510 within the ROI, in relation to the total ventilation received by that respective ROI.

[0055] A global index of wasted ventilation may be correlated with regions in the lungs exhibiting relatively large clot burdens. As used herein, the term “clot burden” refers to the ratio of the number of closed (e.g., occluded by thrombus) pulmonary vessels within a specified region of interest to the total number of closed vessels in the whole lung. To enhance sensitivity to smaller clot burdens, additional indices of wasted ventilation may be calculated within each of the smaller ROIs. In some embodiments, these indices may be supplied as candidate predictors to a statistical or machine-learning classifier (e.g., a multiple logistic -regression model) that combines global and regional indices to improve diagnostic accuracy.

[0056] FIG. 6 illustrates a pulmonary-emboli graph 600 that visually summarizes how a numerical Pulmonary Emboli Score (also referred to in some embodiments as a Pulmonary Embolism Index or Embolilndex) distributes across a population of ventilation-perfusion studies. Pulmonary emboli are obstructions of blood flow in the pulmonary arteries most often caused by blood clots that can impair gas exchange and cause life-threatening cardiopulmonary issues. The horizontal axis represents score values, while the vertical axis represents a count or frequency of studies that fall within each score bin.

[0057] The numerical score plotted in FIG. 6 may be generated by a processor that applies a tained statistical model (e.g., logistic regression or an alternative machine -learning classifier) to one or more indices of wasted ventilation obtained from the V / Q maps described above with reference to FIG. 5. In some embodiments, the model may use only a global index, while in other embodiments the model may incorporate regional indices from individual lungs or quadrants to enhance sensitivity to smaller or more distal perfusion defects. The output of the model may be expressed on a continuous scale (e.g., a log-odds score) or as a probability between 0 % and 100 %.

[0058] Since the score is continuous, a user interface may define one or more thresholds to guide clinical interpretation. For example, a Pulmonary Emboli Score greater than about 0 (e.g., within a range of from about 0.1 to about 8) may correspond to a high likelihood of pulmonary emboli, whereas a value equal to or less than about 0 (e.g., within a range of from about 0 to about -10) may correspond to a low likelihood.

[0059] Although FIG. 6 presents aggregated data, the same score may be displayed individually for a particular patient as a single numeric value, a probability bar, or a graphical indicator (e.g., a color-coded indicator). In some embodiments, the graph 600 may update in real-time as new ventilation-perfusion acquisitions are performed, thereby allowing clinicians to monitor changes in the Pulmonary Emboli Score after therapeutic interventions (e.g., anticoagulation, thrombolysis, or adjustments to ventilator settings).

[0060] Because the score derives from the pixel-level map operations detailed for FIG. 5, additional data acquisition beyond the described ventilation and perfusion frames is not necessary. However, when desired, the processor may augment the score with auxiliary quality metrics (e.g., saline-injection curve shape or motion artifact detection) and may suppress or flag the result if any metric falls outside predefined limits.

[0061] Referring back to FIG. 5, the score or visual indication is then shown in the EIT device or exported to another device which is connected, or which incorporates the EIT device. A graphical representation of mostly ventilated regions 510 and the mostly perfused regions 512 is shown in the EIT device or exported to another device which is connected to, or which incorporates, the EIT device. The mostly ventilated regions 510, the mostly perfused regions 512, and the balanced regions 514 are shown simultaneously in the same image with different color coding or visual texture. Output data may be stored in a structured-report format for integration with electronic medical-record systems.

[0062] FIG. 7 illustrates a process 700 for determining a likelihood of pulmonary emboli of a patient according to embodiments of the disclosure. The process 700 includes generating a ventilation map of lungs of a patient at act 702; generating a perfusion map of the lungs of the patient at act 704; comparing ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map at act 706; determining mostly ventilated regions, mostly perfused regions, and balanced regions from comparing the ventilation pixel values and the perfusion pixel values at act 708; and calculating a percentage of a total lung region that is mostly ventilated at act 710.

