Apparatus and method for determining a measure of lung uniformity

Abstract

The present invention relates to an apparatus and method, as well as a computer program, for determining a measure of lung homogeneity based on electrical impedance tomography (EIT) data. The apparatus (100) comprises a data input unit (112) for receiving EIT data obtained by an electrical impedance tomography device (110), the data input unit (112) being configured to receive and provide EIT data from at least one region of at least one lung (2) of a living subject over an observation period (ΔT). The apparatus (100) also comprises a calculation and control unit (114) connected to the data input unit (112). The calculation and control unit (114) is configured to determine impedance values ​​over the observation period (ΔT) for each pixel (1) of at least one region of the at least one lung (2) and to determine an impedance amplitude value (ΔZ) and an end-expiratory impedance value (EELI) for each pixel (1) of the at least one lung (2). The calculation and control unit (114) is further configured to associate an impedance amplitude value (ΔZ) and an end-expiratory impedance value (EELI) individually for each pixel (1), determine data based on the impedance amplitude value (ΔZ) and the associated end-expiratory impedance value (EELI), and generate a control signal based on the data.

Description

[Technical Field]

[0001] The present invention relates to an apparatus and method for determining a measure of lung uniformity based on electrical impedance tomography (EIT) data, as well as a computer program product. [Background technology]

[0002] Electrical impedance tomography (EIT) is a non-invasive imaging technique based on the application of an electric current and the measurement of the voltage passing through electrodes attached to the patient's body. EIT provides images of the distribution of conductivity, dielectric constant, impedance, or resistivity, or changes therein. Hereinafter, the measured values ​​will be referred to as “impedance values,” and the above distributions will be collectively referred to as “electrical properties.” This image is called an EIT image. The image or sequence of images shows differences in the electrical properties of various body tissues, bones, skin, fluids, and organs, particularly the lungs, which are useful for monitoring the patient's condition.

[0003] A typical EIT configuration is shown in International Publication No. 2015 / 048917. A pair of electrodes are positioned on a belt so as to be in electrical contact with the skin and at a constant distance from each other around the patient's chest. A current or voltage input signal is applied alternately between different or all possible electrode pairs. While the input signal is applied to one of the pair of electrodes, the current or voltage between the remaining electrodes can be measured. The measured voltages of the body parts can be reconstructed into electrical characteristics or changes in electrical characteristics by a reconstruction algorithm used by a data processor to obtain a representation of the distributed impedance value across the cross-section of the patient where the electrode belt ring is positioned. The electrical characteristics are displayed on a screen.

[0004] In the EIT process, numerous impedance measurements are recorded using, for example, 16 or 32 electrodes. From these impedance measurements, the EIT image reconstruction algorithm can generate a two-dimensional image containing pixels representing the characteristics of the lung. The image may contain 32x32 pixels representing the location inside and outside the lung.

[0005] The electrical properties information obtained by EIT can be projected onto cross-sectional images derived from anatomical models in an anatomical context, for example, to show contours representing the outer boundaries of modeled organs within the electrode surface.

[0006] Using the contours of functional structures, pixels within such structures can be (automatically) clustered to form regions of interest (ROIs) that coincide with functionally meaningful anatomical structures such as lungs. The signals of pixels that fall within such ROIs can be identified and analyzed separately for each ROI by automated signal processing means and algorithms.

[0007] Up to 100 or more tomographic EIT images can be reconstructed per second. These high-temporal-resolution images reflect local electrical properties within lung tissue that are influenced by the respiratory and cardiac cycles. In contrast to CT scans, EIT images display functional information, such as local tidal volume, local lung recruitment, expiratory time constant, or distribution of lung perfusion, rather than morphological information. EIT also measures end-expiratory lung impedance (EELI), a parameter that correlates with functional residual volume (FRC) at the overall level and / or end-expiratory lung volume (EELV) at the regional or pixel level.

[0008] The function of EIT is to provide information about ventilation at the level of individual pixels within the EIT image. However, currently there is no reliable conversion from impedance values ​​to volumetric measurements, and therefore, direct calculation of volume and volumetric change from EIT data is not possible.

[0009] German Patent No. 102017007224 discloses determining the characteristics and characteristic changes of at least two localized regions of the lung based on EIT data representing regional changes in lung compliance or elasticity. This method does not provide a standard at the level of the entire lung.

[0010] European Patent No. 3725222 relates to a system for the real-time determination of local stress in the lung during artificial respiration. A specific electrical impedance value at a specific point in time is assigned to a specific EIT pixel. The value z of the local tidal volume is determined as the difference between the end-inspiratory electrical impedance (ZINSP) and the end-expiratory electrical impedance (ZEXSP) of a specific EIT pixel. The pre-strain value is determined by dividing the local tidal volume by the end-inspiratory electrical impedance. The pre-strain value may be normalized with respect to a reference strain value, for example, at a predetermined PEEP value. Changes in the state of the lung may be monitored for a specific patient, but a reference for the state of the lung itself may not be derived. Summary of the Invention Problems to be Solved by the Invention

[0011] The object of the present invention is to overcome the drawbacks of the prior art, and in particular, to provide an apparatus and method that enable the provision of meaningful data regarding the lung in a simple and reliable manner. Means for Solving the Problems

[0012] According to the present invention, these and other objects are solved by an apparatus and method for determining a measure of lung homogeneity based on electrical impedance tomography (EIT) data as described in the independent claims.

[0013] The ratio of the change in the ventilated lung volume dV to the static lung volume V0 or the end-expiratory lung volume EELV is called lung strain (Gattinoni et al., 2012, Stress and strain within the lung, Curr Opin Crit Care 18(1):42-7).

