Diagnostic tool and evaluation tool for pleural effusion based on electrical impedance tomography

Abstract

According to the technical scheme, the pleural effusion diagnosis tool based on electrical impedance tomography is characterized by comprising an electrical impedance measurement unit, a first detection unit and a second detection unit, a data reconstruction unit; and a pleural effusion diagnosis unit. Another technical scheme of the invention is to provide a pleural effusion evaluation tool based on electrical impedance tomography, which is characterized by comprising: an electrical impedance measurement unit; a data reconstruction unit; the pleural effusion diagnosis unit is used for judging a pleural effusion area according to the reconstructed three-dimensional conductivity distribution image; and a pleural effusion volume evaluation unit. Compared with the prior art, the method has the following advantages: (1) noninvasive diagnosis and evaluation of pleural effusion can be realized; (2) diagnosis and evaluation of pleural effusion can be carried out beside the bed of the patient; and (3) by adopting the system provided by the invention, continuous monitoring on the pleural effusion can be realized, so that the content change of the pleural effusion is clear.

Description

[0001] This application is a divisional application of patent application CN202211135102.5, entitled "A Diagnostic and Evaluation Tool for Pleural Effusion Based on Electrical Impedance Tomography," with the parent application date being September 19, 2022. Technical Field

[0002] This invention relates to a diagnostic tool and an assessment tool for pleural effusion, which utilizes three-dimensional electrical impedance tomography to diagnose and measure pleural effusion, and belongs to the field of biomedical electrical impedance tomography. Background Technology

[0003] Pleural effusion is the abnormal accumulation of fluid within the pleural cavity and is a common clinical manifestation. Anatomically, the visceral pleura, parietal pleura, and lymph nodes form the normal circulation of fluid within the pleural cavity. An obstruction in any of these pathways can disrupt the balance of fluid inflow and outflow, leading to abnormal accumulation of fluid. Many factors can cause pleural effusion, including pleural diseases, lung diseases, systemic diseases, and organ dysfunction. The amount of pleural effusion significantly impacts the clinician's treatment plan; therefore, auxiliary examinations are crucial. However, there is currently no accurate method for quantifying the amount of pleural effusion.

[0004] Traditional auxiliary examinations rely on experience-based estimations, leading to significant biases and discrepancies between clinical presentations and actual lesions. Quantitative assessments of pleural effusion can only be made roughly using traditional X-rays and ultrasound, making it difficult to differentiate between loculated effusions and pleural tumors, and accurate calculation is even more challenging. Traditional auxiliary examinations typically employ the following methods: (1) Chest X-ray: Small amounts of pleural effusion are not easily detected on routine chest X-rays. Only when the amount of pleural effusion is greater than 175ml, and the costophrenic angle on the affected side appears blunted on an upright anteroposterior view, can pleural effusion be detected. The appearance of the crescent sign on the anteroposterior view requires more than 200ml, and at least 500ml is required to cause blurring of one side of the diaphragm. Various studies have shown that the lateral decubitus position is the most sensitive for detecting small amounts of pleural effusion on chest X-ray, but this is only a rough estimate and cannot be calculated precisely.

[0005] (2) Chest CT: Chest CT plays a crucial role in the differential diagnosis of pleural effusion, detecting lesions that are difficult to distinguish on routine chest X-rays, and showing the degree and extent of masses, nodules, pleural plaques, calcifications, and loculated pleural effusions. However, the information provided by chest CT is also extremely limited, generally estimated visually as large, moderate, or small. It can also be classified according to the depth of the pleural effusion, but there is basically no unified standard. Effusion depths of less than 3cm, 3-5cm, and greater than 5cm are respectively considered small, moderate, and large.

[0006] (3) Ultrasound: Ultrasound is more accurate than chest X-ray in determining the volume of pleural effusion, especially for diagnosing small amounts of pleural effusion and guiding thoracentesis. However, due to the fluid mobility and amorphous shape of pleural effusion, ultrasound can only estimate the volume of pleural effusion and cannot achieve precise measurement.

[0007] In clinical treatment, the above methods often have a certain degree of error. Therefore, it is particularly important to accurately measure the amount of pleural effusion and provide objective data for clinicians.

