A method for suppressing body motion interference in electrical impedance tomography based on signal structure characteristics

Abstract

The present invention provides a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics, which includes (1) acquiring electrical impedance imaging data through an electrical impedance imaging system; (2) determining whether there is body motion interference data in the data; (3) performing matrix decomposition on the data with body motion interference; and (4) performing electrical impedance imaging. The present invention effectively suppresses body motion interference in electrical impedance imaging, solves the problem that the electrical impedance image is completely distorted due to body motion interference and the lung ventilation imaging cannot be clearly seen. After the processing of the present invention, a rhythmic lung ventilation sequence diagram can be clearly observed.

Description

Technical Field

[0001] The invention belongs to the technical field of electrical impedance imaging detection, and in particular relates to a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics. Background Art

[0002] Chest impedance imaging is an imaging method used to observe the state of lung ventilation during human breathing. It places 16 or 32 electrodes around the human chest cavity, then collects the corresponding voltage while stimulating a safe current, and finally transmits the above data to a computer through an image reconstruction algorithm to obtain an image reflecting the distribution of impedance changes inside the human chest cavity. Since the air inhaled by a person has high impedance characteristics, impedance imaging can reflect the changes in the process of lung inspiration and exhalation in real time, and has important clinical application value.

[0003] However, in chest impedance imaging, the impedance signal is often affected by body motion interference, especially for patients who need long-term monitoring. The medical staff's intervention treatment, turning care, and the patient's free activities or restlessness will produce large body motion noise on the detection signal, which will eventually cause artifacts in the impedance image, affecting the continuity of monitoring and the diagnosis of the disease. Therefore, how to effectively suppress body motion interference in impedance imaging is an important research issue in this field. From the previous analysis (G Zhang, et al. Biomed Eng Online, 2019), the characteristic of the impedance body motion signal is a pulse signal composed of multiple continuous points, so simple frequency domain filtering or median filtering is difficult to eliminate this interference. Summary of the invention

[0004] The present invention provides a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics, the improvement of which is that the method comprises

[0005] (1) obtaining electrical impedance imaging data through an electrical impedance imaging system;

[0006] (2) Determine whether there is body motion interference data in the data;

[0007] (3) Perform matrix decomposition on data with body motion interference;

[0008] (4) Perform electrical impedance imaging.

[0009] Furthermore, the step (1) includes collecting the original measurement data matrix X of the electrical impedance imaging by the electrical impedance imaging system, assuming is the raw multi-channel data set for electrical impedance imaging,

[0010] Where m represents the number of channels of the raw data of electrical impedance imaging, and n represents the number of data points.

[0011] Furthermore, the step (2) includes analyzing the matrix X row by row:

[0012] For the i-th row of data, solve for the median α i and variance δ i , α i represents the median of the i-th row of data, δ i Represents the variance of the median of the i-th row of data;

[0013] Traverse the data x in row i ij , with x ij represents the jth element in the i-th row of data, j is a positive integer ranging from 1 to n. ij >α i +3δ i , then determine that x ij It is the body motion interference data and marks the data point positions.

[0014] Furthermore, the step (3) includes performing matrix decomposition on the matrix X with body motion interference, decomposing X into a low-rank matrix L and a sparse matrix S, that is, X = L + S; the decomposition process is a process of solving the constrained minimization problem, that is, solving min L,S ||L|| * +λS||1, the constraint is X=L+S; where || || * represents the nuclear norm, || ||1 represents the 1-norm, and λ is the regularization parameter.

[0015] Furthermore, the step (3) comprises

[0016] (3.1) Initialize the parameters, set L (0) and S (0) is the initial value of the low-rank matrix L and the sparse matrix S, and L (0) and S (0) Defined as a zero matrix, Y (0) is the initial value of the Lagrange multiplier matrix Y, and defines Y (0) is a zero matrix and the regularization parameter is The step size parameter μ = 1 / ||X2, ||||2 represents the matrix 2 norm, m represents the number of channels of the raw data of electrical impedance imaging, n represents the number of data points, the convergence threshold is ε = 0.001, and the maximum number of iterations K = 1000;

[0017] (3.2) Iterate and solve, for each time k = 1, 2, 3K, K;

[0018] (3.3) Obtain L (k) and S (k) result.