[0014] The apparatus of the present invention is based on the finding that pixel-level strain determination, monitoring, and analysis by EIT can provide meaningful information even if a specific value of volume cannot be determined by EIT.

[0015] Lung strain resulting from increased respiratory drive during mechanical ventilation places a high energy load on lung tissue, which can increase the patient's risk of self-injury of the lungs (Brochard et al., 2017, Mechanical ventilation to minimize lung injury in acute respiratory failure, Am J Respir Crit Care Med 195(4):438-42).

[0016] The overall strain can be derived from the ratio of the tidal volume VT to the functional residual volume FRC.

[0017] During mechanical ventilation, low total lung strain reduces the risk of ventilator-induced lung injury (Protti et al., 2012, Lung stress and strain during mechanical ventilation: Any safe threshold?, Am J Respir Crit Care Med 185:115). However, it remains largely unclear what constitutes a healthy boundary for such a ratio dV / V0, and when strain becomes atypically large (Gattinoni et al., 2012). In mechanically ventilated pigs, Protti et al. did not find lung injury at strain values ​​below 1.5 or acute respiratory failure at strain values ​​above 2.5, although a gray zone remains between these values ​​(Protti et al., 2012, Gattinoni et al., 2012). However, Gattinoni points out that such high lung strain does not typically occur in mechanically ventilated patients, and that lung heterogeneity is a major possible cause of lung injury in mechanically ventilated patients (Gattinoni et al., 2012). To date, the common definition of strain as dV / V0 at the whole-lung level means that such heterogeneity often goes unnoticed because it requires localized lung strain measurements.

[0018] Therefore, when focusing on strain as a cause of lung injury, especially in patients who are breathing spontaneously, it is necessary to focus on determining local strain and identifying heterogeneity of strain. A uniform distribution of local lung strain means that the ratio dV / V0, which is the change in lung volume dV to the resting lung volume V0, is nearly constant throughout the lung. Thus, dV should increase linearly with V0, meaning that areas with larger lung volume exhibit greater ventilation.

[0019] U.S. Patent No. 20140316266 discloses the use of ultrasound imaging to determine local lung strain, lung volume, and changes in lung volume.

[0020] The apparatus according to the present invention includes a data input unit. The data input unit is designed to receive EIT data obtained from an electrical impedance tomography (EMT) scanner equipped with electrodes placed on the patient.

[0021] EIT data may be provided to the data input unit directly from the electrical impedance tomography system, or indirectly via data lines, signal lines, or network connections.

[0022] The apparatus may be associated with or comprise an electrical impedance tomography system.

[0023] The data input unit is configured to receive and provide EIT data from at least one region of at least one lung of a living organism over an observation period. The observation period typically lasts over a number of respiratory cycles. Preferably, the EIT data is provided for cross-sections of at least one entire lung.

[0024] A calculation and control unit is connected to the data input unit. The calculation and control unit is configured to determine the impedance value over the observation period for each pixel in at least one region.

[0025] The computing unit can assign EIT data measured in at least one observation plane to regions of interest, such as the right lung and / or left lung.

[0026] The impedance values ​​are typically stored as a dataset in data memory or data storage area allocated to the computing and control unit and kept available for further data processing in the data memory or data storage area.

[0027] The calculation and control unit is further configured to determine the impedance amplitude value of each pixel in at least one lung.

[0028] Amplitude represents the difference between the maximum and minimum impedance values ​​within a respiratory cycle. Amplitude may be defined based on the absolute maximum and minimum values ​​within the cycle, or based on the average, or on consecutive maximum and minimum values. The impedance amplitude value may be determined by the average impedance difference over the observation period, or by the maximum difference occurring over the observation period.

[0029] The calculation and control unit is further configured to determine the end-expiratory impedance value for each pixel in at least one lung. The end-expiratory impedance value may be calculated as the average minimum of the impedance values ​​over the observation period for each pixel.

[0030] The calculation and control unit is further configured to determine data for each pixel individually based on the impedance amplitude value and the associated end-tidal impedance value.

[0031] The data can be used to map impedance amplitude values ​​and / or end-expiratory impedance values ​​to each pixel.

[0032] The data can provide a representation of the local distribution of the measured values. The data can be used to provide a graphical representation showing the relationship between the change in impedance of each pixel and the end-tidal lung impedance. In unaffected lungs, it is assumed that the impedance amplitude value and the end-tidal impedance value are correlated to some extent.

[0033] Based on the determined data, the calculation and control unit can provide at least one number or at least a range of numbers that are characteristics of the assignment relationship between the impedance amplitude value of each pixel and the associated end-respiratory impedance value.

[0034] The calculation and control unit is further configured to generate control signals based on the data. These control signals can be used to transfer, store, and / or display data or information derived from the data.

[0035] The data can be analyzed by those skilled in the art, who can draw conclusions from the data patterns. The data can also be used for the automated control of medical devices.

[0036] In particular, the calculation and control units are further configured to determine whether the data meets predetermined criteria. The data can be compared to predetermined criteria, such as a predetermined data pattern, a predetermined number of thresholds, or a predetermined range. If the criteria are not met, a heterogeneity indicator may be generated.

[0037] The criteria may be predetermined based on a control group of patients. The control group may consist of individuals with unaffected lungs.

[0038] Impedance amplitude values ​​and associated end-tidal impedance values ​​can be measured or determined for a control group.

