Apparatus and method for determining lung homogeneity measures - Patents.com

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, and a computer program product, for determining a measure of lung homogeneity based on electrical impedance tomography (EIT) data. [Background technology]

[0002] Electrical impedance tomography (EIT) is a non-invasive imaging technique based on the application of electrical current and the measurement of voltage through electrodes attached to the patient's body. EIT provides an image of the distribution of conductivity, permittivity, impedance, or resistivity, or their changes. In the following, the measurements are referred to as "impedance values" and the above distributions are also collectively referred to as "electrical properties". This image is referred to as an EIT image. The image or image sequence shows differences in the electrical properties of various body tissues, bones, skin, body fluids and organs, especially the lungs, which are useful for monitoring the patient's condition.

[0003] A typical EIT arrangement is shown in WO 2015 / 048917. A pair of electrodes are arranged on a belt such that they are in electrical contact with the skin and are spaced at a fixed 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 electrodes of a pair, the current or voltage between the remaining electrode can be measured. The measured voltage of the body part can be reconstructed into an electrical property or a change in electrical property by a reconstruction algorithm used by a data processor to obtain a representation of the distributed impedance values ​​over a cross-section of the patient where the electrode belt rings are located. The electrical property is displayed on a screen.

[0004] In the EIT process, multiple impedance measurements are recorded, for example with 16 or 32 electrodes. From these impedance measurements, an EIT image reconstruction algorithm can generate a two-dimensional image containing pixels that represent the characteristics of the lung. The image can contain 32 x 32 pixels that represent locations inside and outside the lung.

[0005] Information about the electrical properties obtained by EIT can be projected onto cross-sectional images derived from an anatomical model in an anatomical context, showing, for example, contours representing the outer boundaries of the modeled organ in the electrode plane.

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

[0007] Up to 100 or more cross-sectional EIT images can be reconstructed per second. These high-temporal resolution images reflect the local electrical properties in the lung tissue as affected by the respiratory and cardiac cycles. In contrast to CT scans, EIT images display not morphological information but functional information indicating the distribution of regional tidal volume, regional lung recruitment, expiratory time constant, or pulmonary perfusion. EIT also measures end-expiratory pulmonary impedance (EELI), a parameter that correlates with functional residual capacity (FRC) at the global 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 volume measurements, and therefore direct calculation of volume and volume changes from EIT data is not possible.

[0009] DE 102017007224 discloses determining the properties and property changes of at least two local regions of the lung based on EIT data representing regional changes in lung compliance or elasticity. This method does not provide a measure at the whole lung level.

[0010] EP 3725222 relates to a system for real-time determination of local stress in the lungs during artificial ventilation. A specific electrical impedance value at a specific time point is assigned to a specific EIT pixel. A local tidal volume value z is determined as the difference between the end-inspiratory electrical impedance (ZINSP) and the end-expiratory electrical impedance (ZEXSP) of the specific EIT pixel. A prestrain value is determined by dividing the local tidal volume by the end-inspiratory electrical impedance. The prestrain value may be normalized to a reference strain value, for example at a predefined PEEP value. Changes in lung condition may be monitored for a specific patient, but no reference for the lung condition itself may be derived. Summary of the Invention [Problem to be solved by the invention]

[0011] The object of the present invention is to overcome the drawbacks of the prior art, in particular to provide an apparatus and a method making it possible to provide meaningful data relating to the lungs in a simple and reliable manner. [Means for solving the problem]

[0012] According to the present invention, these and other objects are achieved by an apparatus and a method for determining a measure of lung homogeneity based on Electrical Impedance Tomography (EIT) data as set forth in the independent claims.

[0013] The ratio of the change in lung volume dV during ventilation to the resting lung volume V0 or 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 discovery that determining, monitoring and analysing pixel-level distortions with EIT can provide meaningful information even if EIT is not able to determine a specific value of a volume.