[0019] Furthermore, it is characterized in that the step (3.2) comprises

[0020] (3.2.1) for L (k) To update:

[0021] Let M be the intermediate matrix, and calculate the matrix M = XS (k-1) +μ -1 Y (k-1) , where S (k-1) represents the S matrix updated for the k-1th time, Y (k-1) Represents the Y matrix updated for the k-1th time;

[0022] Perform singular value decomposition on the matrix M and obtain M = UΣV T , where U is the left singular vector, V is the right singular vector, and Σ is the singular value matrix;

[0023] Perform threshold processing on each singular value of the singular value matrix Σ, and set the original singular value to σ i , the new singular value is σ′ i , the new singular value is selected as σ i -μ -1 A value larger than 0, namely σ′ i =max(σ i -μ -1 ,0);

[0024] Reconstruction L (k) :L (k) =UΣ'V T , where Σ' is the threshold value after processing, which is represented by σ′ i The singular value matrix constructed on the diagonal;

[0025] (3.2.2) For S (k) To update:

[0026] Let N be the intermediate matrix, and calculate the matrix N = XL (k) +μ -1 Y (k-1) ;

[0027] The element n of the matrix N ij Threshold processing is performed to obtain S (k) , S (k) Each element in is Where sign() represents the sign function;

[0028] (3.2.3) for Y (k) To update: Y (k) =Y (k-1) +μ(XL (k) -S (k) );

[0029] (3.2.4) Check the convergence condition: If ||XL (k) -S (k) || F ≤ε||X|| F Or the maximum number of iterations K = 1000 is reached. Among them || || F Represents the F-norm.

[0030] Furthermore, the step (4) comprises

[0031] Decomposition result, L (k) To remove the interference of body motion, S (k) is the body motion interference data; for L (k) In the data, the data segment with body motion interference is subjected to electrical impedance imaging to obtain the result without body motion interference.

[0032] Beneficial effects:

[0033] The present invention effectively suppresses body motion interference in electrical impedance imaging, solves the problem that the electrical impedance image is completely distorted due to body motion interference and the lung ventilation imaging cannot be clearly seen. After being processed by the present invention, a rhythmic lung ventilation sequence diagram can be clearly observed.

[0034] The present invention greatly improves the clarity and accuracy of electrical impedance images by effectively suppressing body motion interference in electrical impedance imaging. Traditional electrical impedance imaging technology often suffers from image distortion when facing body motion interference, and cannot accurately reflect the lung ventilation situation. The present invention can obtain clear and rhythmic lung ventilation sequence diagrams even under body motion interference. This is of great value for monitoring and evaluating the patient's lung function status, especially in clinical scenarios that require long-term monitoring of respiratory function, such as intensive care or postoperative recovery. Therefore, the present invention not only improves the practicality of electrical impedance imaging technology, but also provides clinicians with a more reliable diagnostic tool, which helps to improve the accuracy and efficiency of patient treatment.

[0035] It should be understood that the above general description and the following specific embodiments are only exemplary and illustrative and do not limit the scope of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to the present invention;

[0037] Figure 2 A diagram of an electrical impedance multi-channel signal with body motion interference in a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to the present invention;

[0038] Figure 3A low-rank matrix L in a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to the present invention;

[0039] Figure 4 A sparse matrix S graph in a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to the present invention;

[0040] Figure 5 It is a schematic diagram showing the influence of body motion interference on the pulmonary ventilation electrical impedance image in a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to the present invention;

[0041] Figure 6 A pulmonary ventilation signal recovery diagram in a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to the present invention;

[0042] It should be understood that the drawings are not necessarily drawn to scale, presenting somewhat simplified representations of various features illustrating the basic principles of the present disclosure. The specific design features of the present invention as disclosed herein, including, for example, specific dimensions, orientations, locations, and shapes will be determined in part by the particular intended application and use environment.