[0039] The criteria can be derived from the impedance amplitude values ​​across lung pixels, the associated end-tidal impedance values, and / or the local distribution of data estimated from the impedance amplitude values ​​and associated end-tidal impedance values.

[0040] The criteria can be derived from the distribution of impedance amplitude values ​​across relevant end-expiratory impedance values.

[0041] The comparison can provide at least one difference between the data obtained for the patient and the baseline, and / or a ratio between the data obtained for the patient and the baseline.

[0042] In EIT, determining the absolute value of impedance, and from that, determining lung volume, is difficult, especially without calibration. Therefore, while changes in distribution and volume can be quantified, inter-patient comparability remains difficult. Changes in volume and resting lung volume are related to impedance amplitude and end-expiratory lung impedance, respectively, but these measurements cannot be converted to volume measurements. In particular, end-expiratory impedance values ​​can vary considerably even within the same patient's EIT records, and distortion values ​​can differ significantly when calculated as a simple ratio of impedance amplitude to end-expiratory lung impedance.

[0043] Therefore, the absolute value of the ratio between the impedance amplitude value and the end-tidal lung impedance may vary between measurements of patients with similar lung strain, or even between consecutive measurements of the same patient.

[0044] Therefore, instead of focusing on absolute distortion values, data derived from relevant impedance amplitude values ​​and end-expiratory impedance values, particularly from the local distribution of impedance amplitude values ​​and end-expiratory impedance values ​​within the lungs, and / or from the statistical distribution of values ​​derived from impedance amplitude values ​​and end-expiratory impedance values, can be used as a measure of lung heterogeneity.

[0045] This makes the method less susceptible to distortion caused by a single outlier. In healthy lungs, distortion is assumed to be low and nearly constant throughout the lung, and large changes in lung volume occur in regions of high resting lung volume.

[0046] According to the present invention, in order to identify regions with higher strain than the surrounding tissue, the absolute value of the change in lung volume or resting lung volume is not required.

[0047] Rather, the analysis can be based on a pixel-by-pixel comparison of parameters that can be measured by EIT.

[0048] The calculation and control unit may be configured to perform a function fitting of impedance amplitude values, in particular, depending on the end-expiratory impedance value of at least one lung, and to obtain a fitting function, in particular, to obtain parameters that characterize the fitting function.

[0049] Regions with larger lung volume exhibit greater ventilation and greater impedance fluctuations, so a linear relationship can be assumed, for example, between impedance amplitude values ​​and end-expiratory impedance values.

[0050] For example, a linear function ΔZ for at least one lung. ideal Linear regression may be performed to obtain =αEELI+β.

[0051] The fitted value represents a constant strain in each lung. The impedance changes measured by EIT depend on various patient-specific factors such as chest wall shape, making it difficult, if not nearly impossible, to calculate tidal volume from EIT without calibration. However, it was found that the impedance amplitude values ​​can be assumed to increase linearly with changes in lung volume.

[0052] EIT provides a distribution of impedance changes proportional to local tidal volume. Similarly, end-expiratory lung impedance can be assumed to increase linearly with end-expiratory lung volume. The distribution of end-expiratory lung impedance can be assumed to be proportional to local resting lung volume.

[0053] Assuming a constant ratio between the change in lung volume and the change in resting lung volume, a linear relationship can be assumed between the impedance amplitude value and the end-tidal impedance value.

[0054] Reconstructing EIT images reveals that such high conductivity is not typical of healthy lung tissue; therefore, regions where end-expiratory impedance is close to zero do not indicate ventilation in healthy individuals. Thus, we can assume that the offset β is 0 and that the ratio between the impedance amplitude value and the end-expiratory impedance value is constant.

[0055] To identify regions with higher strain than the surrounding lung tissue, the absolute values ​​of the local distribution of changes in lung volume and resting lung volume are not necessary. Rather, only information about the deviation of these parameters from a given, for example, linear relationship, at the local level is required.

[0056] As derived above, the distribution of tidal volume and lung volume within the lungs is related to values ​​that can be measured by EIT, namely the tidal variation and end-expiratory impedance of each pixel. Therefore, EIT can provide real-time measurements of the local distribution of lung strain.

[0057] The calculation and control unit may be further configured to determine the distribution of impedance amplitude values ​​relative to fitted values ​​and to generate control signals based on the data. For example, impedance amplitude values ​​and fitted values ​​can be assigned to the respective end-expiratory impedance values ​​of each pixel. Alternatively or additionally, impedance amplitude values ​​and fitted values ​​can be assigned to each pixel. The distribution of impedance amplitude values ​​may be compared to the distribution of fitted values.

[0058] The distribution of impedance amplitude values ​​can provide a measure of lung condition. If the distribution is sufficiently close to the fitted values ​​representing an ideal state, it can be assumed that the lungs have sufficient uniformity. The greater the bias in the distribution, the higher the likelihood of heterogeneity and malfunction.

[0059] The apparatus and / or data input unit and / or calculation and control unit for determining a measure of lung uniformity may be part of a standalone EIT device or part of an external device such as a personal computer, patient monitor, or hospital data processing system.

[0060] A device for determining the uniformity of lungs can interact with other medical devices, such as ventilators or anesthesia machines, and can form a medical technology system.

[0061] In a beneficial embodiment of the device, the calculation and control unit is configured to determine a deviation value for each pixel, which represents the deviation of the impedance amplitude value from the fitted value.

[0062] The deviation may be determined, for example, as the ratio between the impedance amplitude value and the fitted value at the same end-of-expiratory impedance value.