[0015] Lung strain due to increased respiratory drive during mechanical ventilation may place a high energy burden on lung tissue, thereby increasing the patient's risk of self-harm (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] Global strain can be derived from the ratio of tidal volume, VT, divided by functional residual capacity, 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 is largely unclear what the healthy boundaries of such a ratio dV / V0 are and when strains become non-physiologically high (Gattinoni et al., 2012). In mechanically ventilated pigs, Protti et al. did not find lung injury for strain values ​​below 1.5 and acute respiratory failure for strain values ​​above 2.5, although a grey zone remains between these values ​​(Protti et al., 2012, Gattinoni et al., 2012). However, Gattinoni noted that such high lung strains do not typically appear in mechanically ventilated patients, pointing to lung heterogeneity as a possible main cause of lung injury in mechanically ventilated patients (Gattinoni et al., 2012). So far, the common definition of strain as dV / V0 at the whole lung level means that such inhomogeneities often go unnoticed because it requires local lung strain measurements.

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

[0019] US20140316266 discloses the use of ultrasound imaging to determine regional lung strain, lung volume and changes in lung volume.

[0020] The device according to the invention comprises a data entry unit. The data input unit is designed to receive EIT data obtained by an electrical impedance tomography device comprising electrodes placed on the patient.

[0021] The EIT data may be provided to the data input unit directly from the electrical impedance tomography device or indirectly via a data line, signal line or network connection.

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

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

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

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

[0026] The impedance values ​​are typically stored as a data set in a data memory or data storage area allocated to the calculation 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 an impedance amplitude value for each pixel of the at least one lung.

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

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

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

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

[0032] The data may provide a representation of the local distribution of the measurements. The data can be used to provide a graphical representation showing the relationship between the change in impedance for each pixel and the end-expiratory pulmonary impedance. In non-diseased lungs, the impedance amplitude values ​​and the end-expiratory impedance values ​​are assumed to be somewhat correlated.

[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 is characteristic of the assignment relationship between the impedance amplitude value of each pixel and the associated end-tidal impedance value.

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

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

[0036] In particular, the computation and control unit is further configured to determine whether the data meets a predetermined criterion. The data may be compared to a predetermined criterion, such as, for example, a predetermined data pattern, a predetermined threshold number, or a predetermined range. If the criterion is not met, a non-uniformity indicator may be generated.

[0037] The baseline may be predetermined based on a control group of patients, which may consist of people with unaffected lungs.

[0038] The impedance amplitude values ​​and associated end-expiratory impedance values ​​may be measured or determined for a control group.

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

[0040] The criteria may be derived from the distribution of impedance amplitude values ​​across associated end-tidal impedance values.

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

[0042] In EIT, it is difficult to determine the absolute value of impedance and, from this, the lung volume, especially without calibration. Thus, although changes in distribution and volume can be quantified, intrapatient comparability remains difficult. Changes in volume and resting lung volume are related to impedance amplitude values ​​and end-expiratory pulmonary impedance, respectively, but these measurements cannot be converted to volume measurements. In particular, values ​​of end-expiratory impedance values ​​can vary widely even in EIT recordings of the same patient, leading to widely different strain values ​​when calculated as a simple ratio between impedance amplitude values ​​and end-expiratory pulmonary impedance.

[0043] Therefore, the absolute value of the ratio between impedance amplitude and end-expiratory pulmonary impedance may vary between measurements on patients with similar lung strains, or even between successive measurements on the same patient.

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

[0045] This makes the method less susceptible to distortions caused by single outliers. In a healthy lung, strain is assumed to be low and fairly constant throughout the lung, with large changes in lung volume occurring in regions of high resting lung volume.

[0046] In order to identify regions that have higher strain than the surrounding tissue, absolute values ​​of lung volume or change in resting lung volume are not required according to the present invention.

[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 in particular be configured to perform a function fitting of the impedance amplitude values ​​in dependence on the end-expiratory impedance values ​​of the at least one lung and to obtain a fitting function, in particular to obtain parameters characterizing the fitting function.