[0043] In the drawings, reference numbers refer to the same or equivalent parts of the present invention throughout the several figures of the drawing. DETAILED DESCRIPTION

[0044] Reference will now be made in detail to various embodiments of the present invention, examples of which are illustrated in the accompanying drawings and described below. Although the present invention will be described in conjunction with exemplary embodiments of the present invention, it should be understood that this specification is not intended to limit the present invention to those exemplary embodiments. On the other hand, the present invention is intended to cover not only the exemplary embodiments of the present invention, but also various alternatives, modifiers, equivalents and other embodiments, which may be included in the spirit and scope of the present invention as defined by the appended claims.

[0045] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. The specific structures and functions described in the exemplary embodiments of the present invention are for illustrative purposes only. Embodiments according to the concepts of the present invention can be implemented in various forms, and it should be understood that they should not be interpreted as being limited to the exemplary embodiments described in the exemplary embodiments, but include all modifications, equivalents or substitutes included in the spirit and scope of the present invention.

[0046] Throughout the specification, the professional terms used herein are only for describing various exemplary embodiments and are not intended to be limiting. It will be further understood that the terms "include", "comprising", "having", etc., when used in the exemplary embodiments, specifically refer to the presence of the stated parts, steps, operations or elements, but do not exclude the presence or addition of one or more other parts, steps, operations or elements.

[0047] like Figure 1 As shown, the present invention proposes a method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics, which includes

[0048] Step 1: Acquire electrical impedance imaging data through an electrical impedance imaging system;

[0049] like Figure 2 As shown in FIG. 1 , the body motion interference generated by a human body during electrical impedance imaging in the embodiment is shown. Through the electrical impedance imaging system (EIT system), the original measurement data matrix X of EIT is obtained. Assume is the original multi-channel data set of electrical impedance imaging, where the elements of X are all real numbers, m represents the number of rows of matrix X, which actually represents the number of channels of the original data of electrical impedance imaging, in this case 192 channels (generally 196 or 208); n represents the number of columns of matrix X, which actually represents the number of data points collected by EIT data, in this case 8250 data points. Each point differs by 50ms.

[0050] Step 2: Determine whether there is body motion interference data in the data;

[0051] Analyze the matrix X row by row: For the i-th row of data (a total of m rows), first, solve the median α i and variance δ i ; For the i-th row of data, α i represents the median of the i-th row of data, δ i Represents the variance of the i-th row of data.

[0052] Next, traverse the data x in row i ij , where x ij represents the jth element in the i-th row of data, where j is a positive integer ranging from 1 to n. If x ij >α i +3δ i , then determine that x ij is the body motion interference data, and the data point positions are marked. After analyzing the entire matrix X, if there is body motion interference data, proceed to step three.

[0053] Step 3: Perform matrix decomposition on the data with body motion interference;

[0054] For the matrix X with body motion interference, matrix decomposition is performed on X to decompose it into a low-rank matrix L and a sparse matrix S. The data of each row of the data matrix X has a similar shape and has a strong correlation. Therefore, it can be assumed that the matrix X is composed of a low-rank matrix Although the data points of body motion interference have large signal amplitude, the number of points is small, so the data points of body motion interference can be assumed to be a sparse matrix From mathematical modeling, we know that matrices L and S together form matrix X, that is, X = L + S. Therefore, the goal of removing body motion interference is to solve L and S from the X observation matrix.

[0055] The decomposition process is the process of solving the constraint minimization problem, that is, solving the min L,S ||L|| * +λ||S||1, the constraint is X=L+S.

[0056]

[0057] Among them, || || * represents the nuclear norm, || ||1 represents the 1 norm, and λ is the regularization parameter. For this optimization problem, the alternating direction multiplication method can be used for iterative solution, and finally the matrix L, S is obtained. Figure 3 As shown in, the matrix L represents the low-rank components of the observation matrix X. As shown in,Fig.,4, the matrix S represents the sparse volume motion interference of the observation matrix X.