[0063] Generating a ventilation map of lungs of a patient with an EIT system in act 702 may include acquiring a continuous series of impedance frames, as described above with reference to FIG. 5, at a sampling frequency within a range of from about 20 Hz to about 100 Hz, such as from about 20 Hz to about 40 Hz, from about 40 Hz to about 60 Hz, from about 60 Hz to about 80 Hz, and from about 80 Hz to about 100 Hz. A filter may isolate the ventilation signal, after which each frame may be reconstructed into a two- dimensional impedance distribution. The ventilation component of the impedance signal may be isolated using a band-pass filter centered on the patient’s breathing frequency (e.g., only values between about 0.1 Hz to about 0.5 Hz may be included, which corresponds to the frequency of typical adult tidal breathing). The filter may remove higher-frequency components such as cardiac-related impedance changes and low-frequency drift. Each filtered frame may then be reconstructed into a two-dimensional impedance distribution. Pixel-wise ventilation values may then be normalized (e.g., scaled) so that the maximum pixel value equals 1.0, and pixels having values less than 5 % of the maximum may be excluded to reduce noise. Generating a perfusion map of the lungs of the patient with the EIT system in act 704 may include delivering a bolus of a contrast (e.g., hypertonic saline) through a central venous line of the patient. The bolus volume may be within a range of from about 5 mb to about 20 mL, and the concentration may be within a range of from about 5 % to about 10 % sodium chloride. The injection may be performed over a duration in a range from about 1 second to about 10 seconds, such as from about 1 second to about 5 seconds. Impedance frames may be acquired at a sampling frequency within a range of from about 20 Hz to about 100 Hz, such as from about 20 Hz to about 40 Hz, from about 40 Hz to about 60 Hz, from about 60 Hz to about 80 Hz, and from about 80 Hz to about 100 Hz, while the patient is under a respiratory pause period (e.g., not generating any impedance variation related to ventilation). Acquisition of the impedance-versus-time signal may begin immediately before or at the moment of bolus injection and continue until the bolus has fully passed through the pulmonary circulation to capture both arrival and washout phases. For each pixel, the recorded impedance-versus-time signal represents the change in measured electrical impedance in that pixel’s region as the conductive bolus moves through the cardiovascular and pulmonary systems. This signal may include a biphasic pattern with an initial pre-lung component characterized by a small increase in conductivity (i.e., decrease in impedance) caused by the bolus passing through the right heart and large pulmonary vessels, followed by a lung component characterized by a large increase in conductivity caused by the bolus entering the pulmonary microcirculation within the region corresponding to that pixel. The lung component may be isolated from the pre-lung component and fitted to a single gamma function by adjusting the gamma function’s parameters (e.g., amplitude, shape factor, and time offset) so that the curve closely matches the measured lung-phase impedance change. From the fitted gamma function, a perfusion metric such as the maximum slope of the rising phase (i.e., the steepest part of the curve during bolus arrival), the area under the curve, or the time to peak may be calculated to quantify relative blood flow forthat pixel. The resulting pixel-wise perfusion values may be normalized to a maximum of 1.0 with pixels having values less than 5 % of the maximum being excluded to reduce noise.

[0064] In some embodiments, comparing ventilation pixel values to respective perfusion pixel values in act 706 includes, for every pixel that retains valid entries in both maps, calculating a V / Q value. The V / Q is a ratio of the ventilation value over the perfusion value (e.g., the respective ventilation pixel value divided by the respective perfusion pixel value). The resulting V / Q pixel values may be reconstructed in an image (e.g., a 32 x 32 pixel spatial matrix, a 64 x 64 pixel spatial matrix, a 128 x 128 pixel spatial matrix, a 256 x 256 pixel spatial matrix, etc.). Pixels may be graphically distinguished based on their V / Q values (e.g., higher V / Q values may be a first color, while lower V / Q values may be a second different color). In other embodiments, the ventilation pixel values and the respective perfusion pixel values may be compared by computing an absolute difference or logarithmic transform, with the resulting values reconstructed and visually displayed using the same mapping and thresholding techniques described herein. Each pixel’s value may be mapped to a display coordinate system to preserve the anatomical relationship of regions in the cross-sectional image. Pixels may be visually represented by assigning colors, patterns, or grayscale intensities to defined V / Q ranges, as discussed below with relation to act 708.