[0049] Since regions of larger lung volume exhibit greater ventilation and greater impedance variation, for example, a linear relationship between impedance amplitude and end-expiratory impedance values ​​can be assumed.

[0050] For example, a linear function ΔZ for at least one lung ideal A linear regression may be performed to obtain:

[0051] The fitted values ​​represent the constant strain in each lung. Although the impedance changes measured by EIT depend on various patient-specific factors, such as thoracic geometry, making calculation of tidal volumes from EIT at least difficult if not almost impossible without calibration, it was found that impedance amplitude values ​​can be assumed to increase linearly with changes in lung volume.

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

[0053] From the assumption of a constant ratio of change in lung volume to resting lung volume, a linear relationship between impedance amplitude and end-expiratory impedance values ​​can be assumed.

[0054] Depending on the EIT image reconstruction, such high conductivity is not typical of healthy lung tissue, and therefore regions with near-zero end-expiratory impedance do not indicate ventilation in healthy individuals. Therefore, the offset β can be assumed to be 0, and the ratio between the impedance amplitude value and the end-expiratory impedance value can be assumed to be constant.

[0055] In order to identify regions with higher strain than the surrounding lung tissue, absolute values ​​of the local distribution of lung volume and change in resting lung volume are not necessary, rather only information about the deviation of these parameters from a predefined, e.g., 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 of impedance and end-expiratory impedance at each pixel. Thus, EIT can provide a real-time measurement of the regional distribution of lung strain.

[0057] The calculation and control unit may be further configured to determine a distribution of impedance amplitude values ​​relative to the fitting value and generate a control signal based on the data. For example, an impedance amplitude value and a fitting value may be assigned to each end-tidal impedance value of each pixel. Alternatively or additionally, an impedance amplitude value and a fitting value may be assigned to each pixel. The distribution of impedance amplitude values ​​may be compared to the distribution of fitting values.

[0058] The distribution of impedance amplitude values ​​can provide a measure of the lung condition. If the distribution is close enough to a fitted value that represents the ideal condition, it can be assumed that the lungs have sufficient homogeneity. The more skewed the distribution, the more likely it is that inhomogeneities will occur and cause malfunctions.

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

[0060] The device for determining a measure of lung uniformity can interact with further medical devices, such as a ventilator or anesthesia device, to form a medico-technical system.

[0061] In an advantageous embodiment of the device, the calculation and control unit is configured to determine, for each pixel, a deviation value representative of the deviation of the impedance amplitude value from a 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-expiratory impedance value.

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

[0064] The deviation may be a measure of lung heterogeneity. The closer the ratio of the impedance amplitude value to the fit value is to 1.0, the closer the respective impedance amplitude value is to the fit value. Thus, the more pixels with ratios close to 1.0, the higher the likelihood of unaffected lungs. High uniformity suggests healthy lungs. A narrow distribution of ratio values ​​around 1.0 reflects healthy lung tissue activity during normal breathing.

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

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

[0067] The distribution of the histogram gives an idea of ​​the condition of the lungs: the longer the tail to the right of the value 1.0, the more heterogeneous the lungs are and the greater the chance of damage.

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

[0069] The amount 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 there exists a certain percentage (eg 95%) of the deviations of all pixels of a reference patient or a reference group of particularly healthy patients.

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

[0072] In an advantageous embodiment, the apparatus comprises an output unit, the output unit being configured to provide or output an output signal for displaying a representation of the data using the control signal.

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

[0074] The output unit may display a graphical representation relating the impedance amplitude values ​​and / or the fitted amplitude values ​​to the end-expiratory impedance values ​​(EELI) of each pixel, e.g. a two-dimensional graph showing the impedance amplitude values ​​ΔZ and the fitted values ​​depending on the end-expiratory impedance values.

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

[0076] The output unit may alternatively or additionally display a histogram of the number of pixels associated with the deviation values ​​within a particular interval, in particular the interval of the ratio between the impedance amplitude value and the adaptation amplitude value.

