阅读说明：本技术 一种对尿液电导率不敏感的动态膀胱体积测量方法 (Dynamic bladder volume measurement method insensitive to urine conductivity ) 是由 孙江涛 梁小凤 徐立军 谢跃东 田文斌 于 2019-12-12 设计创作，主要内容包括：本发明公开了一种对尿液电导率不敏感的动态膀胱体积测量方法,涉及电阻抗层析成像技术在膀胱尿量监测领域。首先测量前通过MATLAB调用EIDORS函数对人体腹部进行有限元仿真模拟；在膀胱底面往上距离h的位置,在人体周身表面设置n个电极,构建重建图像；然后利用重建图像中膀胱区域的各像素点的坐标和像素值,提取边缘效应特征值；建立边缘效应特征值与膀胱体积的拟合方程；对拟合方程中的待定拟合系数进行求解；最后针对实际患者,以患者的膀胱排空时为参考帧,使用时间差分法,对当下时刻相对参考帧进行重建图像；利用重建图像提取患者的边缘效应特征值,带入拟合方程中映射得到该患者的膀胱体积。本发明减小了尿液电导率带来的影响。(The invention discloses a dynamic bladder volume measurement method insensitive to urine conductivity, and relates to the field of bladder urine volume monitoring by an electrical impedance tomography technology. Firstly, calling an EIDORS function through MATLAB to carry out finite element simulation on human abdomen before measurement; arranging n electrodes on the surface of the whole body of a human body at a position which is a distance h from the bottom surface of the bladder upwards to construct a reconstructed image; then extracting edge effect characteristic values by utilizing the coordinates and pixel values of all pixel points of the bladder area in the reconstructed image; establishing a fitting equation of the edge effect characteristic value and the bladder volume; solving the undetermined fitting coefficient in the fitting equation; finally, aiming at the actual patient, the voided bladder of the patient is taken as a reference frame, and a time difference method is used for reconstructing an image relative to the reference frame at the next moment; and extracting the edge effect characteristic value of the patient by using the reconstructed image, and substituting the edge effect characteristic value into a fitting equation to map to obtain the bladder volume of the patient. The invention reduces the influence caused by the conductivity of urine.)

1. A dynamic bladder volume measurement method insensitive to urine conductivity is characterized by comprising the following specific steps:

firstly, calling an EIDORS function through MATLAB to perform finite element simulation on human abdomen before measurement;

step two, arranging n electrodes on the surface of the whole body of a human body at a position which is at a distance h from the bottom surface of the bladder upwards to construct a reconstructed image;

the ratio of the arc length covered by the electrode to the circumference of the human body is α;

extracting edge effect characteristic values by using the coordinates and pixel values of all pixel points of the bladder area in the reconstructed image;

the edge effect characteristic value g is calculated by the formula:

n represents the number of the bladder area pixel points in the reconstructed image; p is a radical of_iIs the pixel value of the i-th pixel point, y_iThe vertical coordinate of the ith pixel point which takes the upper left corner as the origin in the reconstructed image is determined;

establishing a fitting equation of the edge effect characteristic value and the bladder volume;

V_{bladder of urinary bladder}＝a·g^-4+b

V_{Bladder of urinary bladder}The bladder volume is defined, and a and b are respectively undetermined fitting coefficients;

solving undetermined fitting coefficients in the fitting equation;

the specific solving process is as follows:

firstly, selecting two conditions of empty bladder and full bladder, respectively imaging the bladder for multiple times, and calculating edge effect characteristic values of the two times;

the bladder was empty as bladder volume 0ml immediately after urination;

bladder fullness was determined by two conditions: determination by bladder percussive method and determination by measuring device;

then, according to the bladder volumes under the two conditions of empty bladder and full bladder and the edge effect characteristic values of the two times, calculating undetermined fitting coefficients a and b;

step six, aiming at an actual patient, taking the voided bladder of the patient as a reference frame, and reconstructing an image relative to the reference frame at the current moment by using a time difference method;

and seventhly, extracting the edge effect characteristic value of the patient by using the reconstructed image, and substituting the edge effect characteristic value into a fitting equation to map to obtain the bladder volume of the patient.

2. The method of claim 1, wherein the simulation of step one comprises a body contour, a bladder shape; as well as the electrical conductivity of the bladder and the electrical conductivity of the surrounding tissues of the abdomen except the bladder.

3. The method for dynamic bladder volume measurement insensitive to urine conductivity as claimed in claim 1, wherein said second step is embodied as: in each measurement, two adjacent electrodes in the n electrodes are used as a pair, excitation and measurement are carried out in sequence, and the distribution of the internal electrical impedance of the human body is inverted through a measured voltage signal and a finite element model, namely an image is reconstructed; different pixel sizes in the image represent different conductivity sizes.