[0065] Determining mostly ventilated regions, mostly perfused regions, and balanced regions in act 708 may include applying an upper threshold (e.g., boundary) within a range of from about 1.5 to about 2.5 and a lower threshold within a range of from about 0.4 to about 0.6 to the V / Q map. Pixels having V / Q values greater than the upper threshold may be labelled as mostly ventilated, pixels having V / Q values less than the lower threshold may be labelled as mostly perfused, and pixels having V / Q values greater than the lower threshold and less than the upper threshold may be labelled as balanced. The boundaries where these classifications change may directly correspond to changes in the display color or pattern, providing immediate visual cues to a viewer. For example, pixels below the lower threshold may be displayed in shades of blue, pixels between the thresholds in shades of green, and pixels above the upper threshold in shades of red, with darker or lighter tones used to indicate relative distance from the thresholds. The threshold values and associated color or pattern mappings may be user-adjustable or selected from predefined presets to accommodate different clinical scenarios or user preferences. The display scale may use either discrete color bands corresponding to classification boundaries or a continuous gradient in which hue or intensity changes smoothly with the V / Q value. Other display options may include numerical tables listing the percentage of the image classified into each category; bar or pie charts showing regional distributions; integrated reports combining the image with quantitative metrics; three-dimensional reconstructions; time-series animations showing changes in V / Q over time; or numerical overlays directly on anatomical images. The images and associated metrics may be displayed on the EIT system interface, exported to external monitors, included in printed reports, or transmitted electronically to other healthcare systems for remote review.

[0066] Calculating a percentage of a total lung region that is mostly ventilated in act 710 may include summing the ventilation-weighted contribution of all pixels labelled as mostly ventilated, dividing the sum by the ventilation total obtained from the entire lung crosssection, and producing a percentage. When the percentage exceeds a threshold it may indicate a high likelihood of pulmonary emboli. The threshold may be within a range of from about 10 % to about 30 %. The percentage and a corresponding likelihood score may be displayed on a user interface or exported in a structured-report format for integration with electronic medical records. In some embodiments, when the percentage is above the threshold an alert may be provided through the user interface, such as a color change of a portion of the user interface, a color change of the data on the user interface, an audible alert, etc.

[0067] If the V / Q map indicates a substantial mismatch (e.g., a large difference between ventilation and perfusion values), the system may generate one or more recommended corrective actions and / or transmit control signals to external devices to implement corrective adjustments. The recommended or automated actions may include adjusting ventilator settings, initiating therapeutic patient positioning such as prone positioning, and / or controlling delivery of selective pulmonary vasodilators (e.g., inhaled nitric oxide). In cases where the V / Q data suggest a pattern consistent with pulmonary embolism, the system may recommend or initiate signaling for systemic anticoagulation therapy or thrombolytic therapy. Additional recommendations or control signals may relate to supportive measures such as fluid administration or vasopressor delivery. The system may also be configured to schedule or trigger follow-up EIT assessments after such corrective actions are performed to evaluate changes in regional ventilation-perfusion balance. The methods and systems according to embodiments of the disclosure provide several advantages in measuring lung perfusion and determining the likelihood of pulmonary emboli compared to conventional diagnostic approaches. Compared to conventional diagnostic imaging methods, such as Angio CT, embodiments disclosed herein provide a non-invasive, bedside solution that may substantially eliminate patient transportation risks and exposure to ionizing radiation and iodinated contrast agents. This reduction or elimination of transport and imaging-related risks is particularly advantageous in critically ill or hemodynamically unstable patients who may have contraindications or limited access to traditional imaging methods. In contrast to operator-dependent techniques, such as ultrasound or echocardiography (ECHO), the use of EIT according to embodiments of the disclosure offers improved consistency and reduced variability in imaging results due to its operator-independent nature. Additionally, unlike conventional indirect diagnostic methods, such as D-dimer testing, which often yield ambiguous or nonspecific results, embodiments disclosed herein provide direct and specific assessments of lung perfusion, thus increasing diagnostic accuracy and reducing the rate of false-positive diagnoses. Furthermore, embodiments of the disclosure facilitate rapid diagnosis and clinical decisionmaking due to quicker acquisition and interpretation of perfusion data compared to conventional diagnostic imaging, which may be particularly beneficial in high-risk pulmonary embolism scenarios, where timely therapeutic intervention can significantly affect clinical outcomes.