[0063] Alternatively, the deviation is the difference between the impedance amplitude value and the fitted value at the same end-of-expiratory impedance value, the square of the difference, or the logarithm of the ratio between the impedance amplitude value and the fitted value at the same end-of-expiratory impedance value (log(ΔZ / ΔZ)). ideal )) may be determined by

[0064] The deviation can be a measure of pulmonary heterogeneity. The closer the ratio of impedance amplitude value to the fitted value is to 1.0, the closer each impedance amplitude value is to the fitted value. Therefore, the more pixels with a ratio close to 1.0, the higher the likelihood that the lung is unaffected. High uniformity suggests a healthy lung. A narrow distribution of ratio values ​​around 1.0 reflects the activity of healthy lung tissue during normal breathing.

[0065] Alternatively or additionally, the coefficient of determination R 2 This may also be used as a criterion for lung heterogeneity.

[0066] The calculation and control unit may be configured to determine a histogram for all pixels in at least one lung, showing the number of pixels that have a deviation value at a given interval for each interval.

[0067] The distribution of the histogram can reveal information about the state of the lungs. The longer the right-hand tail of a value of 1.0, the greater the heterogeneity of the lungs and the higher the likelihood of injury.

[0068] The calculation and control unit may be configured to determine the amount of pixels whose deviation value is greater than a predetermined threshold and / or outside a predetermined region, particularly a predetermined region around a fitting function such as a confidence interval.

[0069] The number of pixels may be given as an absolute number or as a percentage. The threshold may be defined by a cutoff value that is considered to represent a pathological condition.

[0070] The threshold may be defined by a value below which a certain percentage (e.g., 95%) of the deviations of all pixels in a reference patient or a reference group of particularly healthy patients exists.

[0071] The number of pixels exceeding the confidence interval boundary or threshold may be used as a measure of lung uniformity or heterogeneity.

[0072] In an advantageous embodiment, the device includes an output unit, which is configured to provide or output an output signal for displaying a representation of data using a control signal.

[0073] The output unit may be part of the EIT device or part of an external device.

[0074] The output unit can display a graphical representation that associates impedance amplitude values ​​and / or fitted amplitude values ​​with the end-effector impedance (EELI) value of each pixel, for example, a two-dimensional graph showing the impedance amplitude value ΔZ and fitted value depending on the end-effector impedance value.

[0075] The output unit may, alternatively or additionally, display the local distribution of end-expiratory impedance values, impedance amplitude values, and / or fitted values ​​across at least one lung.

[0076] The output unit may, alternatively or additionally, display a histogram of the number of pixels associated with deviation values ​​within a specific interval, particularly the ratio of impedance amplitude value to fitted amplitude value.

[0077] The output unit may, alternatively or additionally, display the number of pixels whose deviation value is greater than a predetermined threshold and / or outside a predetermined area.

[0078] Without calibration, the absolute values ​​of impedance amplitude and end-tidal impedance do not correspond to a specific volume in milliliters. Since we are primarily interested in the pattern of the deviation distribution, the impedance amplitude can be normalized for all pixels in at least one lung to obtain the impedance amplitude normalized for all pixels in at least one lung.

[0079] Normalization can be achieved, in particular for each lung, by dividing the impedance amplitude value by the maximum value.

[0080] Alternatively or additionally, the end-tidal impedance value can be normalized across all pixels in at least one lung to obtain a normalized end-tidal impedance value for all pixels in at least one lung.

[0081] The normalized value range is up to 1. In a preferred embodiment, the device is configured to acquire, determine, and / or display reference data, such as reference patient EIT data, reference distribution of impedance amplitude values, fitted values, and / or reference histograms.

[0082] Preferably, the reference data can be determined based on electrical impedance tomography data of a reference patient. The reference patient may be a patient with healthy lungs. The reference data can be obtained by determining the mean values ​​of a large number of reference patients.

[0083] The calculation and control unit may be configured to independently determine data for each lung.

[0084] A method for determining difference parameters based on electrical impedance tomography (EIT) data according to the present invention, preferably performed on the apparatus described above, includes the following steps:

[0085] EIT data are provided from at least one region of at least one lung of a living organism over the observation period.

[0086] The impedance value is determined for each pixel in at least one region of at least one lung over the observation period.

[0087] The impedance amplitude value and end-expiratory impedance value are determined for each pixel in at least one lung.

[0088] The impedance amplitude value (ΔZ) is associated with the end-exhalation impedance (EELI) value of each pixel. Data is determined based on this information.

[0089] A control signal is generated based on the data. In particular, the calculation and control units analyze whether the data meets predetermined criteria. The data can be compared to predetermined criteria such as a predetermined data pattern, a predetermined number, or a predetermined range.

[0090] The impedance amplitude value can be functionally fitted, for example, by linear regression, depending on the end-expiratory impedance value of at least one lung. The fitting function is, for example, a linear function ΔZ for at least one lung. ideal =αEELI+β can be obtained.

[0091] Data representing the distribution of impedance amplitude values ​​relative to the fitted value can be determined. Control signals may be generated based on this data.

[0092] The statistical lung strain distribution based on EIT can be defined as the deviation from the linear relationship between impedance amplitude values ​​and end-expiratory impedance values.

[0093] This can be determined at the level of individual pixels. Assuming that all pixels corresponding to a region with healthy ventilation have a linear relationship between the impedance amplitude value and the end-expiratory impedance value, the plot of the impedance amplitude value as a function of the end-expiratory impedance value lies on a straight line for all pixels.

[0094] If the majority of the lung exhibits healthy ventilation, the ideal ratio between the impedance amplitude value and the end-tidal impedance value can be determined from a linear regression of all pixels. The impedance amplitude values ​​on this fitted function represent a constant strain distribution within the lung.