[0077] The output unit may alternatively or additionally display the amount of pixels whose deviation value is greater than a predefined threshold and / or is outside a predefined region.

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

[0079] Normalization may be achieved by dividing the impedance amplitude value by the maximum value, specifically for each lung.

[0080] Alternatively or additionally, the end-expiratory impedance values ​​may be normalized across all pixels of the at least one lung to obtain a normalized end-expiratory impedance value for all pixels of the at least one lung.

[0081] The normalized values ​​range up to 1. In a preferred embodiment, the device is configured to obtain, determine and / or display reference data, e.g. EIT data of a reference patient, a 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 can be a patient with healthy lungs. The reference data can be obtained by determining an average value of a number of reference patients.

[0083] The computation and control unit may be configured to determine the data for each of the lungs independently.

[0084] A method for determining a difference parameter based on electrical impedance tomography (EIT) data according to the present invention, preferably carried out on an apparatus as described above, comprises the following steps.

[0085] EIT data is provided from at least one region of at least one lung of the living subject over an observation period.

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

[0087] An impedance amplitude value and an end-expiratory impedance value are determined for each pixel of the at least one lung.

[0088] The impedance amplitude value (ΔZ) is related to the end-expiratory impedance value (EELI) of each pixel. Based on this information, data is determined.

[0089] A control signal is generated based on the data. In particular, the computation and control unit analyses whether the data meets a predetermined criterion. The data may be compared to a predetermined criterion, such as a predetermined data pattern, a predetermined number, or a predetermined range.

[0090] Depending on the end-expiratory impedance value of the at least one lung, a function fit, for example a linear regression, of the impedance amplitude value can be performed. The fit function can be, for example, a linear function ΔZ ideal =αEELI+β can be obtained.

[0091] Data representing a distribution of impedance amplitude values ​​relative to the fit value may be determined. A control signal based on the data may be generated.

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

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

[0094] If the majority of the lungs exhibit healthy ventilation, the ideal ratio between impedance amplitude and end-expiratory impedance values ​​can be determined from a linear regression of all pixels. Impedance amplitude values ​​that lie on this fitted function indicate 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 a fit value.

[0096] To quantify the deviation from the ideal distortion for each pixel, the ratio of the measured impedance amplitude value to the fitted function for the same end-expiratory impedance value can be calculated, which may result in a large number of deviation values ​​with a certain statistical distribution.

[0097] In healthy lungs, the median value is close to 1.0, as is the deviation, since the impedance amplitude value is always greater than 0.

[0098] The amount of pixels whose deviation value is greater than a predefined threshold and / or outside a predefined region may be determined.

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

[0100] The cutoff can be defined based on measurements in healthy volunteers. This threshold is necessary because, even in healthy lungs, some variation in impedance amplitude and end-expiratory impedance values ​​is observed, leading to deviations unequal to 1.0.

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

[0102] The data determined based on the impedance amplitude value of each pixel and the associated end-expiratory impedance value can be compared with a respective distribution of reference amplitude values ​​and / or deviation values ​​obtained from healthy patients. By comparison, a conclusion can be drawn regarding a lung disease or pulmonary dysfunction, even at an early stage. Thus, the device and method according to the invention can provide a diagnostic tool.

[0103] The object of the present invention is also solved by a computer program product loadable into the internal memory of a computer, in particular directly into a calculation and control unit of an apparatus as described above and / or an EIT device, which computer program product comprises software code portions for carrying out the steps of the method as described above, when said product is executed on a computer.