[0068] EXAMPLE

[0069] Animals and Pre-operative Preparation

[0070] Ten healthy female Landrace piglets (34.3 ± 2.4 kg) were intubated and anesthetized with continuous infusions of ketamine (5 mg kg1h '). fentanyl (1.5 pg kg1h '). and pancuronium (0.1 mg kg1h '). A ventilator provided volume-controlled ventilation with a tidal volume 8 mb kg a respiratory rate of 25 bpm, an inspiratory-expiratory ratio of 1 :2, a positive end-expiratory pressure (PEEP) of 5 cmFLO, and a fraction of inspired oxygen (FIO2) of 0.21. End-tidal carbon dioxide (ETCO2) and additional volumetric capnography parameters were continuously monitored. All procedures were approved by institutional animal-use and ethics committees.

[0071] The femoral artery was cannulated for pressure monitoring and arterial blood gases. A pulmonary artery catheter was placed via a right jugular vein to measure pulmonary artery pressure and thermodilution cardiac output. A left jugular vein was cannulated for medications and EIT perfusion bolus injection. A multiparameter monitor tracked SpCE, ECG, heart rate, and invasive blood pressure. An EIT system used a 32-electrode belt and proximal flow and pressure sensors, sampling at 50 Hz.

[0072] Experimental Protocol

[0073] Initial baseline ventilation and a sustained-inflation recruitment maneuver were performed, followed by arterial blood gas analysis to exclude animals with PaCE + PaCCE < 400 mmHg. Piglets were then transferred to the CT suite and stabilized supine for 15 minutes. Perfusion assessments were obtained in the following order: EIT, Dynamic contrast-enhanced CT (DCE-CT), repeat EIT, and computed tomography pulmonary angiography (CTPA), under baseline conditions and after inducing proximal and distal balloon occlusions of the pulmonary artery. Each occlusion step was maintained for 15 minutes unless hemodynamic instability occurred. Repeated EIT acquisitions ensured condition stability throughout CT acquisitions. Occlusion success was confirmed by observing a transition from pulsatile pulmonary artery waveform to a non-pulsatile wedge trace lasting at least 30 seconds.

[0074] Following occlusion studies, selective main-stem intubation induced unilateral hypoventilation while the contralateral lung remained ventilated. Prior to obstruction, animals breathed FIO2 0.21 for 15 minutes to enrich trapped nitrogen and reduce atelectasis risk. Perfusion assessments and arterial / mixed-venous blood gases were collected after 15 minutes of obstruction.

[0075] Additional control data from five piglets undergoing only bronchial obstruction steps (bilateral ventilation, obstruction at FIO2 0.21 and 1.0) were included. These piglets had pulmonary artery catheters without balloon inflation, serving as negative controls for pulmonary artery occlusion. A decremental PEEP titration (24 to 4 cmFLO, 2 cmFLO intervals, 30 s each) identified the lowest PEEP with < 1% collapse as indicated by EIT. Ventilation continued thereafter in volume -controlled mode (tidal volume 8 mb kg the identified lowest PEEP, respiratory rate 25 bpm, FIO2 adjusted for SpCh > 93%). For shunt measurements during bronchial obstruction, the ventilated lung received FIO2 1.0 for 15 minutes before blood sampling.

[0076] Imaging Acquisition

[0077] Electrical impedance tomography (EIT) ventilation maps were obtained from tidal impedance changes (AZ) averaged over 10 breaths. Perfusion maps utilized a 10 mb bolus of 10% NaCl injected during 20 seconds of apnea at pre-apnea PEEP. Time-impedance curves were analyzed using a biphasic model, distinguishing pre-lung from lung signals, and lung signals were fitted to a gamma function to generate perfusion maps.