[0095] For each pixel, a deviation value can be determined that represents the deviation of the impedance amplitude value from the fitted value.

[0096] To quantify the deviation of each pixel from the ideal distortion, the ratio of the measured impedance amplitude value to the fitting function for the same end-of-exhalation impedance value can be calculated. This can result in a number of deviation values ​​with a specific statistical distribution.

[0097] In healthy lungs, the median value is close to 1.0. Since impedance amplitude values ​​are always greater than 0, the deviation is similar.

[0098] The amount of pixels whose deviation score is greater than a predetermined threshold and / or outside a predetermined area may be determined.

[0099] Output signals may be provided or output for displaying data. In particular, output signals showing a two-dimensional graph of impedance amplitude values ​​and a fitting function in the dependence of end-of-expiratory values ​​may be displayed. Additionally or alternatively, at least one histogram showing the amount of deviation values ​​may be displayed.

[0100] A cutoff can be defined based on measurements in healthy volunteers. Even in healthy lungs, some variation in impedance amplitude and end-tidal impedance values ​​is observed, and this threshold is necessary because the deviations are unequal at 1.0.

[0101] Data for each lung and / or reference data can be determined and / or displayed independently.

[0102] The data determined based on the impedance amplitude value of each pixel and the associated end-tidal impedance value can be compared with the respective distributions of reference amplitude values ​​and / or deviation values ​​obtained from healthy patients. This comparison may allow conclusions to be drawn regarding lung disease or pulmonary dysfunction, even at an early stage. Therefore, the apparatus and method according to the present invention can provide a diagnostic tool.

[0103] The object of the present invention is also solved by a computer program product that can be directly loaded into the internal memory of a computer, in particular into the computing and control units of the above-described apparatus and / or EIT apparatus, the computer program product comprising a software code portion for performing the steps of the above-described method when the product is executed on the computer.

[0104] Herein, the present invention will be described with reference to preferred embodiments and the following drawings. [Brief explanation of the drawing]

[0105] [Figure 1] This is a schematic diagram of a device for determining a measure of lung uniformity based on electrical impedance tomography (EIT) data. [Figure 2a] This is a schematic diagram of EIT images during the observation period of the first exemplary patient. [Figure 2b] An example of the impedance change measured for a single pixel during the observation period is shown. [Figure 3] This is a graphical representation of impedance amplitude values ​​that depend on the end-expiratory impedance value of each patient in the first example. [Figure 4] This is a graphical representation of the fitted amplitude values ​​for each pixel in the lungs of the first exemplary patient. [Figure 5] This is a graphical representation of the deviation values ​​of each pixel in the lungs of the first example patient. [Figure 6a] This is a graphical representation of impedance amplitude values ​​that depend on the end-expiratory impedance value of each reference patient. [Figure 6b] This is a graphical representation of impedance amplitude values ​​that depend on the end-expiratory impedance value of each COVID-19 patient. [Figure 7a] This is a graphical representation of the standard scores of reference patients. [Figure 7b] This is a graph representing the standard score of COVID-19 patients. [Figure 8]This is a graphical representation of the percentage of pixels with a deviation score greater than the threshold for a group of COVID-19 patients compared to a reference patient. [Modes for carrying out the invention]

[0106] Figure 1 shows a schematic diagram of an example of a device 100 for determining a measure of lung homogeneity based on electrical impedance tomography (EIT) data.

[0107] The apparatus 100 comprises an EIT apparatus 110 having a belt 111 with electrodes not explicitly shown in the figure. EIT data obtained by the EIT apparatus 110 is provided to a data input unit 112 via a data line 113. A calculation and control unit 114 is connected to the data input unit 112. The calculation and control unit 114 processes the EIT data, determines various values ​​representing measures of lung uniformity, and provides output signals as described below.

[0108] These output signals may be provided to an output unit 115 which includes a display unit 116 for displaying the determined values.

[0109] EIT measurements were recorded using a Sentec BB2 (Sentec Corporation, EIT Division, Landquart, Switzerland) with a woven electrode belt equipped with 32 electrodes. Impedance tomography data were recorded at a sampling rate of 47.68 Hz. Patient-specific ventilation images of respiratory lead impedance changes were calculated relative to reference measurements using the manufacturer's imaging algorithm. All calculations were performed offline using Matlab R2018b (MathWorks, Natick, Massachusetts). Distortion images were calculated using 32x32 pixel images created by the manufacturer's imaging algorithm.

[0110] Figure 2a shows EIT images 10 for different time points during the observation period ΔT of the first exemplary patient. t1 , 10 t2 , 10 t3 , 10t4 A schematic diagram is shown. The EIT image is based on the impedance value at each pixel 1 of the lung 2, and shows the impedance value measured for each pixel in the observation plane defined by the position of the belt 111 on the patient. Each pixel in the EIT image is colored according to the impedance value of the pixel.

[0111] Figure 2b shows an example of the change in impedance value measured for one pixel 1 during the observation period ΔT.

[0112] The impedance amplitude value ΔZ is determined as the maximum difference between the maximum impedance value measured during the observation period ΔT and the subsequent minimum value.

[0113] The end-expiratory impedance value (EELI) may be determined as the average minimum value of the impedance during the observation period ΔT.

[0114] For each pixel 1 in both lungs 2, the impedance amplitude value ΔZ and the end-expiratory impedance value EELI are determined.

[0115] Figure 3 shows a graphical representation of the impedance amplitude value ΔZ, which depends on the end-expiratory impedance value EELI for each of the first exemplary patients.