4. The method for dynamic bladder volume measurement insensitive to urine conductivity as claimed in claim 1, wherein the fringe effect in step three is generated because the electric field lines generated by exciting the measuring electrodes not only exist in the electrode plane but also spread toward the third dimension, which causes the position of the object in the reconstructed image to shift when the EIT sensor images the object outside the electrode plane.

Technical Field

The invention relates to application of an Electrical Impedance Tomography (EIT) technology in the field of bladder urine volume monitoring, in particular to a dynamic bladder volume measurement method insensitive to urine conductivity.

Background

Currently, common bladder volume measurement methods include Computed Tomography (CT) and ultrasound. CT has higher precision, but the application of CT in daily bladder volume detection is limited due to higher radiation, complex operation, high price and the like. Ultrasound contrast computed tomography is relatively simple to operate, harmless and amenable to multiple examinations, but its use is still limited to hospitals and professionals. In addition, both methods are static measurements and cannot monitor bladder volume in real time.

Compared with the traditional bladder volume measurement method, the Electrical Impedance Tomography (EIT) method has the advantages of real-time performance, non-invasion, safety, no radiation and low price, and can dynamically measure the bladder volume.

The prior EIT bladder volume measuring method commonly uses a global impedance method, an equivalent circle diameter method, a neural network method and a singular value difference method; as in document 1: t.schlebusch, s.nienke, s.leonhardt, and m.walter, 'estimation of bladder volume based on electrical impedance tomography', physiological measurements; vol 35, No. 9, pp.1813-1823,2014, month 9.

However, the measurement of the bladder volume by the global impedance method, the neural network method and the singular value difference method is easily influenced by the change of the urine conductivity; the equivalent circle diameter method is not sensitive to the change of the urine conductivity, but the measurement precision is poor. The conductivity of human urine is influenced by factors such as living habits, dietary structures and the like, and different individuals and even different time of the same individual are different; for the above reasons, the improvement of the accuracy of the EIT method for measuring the volume of the bladder is limited.

Disclosure of Invention

In view of the above problems in the art, the present invention provides a dynamic bladder volume measurement method insensitive to urine conductivity, which can improve the linearity of bladder volume measurement, and at the same time, compared with the conventional method, the method reduces the influence of the change of urine conductivity on the bladder volume measurement and ensures the measurement accuracy.

The method comprises the following specific steps:

firstly, calling an EIDORS function through MATLAB to perform finite element simulation on human abdomen before measurement;

the simulation comprises a human body contour and a bladder shape; as well as the electrical conductivity of the bladder and the electrical conductivity of the surrounding tissues of the abdomen except the bladder.

Step two, arranging n electrodes on the surface of the whole body of a human body at a position which is at a distance h from the bottom surface of the bladder upwards to construct a reconstructed image;

the ratio of the arc length covered by the electrode to the circumference of the human body is α;

in each measurement, two adjacent electrodes in the n electrodes are used as a pair, excitation and measurement are carried out in sequence, and the distribution of the internal electrical impedance of the human body is inverted through a measured voltage signal and a finite element model, namely an image is reconstructed; different pixel sizes in the image represent different conductivity sizes.

Extracting edge effect characteristic values by using the coordinates and pixel values of all pixel points of the bladder area in the reconstructed image;

the edge effect arises because the electric field lines generated by exciting the measuring electrodes not only exist in the plane of the electrodes, but also diffuse towards the third dimension, which results in a shift in the position of the object in the reconstructed image when the EIT sensor images an object outside the plane of the electrodes.

The edge effect characteristic value g is calculated by the formula:

n represents the number of the bladder area pixel points in the reconstructed image; p is a radical of_iIs the pixel value of the i-th pixel point, y_iThe vertical coordinate of the ith pixel point which takes the upper left corner as the origin in the reconstructed image is shown.

Establishing a fitting equation of the edge effect characteristic value and the bladder volume;

V_{bladder of urinary bladder}＝a·g^-4+b

V_{Bladder of urinary bladder}Is bladder volume, a andand b are respectively undetermined fitting coefficients.

Solving undetermined fitting coefficients in the fitting equation;

the specific solving process is as follows:

firstly, selecting two conditions of empty bladder and full bladder, respectively imaging the bladder for multiple times, and calculating edge effect characteristic values of the two times;

the bladder was empty as bladder volume 0ml immediately after urination.

Bladder fullness was determined by two conditions: determined by bladder percussive method and determined by measuring device.

And then, calculating the undetermined fitting coefficients a and b according to the bladder volumes under the conditions that the bladder is empty and the bladder is full and the edge effect characteristic values of the two times.