[0058] The specific sub-steps are as follows:

[0059] Sub-step 1: Initialize parameters: L (0) and S (0) Set as the initial value of the low-rank matrix L and the sparse matrix S, and set L (0) and S (0) Defined as a zero matrix; let Y (0) is the initial value of the Lagrange multiplier matrix Y, and defines Y (0) is a zero matrix; set the regularization parameter The step size parameter μ = 1 / ||X||2||||2 represents the matrix 2-norm, m represents the number of channels of the electrical impedance imaging raw data, n represents the number of data points, the convergence threshold is ε = 0.001, and the maximum number of iterations K = 1000.

[0060] Sub-step 2: Iterative solution. For each time k = 1, 2, 3K, K, perform the following steps:

[0061] For L (k) To update:

[0062] Let M be the intermediate matrix, and calculate the matrix M = XS (k-1) +μ -1 Y(k-1) , where S (k-1) represents the S matrix updated for the k-1th time, Y (k-1) Represents the Y matrix updated for the k-1th time;

[0063] Perform singular value decomposition on the matrix M and obtain M = UΣV T , where U is the left singular vector, V is the right singular vector, and Σ is the singular value matrix;

[0064] Perform threshold processing on each singular value of the singular value matrix Σ, and set the original singular value to σ i , the new singular value is σ′ i , the new singular value is selected as σ i -μ -1 A value larger than 0, namely σ′ i =max(σ i -μ -1 ,0);

[0065] Reconstruction L (k) :L (k) =UΣ'V T , where Σ' is the threshold value after processing, which is represented by σ′ i The singular value matrix constructed on the diagonal;

[0066] For S (k) To update:

[0067] Let N be the intermediate matrix, and calculate the matrix N = XL (k) +μ -1 Y (k-1) ;

[0068] The element n of the matrix N ij Threshold processing is performed to obtain S (k) , S (k) Each element in is Where sign() represents the sign function;

[0069] Y (k) To update: Y (k) =Y (k-1) +μ(XL (k) -S (k) );

[0070] Check convergence conditions: If ||XL (k) -S (k) || F ≤ε||X|| F Or the maximum number of iterations K = 1000 is reached. Among them |||| F represents the F norm, then stop sub-step 2.

[0071] Sub-step 3: Get the result. The final L (k) and S (k) It is the desired result.

[0072] Step 4: Perform electrical impedance imaging.

[0073] In the above decomposition results, L (k) represents the EIT measurement data after removing body motion interference, S (k) Represents body motion interference data. (k) In the data, the data segments that originally had body motion interference were subjected to EIT imaging, and the results without body motion interference could be obtained.

[0074] like Figure 5 As shown in FIG. 1 , the electrical impedance image is completely distorted due to body motion interference, and the lung ventilation imaging cannot be clearly seen. After analyzing the changes in the electrical impedance imaging before and after the interference is removed and processing, as shown in FIG. 1 , the electrical impedance image is completely distorted due to body motion interference, and the lung ventilation imaging cannot be clearly seen. Figure 6 As shown, the electrical impedance image (using matrix L) at the same time is displayed, and the rhythmic pulmonary ventilation sequence diagram can be clearly observed.

[0075] The above-mentioned embodiments of the present application can be implemented in various hardware, software coding or a combination of the two. For example, the embodiments of the present application can also represent the program code for executing the above-mentioned method in a data signal processor. The present application can also relate to the various functions performed by a computer processor, a digital signal processor, a microprocessor or a field programmable gate array. The above-mentioned processor can be configured according to the present application to perform specific tasks, which are completed by executing machine-readable software code or firmware code that defines the specific method disclosed by the present application. The software code or firmware code development can represent different programming languages ​​and different formats or forms. Different target platform compiled software codes can also be represented. However, the different code styles, types and languages ​​of the software code for performing tasks according to the present application and other types of configuration codes do not depart from the spirit and scope of the present application.

[0076] The foregoing description of specific exemplary embodiments of the present invention has been presented for the purpose of illustration and description. It is not intended to exclude or limit the present invention to the precise form disclosed, and it is obvious that many modifications and changes are feasible in view of the above teachings. Exemplary embodiments are selected and described to explain certain principles of the present invention and their practical application, so that other technicians in the field can make or use various exemplary embodiments of the present invention, and various alternatives and modifications thereof. It is intended that the scope of the present invention will be defined by the claims attached hereto and their equivalents.