[0104] The invention will now be described with reference to preferred embodiments and the following drawings. [Brief description of the drawings]

[0105] [Figure 1] FIG. 1 is a schematic diagram of an apparatus for determining a measure of lung homogeneity based on electrical impedance tomography (EIT) data. [Figure 2a] FIG. 1 is a schematic diagram of EIT images during an observation period of a first exemplary patient. [Figure 2b] An example of the change in impedance measured for one pixel during an observation period is shown. [Diagram 3] 1 is a graphical representation of impedance amplitude values ​​depending on respective end-expiratory impedance values ​​for a first exemplary patient; [Figure 4] 1 is a graphical representation of fitted amplitude values ​​for each pixel of a first exemplary patient's lungs. [Diagram 5] 1 is a graphical representation of the deviation values ​​of each pixel of a first exemplary patient's lungs. [Figure 6a] 1 is a graphical representation of impedance amplitude values ​​depending on the respective end-expiratory impedance values ​​of a reference patient. [Figure 6b] 1 is a graphical representation of impedance amplitude values ​​depending on the respective end-expiratory impedance values ​​of COVID-19 patients. [Figure 7a] 1 is a graphical representation of the standard deviations of the reference patients. [Figure 7b] A graphical representation of the standard deviation scores of COVID-19 patients. [Figure 8]1 is a graphical representation of the percentage of pixels having a deviation value greater than a threshold for a group of COVID-19 patients compared to a reference patient. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

[0110] FIG. 2a shows EIT images 10 for different time points during an observation period ΔT of a first exemplary patient. t1 , 10 t2 , 10 t3 , 10t4 1 shows a schematic diagram of a lung imaging system according to the present invention. The EIT image is based on the impedance values ​​at each pixel 1 of the lungs 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 of the EIT image is coloured according to the impedance value of the pixel.

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

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

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

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

[0115] FIG. 3 shows a graphical representation of the impedance amplitude values ​​ΔZ in dependence on the respective end-expiratory impedance values ​​EELI of a first exemplary patient.

[0116] As we are primarily interested in patterns, the impedance amplitude values ​​ΔZ and the end-expiratory impedance values ​​EELI were normalized to a range of 1.0. The impedance amplitude values ​​ΔZ and the end-expiratory impedance values ​​EELI were divided by their respective maximum values.

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

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

[0119] Function fitting, in this case a linear regression ΔZ=α EELI, is used to determine the fitted function ΔZ for each of the two lungs 2. ideal 1(EELI) and ΔZ ideal This is performed separately for both lungs to obtain 2(EELI). ideal 1. ΔZ ideal 2 is the value that corresponds to a particular end-expiratory impedance value when there is a constant strain in each lung.

[0120] Similarly, a function fit ΔZ=α EELI+β can be performed to determine / rescale EELI in a manner 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 lung 2 of the exemplary patient ideal 1. ΔZ ideal 2 is shown in FIG. 4. Each pixel 1 of the observation plane assigned to one of the lungs 2 has a value ΔZ ideal 1 or ΔZ ideal Colored according to 2.

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

[0123] The linear fit is found to represent a reasonable model of the normal behavior of the lung 2. Large ideal amplitude ΔZ ideal The value with is located in the pixel in the center of the lung 2, while smaller values ​​can be found in the boundary region of the lung 2.

[0124] For each pixel, the fit value ΔZ ideal The deviation value Strain represents the deviation of the impedance amplitude value ΔZ from EIT can be determined.

[0125] In this example, the deviation value Strain EITis the impedance amplitude value ΔZ and the fitted value ΔZ at the same end-expiratory impedance value EELI for each pixel 1 of each lung 2. ideal Ratio of ΔZ / ΔZ ideal is obtained by

[0126] FIG. 5 shows the Strain deviation value for the first exemplary patient. EIT , ΔZ / ΔZ ideal 1 and ΔZ / ΔZ ideal 2. Each pixel 1 of the observation plane belonging to one of the lungs 2 has a value ΔZ / ΔZ of the end-tidal value EELI of the respective pixel. ideal 1 or ΔZ / ΔZ ideal 2. For each of the lungs 2, the respective deviation values ​​Strain EIT , ΔZ / ΔZ ideal 1 and ΔZ / ΔZ ideal An equal value of 2 is shown as level line 4.