Step six, aiming at an actual patient, taking the voided bladder of the patient as a reference frame, and reconstructing an image relative to the reference frame at the current moment by using a time difference method;

and seventhly, extracting the edge effect characteristic value of the patient by using the reconstructed image, and substituting the edge effect characteristic value into a fitting equation to map to obtain the bladder volume of the patient.

The invention has the advantages that:

compared with a global impedance method, the dynamic bladder volume measuring method insensitive to urine conductivity improves linearity, and an edge effect is based on image characteristics rather than pixel points, so that influence caused by the urine conductivity is reduced.

Drawings

FIG. 1 is a flow chart of a method of dynamic bladder volume measurement that is insensitive to urine conductivity in accordance with the present invention;

FIG. 2 is a schematic diagram of a finite element model of a human abdomen according to the present invention;

FIG. 3 is a schematic view of a four terminal system of the present invention;

FIG. 4 is a schematic diagram of the edge effect principle of the present invention;

FIG. 5 is a graph showing variation of edge effect characteristic values under different parameters according to the present invention;

FIG. 6 is a graph of global impedance variation under different parameters of the present invention.

Detailed Description

The following describes embodiments of the present invention in detail and clearly with reference to the examples and the accompanying drawings.

The traditional index of global impedance measures the volume of the bladder, essentially imaging the trunk, then summing all pixel points of a two-dimensional image, and representing the increase of the volume of the bladder by the increase of the global impedance. This volumetric measurement method has two potential problems: 1) the summation of all pixel points of the two-dimensional image means that the image formed by the non-bladder area is also calculated, such as intestines and stomach, pelvis and the like; 2) when the urine conductivity changes, the change of the pixel point value of the image not only reflects the change of the bladder volume, but also reflects the change of the urine conductivity. In fact, the conductivity of urine is not nearly the same between individuals or at different times in the same individual, subject to factors such as diet. These two errors in the measurement principle are important obstacles to the impossibility of applying EIT bladder volume monitoring to clinical measurement.

The bladder volume measurement method applying the EIT edge effect can well solve the two problems: 1) the edge effect characteristic value only needs to be calculated in the bladder area, so that the influence of image change caused by organ change at other positions on bladder volume measurement is avoided; 2) when the conductivity of the urine changes, the reason for causing the edge effect is only related to the distance of the object from the plane of the sensor and is not related to the conductivity of the object, namely, the change of the characteristic value of the edge effect is only related to the change of the shape of the bladder and is not related to the conductivity of the urine, so that the influence of the conductivity of the urine is eliminated. Furthermore, bladder volume measurements using the edge effect can improve the linearity of the results to some extent.

The invention relates to a dynamic bladder volume measuring method insensitive to urine conductivity, which comprises the following steps as shown in figure 1:

firstly, simulating the human abdomen by a finite element model before measurement;

the simulation comprises a human body contour and a bladder shape; as well as the electrical conductivity of the bladder and the electrical conductivity of the surrounding tissues of the abdomen except the bladder. And (3) calling a function in the EIDORS through MATLAB to perform finite element simulation, wherein a finite element model is shown in figure 2, the outline of the abdomen in the figure is simulated by the human abdomen, and the gray part is the bladder.

Step two, arranging n electrodes on the surface of the whole body of a human body at a position which is at a distance h from the bottom surface of the bladder upwards to construct a reconstructed image;

the ratio of the arc length covered by the electrodes to the circumference of the human body is α, and the number n of the electrodes is 16 in the embodiment.

During each measurement, two adjacent electrodes in the 16 electrodes are used as a pair, excitation and measurement are carried out in sequence, and inversion is carried out on the distribution of the internal electrical impedance of the human body through a measured voltage signal and a finite element model, namely an image is reconstructed; different pixel sizes in the image represent different conductivity sizes.

Extracting edge effect characteristic values by using the coordinates and pixel values of all pixel points of the bladder area in the reconstructed image;

the edge effect arises because the electric field lines generated by exciting the measuring electrodes not only exist in the plane of the electrodes, but also diffuse towards the third dimension, which results in a shift in the position of the object in the reconstructed image when the EIT sensor images an object outside the plane of the electrodes.

Describing the edge effect, for the four-terminal system, the general position of the object in the reconstructed image can be calculated by the following formula:

the simplified model is shown in FIG. 3, where η denotes the convergence ratio, E denotes the potential, q1 denotes the excitation electrode pair, q2 denotes the measurement electrode pair, and ρ₁Represents the distance of object p to excitation electrode pair q 1; rho₂Represents the distance of object p to measuring electrode pair q 2; p' denotes the position of the object p in the reconstructed image, d₁The distance from position p' to the excitation electrode pair q1 is indicated, d the distance between the excitation electrode pair q1 to the measurement electrode pair q2 is indicated, and z the distance of the object to the electrode plane.