[0078] DCE-CT imaging was conducted with a scanner. A 19.2 mm slab above the diaphragm was dynamically imaged for 20 seconds during a 30-second apnea, beginning 3-5 seconds before intravenous contrast injection. Pulmonary blood flow was determined using steepest-slope analysis.

[0079] CTPA was performed using the scanner, with helical imaging triggered by contrast injection to the patient’s vasculature. Perfusion distribution was calculated based on visible segmental arteries within target and total lung regions.

[0080] Signal Processing and Analysis

[0081] EIT ventilation and perfusion maps were normalized, excluding pixels below 5%. Regions of interest (ROIs) included global, right, left, and quadrant regions. Ventilationperfusion (V / Q) matching categories were defined pixel-wise as mostly ventilated, balanced, or mostly perfused. Wasted ventilation was quantified as ventilation directed to mostly ventilated (i.e., V / Q > 2) pixels. Logistic regression models utilized wasted-ventilation indices to predict clot burden. The proportion of tidal ventilation entering the mostly ventilated pixels provided a global wasted-ventilation index, a slice-level surrogate of dead-space ventilation.

[0082] Results - Animal Data

[0083] Baseline perfusion was evenly split between lungs (50.4 ± 4.2 %). Proximal occlusion reduced perfusion to the target ROI to 9.2 ± 5.7 % (P < 0.0001), while distal occlusion lowered perfusion to 35 ± 9.0 % (P < 0.0001). EIT-derived perfusion showed strong agreement with reference methods: versus DCE-CT, R = 0.95 (95 % CI 0.91-0.97) with a -3.0 ± 9.2 % bias and limits of agreement -21.1 % to 15.0 % ; versus CTPA, R = 0.96 (95 % CI 0.92-0.98) with a 3.5 ± 8.4 % bias.

[0084] The global wasted-ventilation index correlated with ventilatory ratio (R = 0.47, P = 0.001) and alveolar dead space (R = 0.63, P = 0.002). During unilateral bronchial obstruction at an FIO2 of 1 .0, perfusion to the non-ventilated lung correlated inversely with shunt fraction (R = -0.68, P = 0.013).

[0085] Logistic Regression Models for Pulmonary Artery Occlusion

[0086] Two logistic-regression classifiers were developed. Model 1 (e.g., a univariate classifier) used the global wasted-ventilation index alone to predict embolic clot burden. Model 2 (e.g., a multivariable classifier, a multiple logistic regression model) augmented this global measure with four ROI-specific indices (right lung, left lung, lower-right quadrant, lower-left quadrant) to capture spatially heterogeneous perfusion loss.

[0087] Model 1 successfully identified cases with embolic clot burden, and Model 2 demonstrated improved performance by incorporating additional regional indices. When analysis was restricted to proximal emboli, Model 2 maintained a high level of predictive capability.

[0088] Clinical Validation - Patient Cohort

[0089] Among 232 EIT-perfusion studies from 65 patients with acute hypoxemic respiratory failure, both models demonstrated successful identification of cases consistent with acute pulmonary embolism. Model 2 consistently outperformed Model 1 in this cohort, and in some cases, follow-up assessments after therapeutic intervention showed changes in model classification that were concordant with subsequent imaging findings.

[0090] Non-limiting, example embodiments may include the following, alone or in combination:

[0091] Embodiment 1 : A method of measuring perfusion and ventilation in a lung, the method including injecting a contrast into a patient, measuring an impedance through a region of the lung of the patient after injecting the contrast into the patient, acquiring a voltage signal during at least a first change in impedance caused by a passage of the contrast, calculating the perfusion in the lung based on the voltage signal, measuring an impedance through the region of the lung of the patient during a breathing cycle, acquiring a second voltage signal during at least a first change in impedance caused by inspiration, and calculating the ventilation in the lung based on the second voltage signal.

[0092] Embodiment 2: The method of Embodiment 1, further including creating an image of the region of the lung where each pixel of the image represents an impedance measurement of a representative region of the lung.