[0127] As can be seen by comparing with Figure 4, the deviation value is the fitted value ΔZ ideal are distributed differently.

[0128] The lung regions can be identified and the deviation value ΔZ / ΔZ ideal 1 deviates more or less from the ideal, undisturbed deviation value of 1.0. These regions can be considered to have disturbed lung distortion.

[0129] Thus, patients with areas of high intrapulmonary distortion as well as non-physiological lung heterogeneity can be identified.

[0130] FIG. 6a shows a graphical representation of the normalized impedance amplitude values ​​ΔZ in dependence on the respective normalized end-expiratory impedance values ​​EELI of a reference patient, and FIG. 6b shows a graphical representation of the normalized impedance amplitude values ​​ΔZ in dependence on the respective end-expiratory impedance values ​​EELI of a COVID-19 patient.

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

[0132] For COVID-19 patients, two consecutive EIT measurements were performed, 3 days apart. For healthy volunteers, only one measurement was recorded. All measurements may be recorded in sitting, supine, left and right lateral positions. The examples shown in this application are based only on EIT recordings in the supine position.

[0133] Lung distortion resulting from increased respiratory drive may impose a high energy load on lung tissue, thereby increasing the risk of patients self-harming their lungs. Such large lung distortion 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 picture with significantly heterogeneously damaged lungs. 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 one-time strain and energy load on vulnerable lung tissue, increases the risk of patients self-harming their lungs.

[0134] Even in healthy lungs, the distortion is not zero. Instead, the volume change during ventilation in a region is matched with the total lung volume in that region, resulting in a physiological level of distortion. Moreover, even in healthy lungs, some pixels deviate from the linear relationship. Therefore, a typical distribution of these values ​​needs to be defined from measurements in healthy volunteers.

[0135] Therefore, the impedance amplitude values ​​ΔZ are not exactly on the straight line fit. A study including 10 healthy volunteers aged 32 ± 8 years and 10 COVID-19 patients aged 55 ± 21 years found an overall good correlation, with a mean R 2 The R value was 0.80 in COVID-19 patients and 0.92 in healthy volunteers. 2 is R 2=1-sum(ΔZ-ΔZ ideal ) 2 / sum(ΔZ-Average(ΔZ)) 2 The lowest R 2 The values ​​were 0.77 in volunteers and 0.29 in patients.

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

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

[0138] Therefore, the distribution of the normalized impedance amplitude values ​​ΔZ depending on the respective normalized end-expiratory impedance values ​​EELI can be considered as a measure of lung heterogeneity.

[0139] Figure 7a shows the standard deviation of the reference patient, Strain EIT , and Fig. 7b shows the graph representation of the deviation value Strain of COVID-19 patients. EIT A graph representation of

[0140] Strain EIT is the impedance amplitude value ΔZ at the same end-tidal impedance value EELI and the fitting function ΔZ ideal The ratio was calculated as the ratio of

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

[0142] Physiologically, too high a value is more fatal to the patient. Alternatively, for example, log(ΔZ / ΔZ ideal ) can also be determined for every pixel. This consideration provides tails on either side of the ideal value, making it possible to set thresholds on either side of the ideal value. Too large ΔZ / ΔZ ideal Not only the value of ΔZ / ΔZ ideal The value of can also be taken into account.

[0143] 7a and 7b show the strain distributions at two equidistant intervals between each of the two strains. EIT , where the intervals have a length of 1 / 10.

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

[0145] For COVID-19 patients, the histogram shows a long tail, which means that many pixels have a large deviation value 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 small number of outlier pixels show non-physiologically large distortions, the impedance amplitude value ΔZ and the fitted function value ΔZ ideal The ratio of , where 97.5% of all pixels in healthy volunteers have a lower ratio, can be determined as the cutoff for healthy lung strain.

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

[0148] Furthermore, the number of pixels displaying such high deviation values ​​may be an indicator of total lung strain, and the sum of these pixels may then be considered an indicator of total strain.