By this formula, a relationship between the object-to-electrode plane distance and the object position in the reconstructed image can be established. As shown in fig. 4, the reconstructed images of the beads at different distances from the electrode plane are shown, and it can be clearly seen from the figure that the farther an object is from the electrode plane, the closer the object position in the reconstructed image is to the center.

The bladder extends up the abdominal wall during the increase in volume with little change in the bottom position and the geometric center of the entire bladder changes upward during the increase in volume of the bladder. The reconstructed image can be processed by utilizing the edge effect, the edge effect characteristic value is extracted, and the position change of the geometric center of the bladder is reflected, and the position change is monotonically related to the bladder volume, so that the bladder volume measurement is carried out.

The movement of the position of the object in the reconstructed image is characterized by calculating the center of gravity. For the application of bladder volume measurement, to avoid the influence of non-bladder region imaging on bladder volume measurement, only the center of gravity of the bladder region is calculated. The edge effect characteristic value g is calculated by the formula:

n represents the number of the bladder area pixel points in the reconstructed image; p is a radical of_iIs the pixel value of the i-th pixel point, y_iThe vertical coordinate of the ith pixel point which takes the upper left corner as the origin in the reconstructed image is shown.

Establishing a fitting equation of the edge effect characteristic value and the bladder volume when the edge effect characteristic value changes along with the bladder volume;

a series of different settings which are in accordance with the actual condition of a human body are carried out on two parameters of the size of the bladder and the conductivity of the bladder in the finite element model, wherein the size of the bladder is between 40ml and 500ml, and the conductivity of the bladder is between 0.4S/m and 3.4S/m. In this example, the volume change was 40ml to 490ml with 30ml intervals. Urine conductivity is set to three trends of increase, constant and decrease respectively. Wherein the conductivity of the urine is increased to change from 0.4S/m to 3.4S/m at an interval of 0.2S/m; the conductivity of the urine does not become 2S/m; when the conductivity of the urine is reduced, the conductivity of the urine is changed from 3.4S/m to 0.4S/m at an interval of 0.2S/m; there are 16 x 3 settings. And simultaneously, carrying out image reconstruction under corresponding settings, and calculating edge effect characteristic values.

In each volume, the edge effect characteristic value under different conductivity is matched, and the edge effect characteristic value and the bladder volume are fitted. Trying a plurality of fitting modes such as exponential, logarithm, polynomial and the like, and comparing the fitting goodness R²The best fit curve is found. It is noted that the fitting equation should contain less than two undetermined coefficients. The results show that the negative fourth power of the edge effect eigenvalues has the highest goodness of fit to the bladder volume, as shown in figure 5. The fitting equation is:

V_{bladder of urinary bladder}＝a·g^-4+b

V_{Bladder of urinary bladder}For bladder volume, a and b are the coefficients to be fitted, respectively.

Solving undetermined fitting coefficients in the fitting equation through the edge effect characteristic value of the known volume;

the specific solving process is as follows:

firstly, selecting two conditions of empty bladder and full bladder, respectively imaging the bladder for multiple times, and calculating edge effect characteristic values of the two times;

the edge effect characteristic values for two known volumes are typically selected for bladder empty and bladder full. The bladder was empty as bladder volume 0ml immediately after urination. There are two cases of bladder fullness, the first determines whether the bladder is full by a bladder plexus method, assuming that the bladder is full with a volume of 400 ml; the second is by a more accurate bladder volume measuring device, such as an ultrasound device. Under the two states, the bladder is imaged for multiple times and the edge effect characteristic value is calculated, and the two data points are respectively (g)₁,V₁)，(g₂,V₂)。。

And then, calculating undetermined fitting coefficients a and b according to the bladder volumes under the two conditions of empty bladder and full bladder and the two edge effect characteristic values to obtain a fitting equation between the personalized edge effect characteristic value and the bladder volume. .

And sixthly, aiming at the actual patient, continuously and dynamically monitoring the bladder by using a time difference method by using differential imaging and taking the measured value of the voided bladder of the patient as a reference value to obtain the measured value, reconstructing an image and calculating an edge effect characteristic value.

And seventhly, extracting the edge effect characteristic value of the patient by using the reconstructed image, and substituting the edge effect characteristic value into a fitting equation to map to obtain the bladder volume of the patient.

To compare to the conventional global impedance approach, the global impedance is calculated at each parameter setting, as shown in fig. 6. Comparing the two graphs can find that the edge effect characteristic values under different urine conductivities in the edge effect method have better consistency, and the global impedance method is easily influenced by the change of the urine conductivity when the bladder volume is larger and has poorer linearity.