[0093] Embodiment 3 : The method of Embodiment 2, wherein calculating the perfusion in the lung based on the voltage signal includes acquiring a pixel voltage signal for each pixel of the image.

[0094] Embodiment 4: The method of Embodiment 3, further including creating a perfusion image representative of a calculated perfusion at each pixel. Embodiment 5 : The method of Embodiment 2, wherein calculating the ventilation in the lung based on the second voltage signal includes acquiring a pixel voltage signal for each pixel of the image.

[0095] Embodiment 6: The method of Embodiment 1, wherein injecting a contrast into a patient includes injecting one or more of a saline solution, a gadolinium-based contrast agent, an iodine -based contrast agent, and a microbubble ultrasound contrast agent.

[0096] Embodiment 7 : A method of determining a likelihood of pulmonary emboli of a patient, the method including generating a ventilation map of lungs of the patient with an EIT system, generating a perfusion map of the lungs of the patient with the EIT system, comparing ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map, identifying mostly ventilated pixels, mostly perfused pixels, and balanced pixels from comparing the ventilation pixel values and the perfusion pixel values, determining for at least one region of interest a ratio of ventilation directed to pixels identified as mostly ventilated within the region of interest to the total ventilation within the region of interest to generate a regional index of wasted ventilation, and identifying a high likelihood of pulmonary emboli if the regional index of wasted ventilation is above a threshold value.

[0097] Embodiment 8: The method of Embodiment 7, wherein comparing the ventilation pixel values to the perfusion pixel values includes dividing the ventilation pixel values by the perfusion pixel values.

[0098] Embodiment 9: The method of Embodiment 8, wherein determining mostly ventilated pixels, mostly perfused pixels, and balanced pixels from comparing the ventilation pixel values and the perfusion pixel values includes calculating a ratio of a ventilation pixel value to a corresponding perfusion pixel value, determining that a pixel is mostly ventilated when the ratio is above about 1.5, determining that a pixel is mostly perfused when the ratio is less than about 0.4, and determining that a pixel is balanced when the ratio is greater than about 0.4 and less than about 1.5.

[0099] Embodiment 10: The method of Embodiment 9, further including applying the regional index of wasted ventilation to a trained statistical model to generate a pulmonary emboli score.

[0100] Embodiment 11 : The method of Embodiment 7, wherein generating a ventilation map of lungs of the patient includes measuring an impedance through a region of the lung of the patient with an EIT system during a breathing cycle, acquiring a voltage signal during at least a first change in impedance caused by inspiration, calculating the ventilation in the lung based on the voltage signal, and creating an image of the region of the lung where each pixel of the image represents an impedance measurement of a representative region of the lung.

[0101] Embodiment 12: The method of Embodiment 7, wherein comparing ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map includes generating a V / Q map of pixels representative of the comparison between the ventilation pixel values and the respective perfusion pixel values.

[0102] Embodiment 13: An EIT system including an EIT device configured to capture impedance images of a patient, a display configured to display information to a user, a memory device, and a non-transitory computer readable medium storing instructions thereon that, when executed by at least one processor, cause the at least one processor to generate a ventilation map of lungs of the patient with the EIT device, generate a perfusion map of the lungs of the patient with the EIT device, compare ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map, generate a V / Q map where V / Q map pixels represent comparison values from comparing the ventilation pixel values to the perfusion pixel values separated into at least three groups including mostly ventilated pixels, mostly perfused pixels, and balanced pixels, generate at least one of an index or a number that indicates a likelihood of pulmonary emboli, display the V / Q map on the display wherein each of the mostly ventilated pixels, the mostly perfused pixels, and the balanced pixels are graphically distinguished, and display the likelihood of pulmonary emboli.

[0103] Embodiment 14: The EIT system of Embodiment 13, wherein producing the V / Q map on the display wherein each of the mostly ventilated pixels, the mostly perfused pixels, and the balanced pixels are graphically distinguished includes assigning the mostly ventilated pixels a first color, assigning the mostly perfused pixels a second color different from the first color, and assigning the balanced pixels a third color different from the first color and the second color.