[0149] In the patients in the study, an average of 21 out of 239 pixels in the lung regions showed strain values ​​greater than 2.11, whereas this was true for an average of only 6 out of 242 pixels in the lung regions of the healthy volunteers.

[0150] FIG. 8 shows a graphical representation in the form of a box plot for the percentage of pixels with a deviation value greater than the threshold for the group of COVID-19 patients in the study compared to the reference patients in the trial.

[0151] This difference in numbers between patients and volunteers is held to be statistically significant with a p-value calculated using a two-sample test of 0.028.

[0152] p is the probability that the skewness values ​​of patients and volunteers come from independent random samples from normal distributions with equal means and equal but unknown variances (null hypothesis). The test statistic t is calculated and p is the probability of observing the test statistic as extreme or more extreme than the value observed under the null hypothesis. Calculations are performed with statistical software using numerical methods. Here, the ttest2 function from Matlabs 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. 1. An apparatus (100) for determining a measure of lung homogeneity based on electrical impedance tomography (EIT) data, comprising: a data input unit (112) for receiving the 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); a calculation and control unit (114) connected to said data input unit; Equipped with The calculation and control unit (114) - determining, for each pixel (1) of at least one region of at least one lung (2), an impedance value over said observation period (ΔT); - determining an impedance amplitude value (ΔZ) for each pixel (1) of at least one lung (2); - determining an end-expiratory impedance value (EELI) for each pixel of at least one lung; - associating said impedance amplitude value (ΔZ) and said end-expiratory impedance value (EELI) separately for each pixel (1); - determining data based on said impedance amplitude value (ΔZ) and said associated end-expiratory impedance value (EELI); - generating a control signal based on said data; - A device, in particular adapted to compare said data with predetermined criteria.

2. The apparatus of claim 1 , wherein the calculation and control unit (114) is configured to compare the data to a predetermined standard based on a control group of patients.

3. The calculation and control unit (114) performs a function fitting of the impedance amplitude value (ΔZ) in dependence on the end-expiratory impedance value (EELI) for at least one lung to obtain a fitted function (ΔZ ｉｄｅａｌ (EELI)), especially the linear function ΔZ ｉｄｅａｌ 3. The apparatus of claim 1, configured to obtain:

4. The calculation and control unit calculates, for each pixel, a fit value (ΔZ ｉｄｅａｌ A deviation value (Strain) representing the deviation of the impedance amplitude value ΔZ from ＥＩＴ ), in particular, the impedance amplitude value (ΔZ) and the fitting value (ΔZ) at the same end-expiratory impedance value (EELI) ｉｄｅａｌ ) between the ratio (ΔZ / ΔZ ｉｄｅａｌ 4. The apparatus of claim 3, configured to determine

5. The calculation and control unit (114) calculates a strain value (Strain) at a certain interval for each interval of all pixels (1) of at least one lung (2). ＥＩＴ 5. The apparatus of claim 2, further configured to determine a histogram indicating the number of pixels having a .DELTA..times ...

6. The calculation and control unit (114) The deviation value (Strain ＥＩＴ ) is greater than a predetermined threshold, and / or the deviation value (Strain ＥＩＴ 6. Apparatus according to claim 2, wherein the first pixel is arranged to determine the amount of pixels (1) that lie outside a predetermined region.