[0116] Since we are primarily interested in the pattern, we normalized the impedance amplitude value ΔZ and the end-tidal impedance value EELI to a range of up to 1.0. We then divided the impedance amplitude value ΔZ and the end-tidal impedance value EELI by their respective maximum values.

[0117] The impedance amplitude value ΔZ was normalized separately for both lungs, and the ΔZ for each lung 2 was divided by the maximum value for that lung 2.

[0118] The normalized impedance amplitude value ΔZ of each pixel is plotted against its respective normalized end-expiratory impedance value EELI.

[0119] Function fitting, in this case linear regression ΔZ = α·EELI, is performed separately for each of the two lungs 2 to obtain the fitting functions ΔZ ideal 1(EELI) and ΔZ ideal 2(EELI). The values ΔZ ideal 1, ΔZ ideal 2 are values corresponding to specific end - expiratory impedance values when there is a certain strain in each lung.

[0120] [[ID= Similarly, function fitting ΔZ = α·EELI + β can be performed and EELI can be determined / resampled in a way that eliminates the offset β. The linear relationship between ΔZ and EELI reflects the linear relationship between the change in lung volume dV and the resting lung volume V0.

[0121] ΔZ for each pixel of each of the two lungs 2 of an exemplary patient ideal 1, ΔZ ideal 2 are shown in FIG. 4. Each pixel 1 of the observation plane assigned to one of the lungs 2 is colored according to the value ΔZ ideal 1 or ΔZ ideal 2 of the respective value EELI of each pixel.

[0122] FIG. 4 shows a graphical representation of the fitted amplitude values for each pixel of the lungs of a first exemplary patient, with equal values of the amplitude values shown as horizontal lines 3.

[0123] It can be seen that linear fitting represents a reasonable model of the normal behavior of the lungs 2. Values with a large ideal amplitude ΔZ ideal are located at the pixels at the center of the lungs 2, but smaller values can be found in the boundary regions of the lungs 2.

[0124] For each pixel, a deviation value Strain ideal representing the deviation of the impedance amplitude value ΔZ from the fitted value ΔZ EIT can be determined.

[0125] In this example, the deviation value Strain EITFor each pixel 1 in each lung 2, the impedance amplitude value ΔZ and the fitted value ΔZ are determined at the same end-expiratory impedance value EELI. ideal The ratio ΔZ / ΔZ ideal It is obtained by [method].

[0126] Figure 5 shows the standard scores for the first example patient. EIT ΔZ / ΔZ ideal 1 and ΔZ / ΔZ ideal The graph shows the local distribution of 2. Each pixel 1 in the observation plane belonging to one of the lungs 2 represents the end-of-expiratory value EELI of each pixel ΔZ / ΔZ ideal 1 or ΔZ / ΔZ ideal Assigned to 2. For each of the lungs 2, the respective standard scores Strain EIT ΔZ / ΔZ ideal 1 and ΔZ / ΔZ ideal The equal value of 2 is shown as level line 4.

[0127] As can be seen by comparing with Figure 4, the standard score is the fitted value ΔZ ideal Its distribution is different from that of the others.

[0128] It is possible to identify the lung region and the deviation value ΔZ / ΔZ ideal A score of 1 deviates to a greater or lesser extent from the ideal, undisturbed standard deviation of 1.0. These ranges may be considered to have distorted lung distortion.

[0129] Therefore, it is possible to identify patients with areas of high strain within the lungs as well as non-physiological pulmonary heterogeneity.

[0130] Figure 6a shows a graphical representation of the normalized impedance amplitude ΔZ, which depends on the normalized end-expiratory impedance value (EELI) of each reference patient, and Figure 6b shows a graphical representation of the normalized impedance amplitude ΔZ, which depends on the end-expiratory impedance value (EELI) of each COVID-19 patient.

[0131] Both representations show the measured pixel values ​​and fitting functions for both lungs separately. The impedance amplitude values ​​ΔZ and end-expiratory impedance values ​​EELI of the reference patient were obtained by EIT measurements in healthy volunteers.

[0132] For COVID-19 patients, two consecutive EIT measurements were performed with a 3-day interval between them. For healthy volunteers, only one measurement was recorded. All measurements may be recorded in a seated, supine, and left and right lateral position. The examples shown in this application are based solely on EIT recordings in the supine position.

[0133] Lung strain resulting from increased respiratory drive places a high energy load on lung tissue, potentially increasing the patient's risk of lung injury. Such large lung strain may contribute to respiratory failure in COVID-19 patients. Respiratory failure due to COVID-19 pneumonia can rapidly progress to an acute respiratory distress syndrome (ARDS)-like clinical presentation with significantly heterogeneous lung damage. These patients often develop pathological respiratory drive with severe hypoxemia and high respiratory load, even in the early stages. During the course of the disease, this increased respiratory drive, which induces high strain and energy load on fragile lung tissue, increases the patient's risk of lung injury.

[0134] Even in healthy lungs, distortion is not zero. Instead, volume changes during ventilation in a given region conform to the total lung volume in that region, resulting in physiological levels of distortion. Furthermore, even in healthy lungs, some pixels deviate from linear relationships. Therefore, the typical distribution of these values ​​must be defined from measurements in healthy volunteers.