[0077] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics, characterized in that: The method comprises: Step (1) acquiring electrical impedance imaging data through an electrical impedance imaging system; Step (2) determining whether there is body motion interference data in the data; Step (3) performing matrix decomposition on the data with body motion interference; Step (4) performing electrical impedance imaging; The step (3) includes performing matrix decomposition on the matrix X with body motion interference, decomposing X into a low-rank matrix L and a sparse matrix S, that is, X=L+S; the decomposition process is a process of solving the constraint minimization problem, that is, solving the min L,S ||L|| * +λ||S||1, the constraint is X=L+S; where |||| * represents the nuclear norm, ||||1 represents the 1-norm, and λ is the regularization parameter.

2. The method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to claim 1, characterized in that: The step (1) comprises collecting the original measurement data matrix X of the electrical impedance imaging by the electrical impedance imaging system, assuming It is the raw multi-channel data set for electrical impedance imaging; Where m represents the number of channels of the raw data of electrical impedance imaging, and n represents the number of data points.

3. The method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to claim 1, characterized in that: The step (2) includes analyzing the matrix X row by row: For the i-th row of data, solve for the median α i and variance δ i , α i represents the median of the i-th row of data, δ i Represents the variance of the i-th row of data; Traverse the data x in row i ij , with x ij represents the jth element in the i-th row of data, j is a positive integer ranging from 1 to n. ij >α i +3δ i , then determine that x ij It is the body motion interference data and marks the data point positions.

4. The method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to claim 1, characterized in that: The step (3) comprises: (3.1) Initialize the parameters, set L (0) and S (0) is the initial value of the low-rank matrix L and the sparse matrix S, and L (0) and S (0) Defined as a zero matrix, Y (0) is the initial value of the Lagrange multiplier matrix Y, and defines Y (0) is a zero matrix and the regularization parameter is The step size parameter μ = 1 / ||X||2, ||||2 represents the matrix 2 norm, m represents the number of channels of the electrical impedance imaging raw data, n represents the number of data points, the convergence threshold is ε = 0.001, and the maximum number of iterations K = 1000; (3.2) Iterate and solve, for each time k = 1, 2, 3..., K; (3.3) Obtain L (k) and S (k) result.

5. The method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to claim 4, characterized in that: The step (3.2) comprises: (3.2.1) for L (k) To update: Let M be the intermediate matrix, and calculate the matrix M = XS (k-1) +μ -1 Y (k-1) , where S (k-1) represents the S matrix updated for the k-1th time, Y (k-1) Represents the Y matrix updated for the k-1th time; Perform singular value decomposition on the matrix M and obtain M = U∑V T , where U is the left singular vector, V is the right singular vector, and ∑ is the singular value matrix; Perform threshold processing on each singular value of the singular value matrix ∑, and set the original singular value to σ i , the new singular value is σ′ i , the new singular value is chosen as σ i -μ -1 A value larger than 0, namely σ′ i =max(σ i -μ -1 ,0); Reconstruction L (k) :L (k) =U∑'V T , where ∑' is the threshold value after processing, represented by σ i 'The singular value matrix constructed on the diagonal; (3.2.2) For S (k) To update: Let N be the intermediate matrix, and calculate the matrix N = XL (k) +μ -1 Y (k-1) ; The element n of the matrix N ij Threshold processing is performed to obtain S (k) , S (k) Each element in is Where sign() represents the sign function; (3.2.3) for Y (k) To update: Y (k) =Y (k-1) +μ(XL (k) -S (k) ); (3.2.4) Check the convergence condition: If ||XL (k) -S (k) || F ≤ε||X F Or reach the maximum number of iterations K = 1000, where |||| F Represents the F-norm.

6. The method for suppressing body motion interference in electrical impedance imaging based on signal structure characteristics according to claim 1, characterized in that: The step (4) comprises: Decomposition result, L (k) To remove the interference of body motion, S (k) is the body motion interference data; for L (k) In the data, the data segment with body motion interference is subjected to electrical impedance imaging to obtain the result without body motion interference.

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