7. an output unit (115), configured to use the control signal to provide or output an output signal for displaying a representation of the data; especially, For each pixel, the impedance amplitude value (ΔZ) and / or the adaptation amplitude value (ΔZ ｉｄｅａｌ a graph relating the end-expiratory impedance (EELI) to the end-expiratory impedance (EELI); - end-expiratory impedance value (EELI), impedance amplitude value (ΔZ) and / or fit value (ΔZ ｉｄｅａｌ ) local distribution, - A specific interval, in particular the impedance amplitude value (ΔZ) and the adaptation amplitude value (ΔZ ｉｄｅａｌ ) and the deviation value (Strain ＥＩＴ ) and a histogram of the number of pixels associated with - Standard deviation (Strain ＥＩＴ ) is greater than a predefined threshold and / or the deviation value (Strain ＥＩＴ ) showing the amount of pixels (1) that are outside a predetermined area; An apparatus according to any one of claims 1 to 6, configured to display

8. The calculation and control unit (114) normalizing the impedance amplitude values ​​(ΔZ) across all pixels (1) for the at least one lung (2) to obtain normalized amplitude values ​​(ΔZ) for all pixels of the at least one lung (2); and / or The apparatus according to any one of claims 1 to 7, configured to normalize the end-expiratory impedance value (EELI) across all pixels (1) for at least one lung (2) to obtain a normalized end-expiratory impedance value (EELI) for each pixel (1) of the at least one lung (2).

9. For example, based on electrical impedance tomography (EIT) data of a reference patient, reference data, such as impedance amplitude values ​​(ΔZ) and fitted values ​​(ΔZ ｉｄｅａｌ 9. The apparatus according to claim 1, configured to determine and / or display a reference distribution or a reference histogram of the signal intensity.

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

11. A method for determining the measure of homogeneity of the lungs based on Electrical Impedance Tomography (EIT) data, in particular using a device according to any one of claims 1 to 10, comprising: - providing EIT data from at least one region of at least one lung (2) of a living subject over an observation period (ΔT); - determining an impedance value over an observation period for each pixel (1) of at least one region of at least one lung (2); determining an impedance amplitude value (ΔZ) for each pixel (1) of at least one lung (2); - determining an end-expiratory impedance value (EELI) for each pixel (1) of at least one lung (2); - associating said impedance amplitude value (ΔZ) and said end-expiratory impedance value (EELI) individually for each pixel (1); - determining data based on said impedance amplitude value (ΔZ) and said associated end-expiratory impedance value (EELI); - generating a control signal based on said data; In particular, comparing said data with predetermined criteria; A method comprising:

12. 12. The method of claim 11, comprising the step of pre-determining a baseline based on a control group of patients, and preferably storing said baseline in a data storage.

13. A function fit of the impedance amplitude value (ΔZ) in dependence on the end-expiratory impedance value (EELI) for at least one lung is performed to obtain a fitted function (ΔZ ｉｄｅａｌ (EELI)), especially the linear function ΔZ ｉｄｅａｌ = αEELI + β, and obtain the fitted value (ΔZ ｉｄｅａｌ 13. The method of claim 11 or 12, comprising determining data representative of a distribution of impedance amplitude values ​​(ΔZ) versus impedance amplitude (ΔZ).

14. For each pixel, the fit value (ΔZ ｉｄｅａｌ A deviation value (Strain) representing the deviation of the impedance amplitude value (ΔZ) from ＥＩＴ ), in particular, the impedance amplitude value (ΔZ) at the same end-expiratory impedance value (EELI) and the fitting value (ΔZ ｉｄｅａｌ ) between the ratio (ΔZ / ΔZ ｉｄｅａｌ 14. The method of claim 13, comprising determining

15. The deviation value (Strain ＥＩＴ ) is greater than a predetermined threshold, and / or the deviation value (Strain ＥＩＴ 15. The method of claim 14, further comprising the step of: determining the amount of pixels (1) that lie outside a predetermined region.

16. A method according to any one of claims 11 to 15, comprising the step of providing or outputting an output signal for displaying a representation of said data, in particular the step of displaying said output signal.

17. The method according to any one of claims 11 to 16, comprising a step of determining data for each of the lungs (2) and / or a step of determining reference data.

18. A computer program product loadable directly into and / or for execution on a computer, in particular into a computing and control unit of an apparatus and / or an EIT apparatus according to any one of claims 1 to 10, said computer program product comprising software code portions for carrying out the steps of the method according to any one of claims 11 to 17.

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