[0135] Therefore, the impedance amplitude value ΔZ is not exactly on the fitting line. In a study involving 10 healthy volunteers aged 32±8 years and 10 COVID-19 patients aged 55±21 years, a good overall correlation was observed, with a mean R 2 The value was 0.80 in COVID-19 patients and 0.92 in healthy volunteers. 2 R 2=1-sum(ΔZ-ΔZ ideal ) 2 / sum(ΔZ-Average(ΔZ)) 2 It can be calculated as follows. The lowest R 2 The values ​​were 0.77 for volunteers and 0.29 for patients.

[0136] R-squared 2 The correlation itself, as shown, can be considered a measure of distortion and / or heterogeneity. However, information about which pixels exhibit the correct ratio ΔZ / EELI between impedance amplitude value ΔZ and end-expiratory impedance value EELI becomes less clear as there are fewer healthy lung pixels. Therefore, R 2 When the value is low, information regarding the local distribution of strain should be handled with caution.

[0137] However, as can be seen in Figure 6b, the variation in impedance amplitude values ​​with respect to the fitting function is much greater in COVID-19 patients than in healthy reference patients.

[0138] Therefore, the distribution of normalized impedance amplitude values ​​ΔZ, which depend on each normalized end-expiratory impedance value EELI, can be considered a measure of lung heterogeneity.

[0139] Figure 7a shows the standard scores of the reference patients. EIT Figure 7b shows the graph representation of the standard score of COVID-19 patients. EIT This shows a graphical representation of the result.

[0140] Standard Score Strain EIT This is the impedance amplitude value ΔZ and the fitting function ΔZ at the same end-of-expiratory impedance value EELI. ideal It was calculated as a ratio to the value of .

[0141] The average of these ratios is always 1.0, and in a healthy lung, most pixels have a deviation value close to 1.0. EIT It has.

[0142] Physiologically, excessively high values ​​are more fatal to the patient. Or, for example, log(ΔZ / ΔZ) ideal ) can also be determined for all pixels. This consideration allows us to provide tails on both sides of the ideal value and set thresholds on both sides of the ideal value. Too large ΔZ / ΔZ ideal Not only the value of ΔZ, but also the value of ΔZ / ΔZ is too small. ideal The value of can also be taken into consideration.

[0143] Figures 7a and 7b show the standard scores at one equidistant interval between two standard scores. EIT This shows a histogram of the number of pixels. In this case, the interval has a length of 1 / 10.

[0144] As can be seen in Figure 7a, the maximum value for healthy reference patients is 1, as expected, and most pixels are at least close to 1.

[0145] In the case of COVID-19 patients, the histogram shows a long tail, which means that many pixels have large deviation values ​​(strain). EIT It means having

[0146] A cutoff can be defined based on measurements in a sample group of healthy volunteers. Assuming that in healthy lung tissue only a few outlier pixels exhibit non-physiologically large distortion, the impedance amplitude value ΔZ and the fitted function value ΔZ can be defined. ideal The ratio, with 97.5% of all pixels in healthy volunteers having a lower ratio, can be determined as a cutoff for healthy lung distortion.

[0147] In the image of the local strain EIT distribution (see Figure 5), pixels with values ​​exceeding this threshold indicate regions of high lung strain.

[0148] Furthermore, the number of pixels displaying such high deviation scores can serve as an indicator of total lung distortion. Therefore, the sum of these pixels can be considered an indicator of total distortion.

[0149] In the patients in the aforementioned study, an average of 21 out of 239 pixels in the lung region showed a distortion value greater than 2.11, compared to an average of only 6 out of 242 pixels in the lung region of healthy volunteers.

[0150] Figure 8 shows a box plot-style graphical representation of the percentage of pixels in the group of COVID-19 patients in the study that have a deviation score greater than the threshold, compared to the reference patient in the study.

[0151] This numerical difference between patients and volunteers is preserved to the extent that it is statistically significant with a p-value calculated using a two-sample test of 0.028.

[0152] p is the probability that the skewed values ​​of patients and volunteers originate from random samples independent of a normally distributed system, where the mean and variability are equal but unknown (null hypothesis). The test statistic t is calculated, and p is the probability that the test statistic is observed to be extreme or more extreme than the observed value under the null hypothesis. The calculation is performed using statistical software employing numerical methods. Here, the Matlabs ttest2 function was used.

[0153] The median percentage of pixels greater than 2.11 was 1.8% in healthy volunteers and 10.2% in COVID-19 patients.

Claims

1. A device (100) for determining a measure of lung uniformity based on electrical impedance tomography (EIT) data, A data input unit (112) that receives the EIT data obtained by an electrical impedance tomography device (110), wherein the data input unit (112) is configured to receive and provide EIT data from at least one region of at least one lung (2) of a living organism over an observation period (ΔT), A calculation and control unit (114) connected to the data input unit, Equipped with, The calculation and control unit (114) is - Determine the impedance value over the observation period (ΔT) for each pixel (1) in at least one region of at least one lung (2), - Determine the impedance amplitude value (ΔZ) of each pixel (1) of at least one lung (2), - Determine the end-expiratory impedance (EELI) value for each pixel in at least one lung, - The impedance amplitude value (ΔZ) and the end-of-exhalation impedance value (EELI) are individually associated with each pixel (1), - The data is determined based on the impedance amplitude value (ΔZ) and the associated end-expiratory impedance value (EELI), - A control signal is generated based on the above data. The calculation and control unit (114) is configured to perform a function fitting of the impedance amplitude value (ΔZ) depending on the end-expiratory impedance value (EELI) for at least one lung, and to obtain a fitted value (ΔZ ideal) from a linear fitting function ΔZ ideal = αEELI + β for at least one lung. The device is configured such that the calculation and control unit determines, for each pixel, a deviation value (Strain EIT) representing the deviation of the impedance amplitude value ΔZ from the matching value (ΔZ ideal).