[0104] Embodiment 15: The EIT system of Embodiment 13, wherein the instructions further cause the at least one processor to store the ventilation map, the perfusion map, and the V / Q map in a structured report format in the memory device for integration with an electronic medical record system. Embodiment 16: The EIT system of Embodiment 13, wherein comparing ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map includes dividing the ventilation pixel values by the perfusion pixel values to generate a ratio.

[0105] Embodiment 17: The EIT system of Embodiment 16, wherein generating a V / Q map where V / Q map pixels represent comparison values from comparing the ventilation pixel values to the perfusion pixel values separated into at least three groups including mostly ventilated pixels, mostly perfused pixels, and balanced pixels includes determining that a pixel is mostly ventilated when the ratio between the ventilation pixel value and the perfusion pixel value is greater than about 1.5, determining that a pixel is mostly perfused when the ratio between the ventilation pixel value and the perfusion pixel value is less than about 0.4, and determining that a pixel is balanced when the ratio between the ventilation pixel value and the perfusion pixel value is greater than or equal to about 0.4 and less than or equal to about 1.5.

[0106] Embodiment 18: The system of Embodiment 13, wherein displaying the V / Q map on the display includes displaying the V / Q map for one or more regions of interest of the lungs of the patient.

[0107] Embodiment 19: The system of Embodiment 13, wherein displaying the V / Q map on the display includes distinguishing regions with a higher likelihood of pulmonary emboli.

[0108] Embodiment 20: The system of Embodiment 13, wherein displaying the likelihood of pulmonary emboli includes displaying at least one of a number, a percentage, or a graph. While the disclosure has been described herein with respect to certain illustrated embodiments, those of ordinary skill in the art will recognize and appreciate that it is not so limited. Rather, many additions, deletions, and modifications to the illustrated embodiments may be made without departing from the scope of the invention as claimed, including legal equivalents thereof. In addition, features from one embodiment may be combined with features of another embodiment while still being encompassed within the scope of the disclosure as contemplated by the inventors. Further, embodiments of the disclosure have utility with different and various tool types and configurations.

Claims

CLAIMSWhat is claimed is:

1. A method of measuring perfusion and ventilation in a lung, the method comprising: injecting a contrast into a patient; measuring an impedance through a region of the lung of the patient after injecting the contrast into the patient; acquiring a voltage signal during at least a first change in impedance caused by a passage of the contrast; calculating the perfusion in the lung based on the voltage signal; measuring an impedance through the region of the lung of the patient during a breathing cycle; acquiring a second voltage signal during at least a first change in impedance caused by inspiration; and calculating the ventilation in the lung based on the second voltage signal.

2. The method of claim 1, further comprising creating an image of the region of the lung where each pixel of the image represents an impedance measurement of a representative region of the lung.

3. The method of claim 2, wherein calculating the perfusion in the lung based on the voltage signal comprises acquiring a pixel voltage signal for each pixel of the image.

4. The method of claim 3, further comprising creating a perfusion image representative of a calculated perfusion at each pixel.

5. The method of claim 2, wherein calculating the ventilation in the lung based on the second voltage signal comprises acquiring a pixel voltage signal for each pixel of the image.

6. The method of claim 1, wherein injecting a contrast into a patient comprises injecting one or more of a saline solution, a gadolinium-based contrast agent, an iodine- based contrast agent, and a microbubble ultrasound contrast agent.

7. A method of determining a likelihood of pulmonary emboli of a patient, the method comprising: generating a ventilation map of lungs of the patient with an EIT system; generating a perfusion map of the lungs of the patient with the EIT system; comparing ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map; identifying mostly ventilated pixels, mostly perfused pixels, and balanced pixels from comparing the ventilation pixel values and the perfusion pixel values; determining, for at least one region of interest, a ratio of ventilation directed to pixels identified as mostly ventilated within the region of interest to the total ventilation within the region of interest to generate a regional index of wasted ventilation; and identifying a high likelihood of pulmonary emboli if the regional index of wasted ventilation is above a threshold value.

8. The method of claim 7, wherein comparing the ventilation pixel values to the perfusion pixel values comprises dividing the ventilation pixel values by the perfusion pixel values.

9. The method of claim 8, wherein determining mostly ventilated pixels, mostly perfused pixels, and balanced pixels from comparing the ventilation pixel values and the perfusion pixel values comprises: calculating a ratio of a ventilation pixel value to a corresponding perfusion pixel value; determining that a pixel is mostly ventilated when the ratio is above about 1.5; determining that a pixel is mostly perfused when the ratio is less than about 0.4; and determining that a pixel is balanced when the ratio is greater than about 0.4 and less than about 1.5.

10. The method of claim 9, further comprising applying the regional index of wasted ventilation to a trained statistical model to generate a pulmonary emboli score.

11. The method of claim 7, wherein generating a ventilation map of lungs of the patient comprises: measuring an impedance through a region of the lung of the patient with an EIT system during a breathing cycle; acquiring a voltage signal during at least a first change in impedance caused by inspiration; calculating the ventilation in the lung based on the voltage signal; and creating an image of the region of the lung where each pixel of the image represents an impedance measurement of a representative region of the lung.

12. The method of claim 7, wherein comparing ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map comprises generating a V / Q map of pixels representative of the comparison between the ventilation pixel values and the respective perfusion pixel values.

13. An EIT system comprising: an EIT device configured to capture impedance images of a patient; a display configured to display information to a user; a memory device; and a non-transitory computer readable medium storing instructions thereon that, when executed by at least one processor, cause the at least one processor to: generate a ventilation map of lungs of the patient with the EIT device; generate a perfusion map of the lungs of the patient with the EIT device; compare ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map; generate a V / Q map where V / Q map pixels represent comparison values from comparing the ventilation pixel values to the perfusion pixel values separated into at least three groups including mostly ventilated pixels, mostly perfused pixels, and balanced pixels; generate at least one of an index or a number that indicates a likelihood of pulmonary emboli;display the V / Q map on the display wherein each of the mostly ventilated pixels, the mostly perfused pixels, and the balanced pixels are graphically distinguished; and display the likelihood of pulmonary emboli.

14. The EIT system of claim 13, wherein producing the V / Q map on the display wherein each of the mostly ventilated pixels, the mostly perfused pixels, and the balanced pixels are graphically distinguished comprises assigning the mostly ventilated pixels a first color, assigning the mostly perfused pixels a second color different from the first color, and assigning the balanced pixels a third color different from the first color and the second color.

15. The EIT system of claim 13, wherein the instructions further cause the at least one processor to store the ventilation map, the perfusion map, and the V / Q map in a structured-report format in the memory device for integration with an electronic medical-record system.

16. The EIT system of claim 13, wherein comparing ventilation pixel values of the ventilation map to respective perfusion pixel values of the perfusion map comprises dividing the ventilation pixel values by the perfusion pixel values to generate a ratio.

17. The EIT system of claim 16, wherein generating a V / Q map where V / Q map pixels represent comparison values from comparing the ventilation pixel values to the perfusion pixel values separated into at least three groups including mostly ventilated pixels, mostly perfused pixels, and balanced pixels comprises: determining that a pixel is mostly ventilated when the ratio between the ventilation pixel value and the perfusion pixel value is greater than about 1.5; determining that a pixel is mostly perfused when the ratio between the ventilation pixel value and the perfusion pixel value is less than about 0.4; and determining that a pixel is balanced when the ratio between the ventilation pixel value and the perfusion pixel value is greater than or equal to about 0.4 and less than or equal to about 1.5.

18. The system of claim 13, wherein displaying the V / Q map on the display comprises displaying the V / Q map for one or more regions of interest of the lungs of the patient.

19. The system of claim 13, wherein displaying the V / Q map on the display comprises distinguishing regions with a higher likelihood of pulmonary emboli.

20. The system of claim 13, wherein displaying the likelihood of pulmonary emboli comprises displaying at least one of a number, a percentage or a graph.

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