2. The apparatus according to claim 1, wherein the calculation and control unit is configured to compare the data with a predetermined standard.

3. The apparatus according to claim 1, wherein the calculation and control unit (114) is configured to compare the data with a predetermined standard based on a patient control group.

4. The calculation and control unit uses the standard deviation (Strain ＥＩＴ ) is the impedance amplitude value (ΔZ) and the matching value (ΔZ) at the same end-expiratory impedance value (EELI). ｉｄｅａｌ The ratio between (ΔZ / ΔZ) ｉｄｅａｌ The apparatus according to claim 1, wherein the apparatus is as follows:

5. The calculation and control unit (114) calculates a deviation value (Strain) at a constant interval for each of the intervals between all pixels (1) of at least one lung (2). ＥＩＴ The apparatus according to claim 1, configured to determine a histogram showing the number of pixels having ).

6. The calculation and control unit (114) is The aforementioned standard score (Strain ＥＩＴ ) is greater than a predetermined threshold, and / or the standard score (Strain ＥＩＴ The apparatus according to claim 1, wherein the ) is configured to determine the amount of pixels (1) that are outside a predetermined area.

7. The apparatus according to any one of claims 1 to 6, further comprising an output unit (115), wherein the output unit (115) is configured to provide or output an output signal for displaying a representation of the data using the control signal.

8. The output unit (115) uses the control signal to: - A graph relating the impedance amplitude value (ΔZ) and / or the fitted value (ΔZ ideal) for each pixel to the end-expiratory impedance value (EELI), - Local distribution of the end-expiratory impedance value (EELI), impedance amplitude value (ΔZ), and / or fitted value (ΔZ ideal) across at least one lung, - A histogram of the number of pixels associated with the aforementioned standard deviation (Strain EIT) within a specific interval, - A figure showing the amount of pixels (1) whose standard deviation (Strain EIT) is greater than a predetermined threshold and / or whose standard deviation (Strain EIT) is outside a predetermined area, The apparatus according to claim 7, configured for displaying.

9. The calculation and control unit (114) is The impedance amplitude value (ΔZ) is normalized across all pixels (1) for at least one lung (2), and the normalized amplitude value (ΔZ) is obtained for all pixels of at least one lung (2). and / or, The apparatus according to any one of claims 1 to 8, configured to normalize the end-expiratory impedance value (EELI) across all pixels (1) for at least one lung (2), and to obtain the normalized end-expiratory impedance value (EELI) for each pixel (1) of at least one lung (2).

10. The apparatus according to any one of claims 1 to 9, configured to determine and / or display reference data.

11. The apparatus according to any one of claims 1 to 10, configured to determine and / or display a reference distribution or reference histogram of impedance amplitude values ​​(ΔZ) and fitted values ​​(ΔZ ideal) based on electrical impedance tomography (EIT) data of a reference patient.

12. The apparatus according to any one of claims 1 to 11, wherein the calculation and control unit (114) is configured to independently determine data for each of the lungs (2).

13. A method for determining the measure of uniformity of the lung based on electrical impedance tomography (EIT) data, using the apparatus described in any one of Claims 1 to 10, - A step of providing EIT data from at least one region of at least one lung (2) of a living organism over an observation period (ΔT), - A step of determining the impedance value over the observation period for each pixel (1) in at least one region of at least one lung (2), The steps include determining the impedance amplitude value (ΔZ) of each pixel (1) of at least one lung (2), - A step of determining the end-expiratory impedance (EELI) value of each pixel (1) of at least one lung (2), - A step of individually associating the impedance amplitude value (ΔZ) and the end-of-exhalation impedance value (EELI) for each pixel (1), - A step of determining data based on the impedance amplitude value (ΔZ) and the associated end-expiratory impedance value (EELI), - A step of generating a control signal based on the above data, and further, The steps include: performing a functional fitting of the impedance amplitude value (ΔZ) depending on the end-expiratory impedance value (EELI) for at least one lung, obtaining a fitted value (ΔZ ideal) from the linear fitting function ΔZ ideal = αEELI + β for at least one lung (2), and determining data representing the distribution of impedance amplitude value (ΔZ) with respect to the fitted value (ΔZ ideal); For each pixel, the steps include determining a deviation value (Strain EIT) that represents the deviation of the impedance amplitude value ΔZ from the matching value (ΔZ ideal), Methods that include...

14. The method according to claim 13, wherein data is compared with a predetermined standard.

15. The method according to claim 13, comprising the step of predetermining criteria based on a patient control group and storing the criteria in data storage.

16. The method according to claim 13, further comprising the step of determining the ratio (ΔZ / ΔZ ideal) between the impedance amplitude value (ΔZ) at the same end-expiratory impedance value (EELI) and the matching value (ΔZ ideal).

17. The aforementioned standard score (Strain ＥＩＴ ) is greater than a predetermined threshold, and / or the standard score (Strain ＥＩＴ The method according to claim 14, further comprising the step of determining the amount of pixels (1) that are outside a predetermined area.

18. The method according to any one of claim 13, comprising the step of providing or outputting an output signal for displaying the representation of the aforementioned data.

19. The method according to any one of claims 13, comprising the step of determining data for each of the lungs (2) and / or the step of determining reference data.

20. A computer program product that can be directly loaded into and / or on a computer for execution on a computer, the computing and control unit of the apparatus and / or EIT apparatus according to any one of claims 1 to 10, and / or comprising a software code portion for performing the steps of the method according to any one of claims 13 to 19.

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