[0008] Electrical Impedance Tomography (EIT) is a technique that uses the electrical impedance properties of a biological sample to create an image. It offers advantages such as being non-invasive, bedside, and allowing for continuous monitoring. Thoracic imaging is one of the most promising applications of EIT. It utilizes an array of electrodes distributed around the pleural cavity to measure electrical impedance, and then reconstructs the data to obtain an image of the electrical conductivity within the pleural cavity. When pleural effusion occurs, the conductivity of the corresponding area increases (the conductivity of pleural effusion is higher than that of surrounding tissues), which is reflected in the EIT image. Therefore, EIT images can be used to estimate the volume of pleural effusion. Summary of the Invention

[0009] The objective of this invention is to use EIT (Earth Tract Injection) technology to determine pleural effusion. Another objective of this invention is to use EIT technology to more accurately assess the volume of pleural effusion.

[0010] To achieve the above objectives, one technical solution of the present invention provides a diagnostic tool for pleural effusion based on electrical impedance tomography, comprising: The impedance measurement unit uses an array of electrodes distributed on the surface of the human chest cavity to measure impedance and obtain impedance measurement data. The data reconstruction unit is used to reconstruct a three-dimensional conductivity distribution image within the thoracic cavity using electrical impedance measurement data. The data reconstruction unit employs an image reconstruction algorithm to perform forward modeling on the electrical impedance measurement data. The forward modeling process includes the following steps: For a given constant current excitation, the potential distribution within the thoracic cavity satisfies the conductivity equation and boundary conditions shown below: ; ; ; ; ; ; In the formula: This represents the electrical potential distribution within the thoracic cavity. Let be the normal derivative of the potential; The electrical conductivity distribution within the thoracic cavity; It is a micro-element of area; The region covered by electrode l in the electrode array; and These represent the current and potential on electrode l, respectively; Let be the contact impedance of electrode l; the boundary value problem shown in the above equation is solved using numerical methods; Represent the forward model as an operator The electrical impedance tomography measurement model is represented as follows: ; The image reconstruction problem of electrical impedance tomography is from Solving for conductivity distribution ; Image reconstruction from electrical impedance tomography can be expressed as the following optimization problem: ; Where R and α are the regularization matrix and regularization parameter, respectively, the iterative formula for solving the above optimization problem is obtained using the Gauss-Newton method: ; In the formula, This represents the conductivity distribution in the k-th iteration. The Jacobian matrix representing the forward model is defined as follows: ; The above iterative formula is used to iterate until convergence, thus obtaining a three-dimensional conductivity distribution image within the thoracic cavity; The pleural effusion diagnostic unit is used to determine the area of ​​pleural effusion based on the reconstructed three-dimensional conductivity distribution image.

[0011] Preferably, the impedance measurement unit includes an electrode array, a constant current excitation, and a voltage measurement module, wherein: The electrode array contains at least 32 electrodes, which are arranged in a three-dimensional manner on the surface of the thoracic cavity in a multi-ring electrode layout. Constant current excitation is used to apply constant current excitation to some electrodes in an electrode array, and the position of the excitation is continuously changed during the impedance measurement. The voltage measurement module is used to measure the voltage on the electrodes in the electrode array that are not subjected to constant current excitation, and to obtain the impedance measurement data by normalizing the measured voltage with the excitation current.

[0012] Preferably, the data reconstruction unit further employs random or deterministic image reconstruction algorithms, model-based or data-driven image reconstruction algorithms to reconstruct the three-dimensional conductivity distribution within the thoracic cavity.

[0013] Preferably, the pleural effusion diagnostic unit uses an image segmentation algorithm to determine the region of abnormally increased conductivity caused by pleural effusion.

[0014] Preferably, the image segmentation algorithm is a threshold-based image segmentation algorithm, then let the three-dimensional conductivity distribution image obtained by the data reconstruction unit be... , Let be the value of the i-th voxel in the three-dimensional conductivity distribution image, and M be the total number of voxels in the image. For any i-th voxel, we have: If If the value of voxel > T, then the i-th voxel belongs to the pleural effusion region; otherwise, the i-th voxel does not belong to the pleural effusion region. The pleural effusion diagnostic unit obtains the pleural effusion region based on all voxels belonging to the pleural effusion region, denoted as R. PF .

[0015] Another technical solution of the present invention is to provide a pleural effusion assessment tool based on electrical impedance tomography, characterized in that it includes: The impedance measurement unit uses an array of electrodes distributed on the surface of the human chest cavity to measure impedance and obtain impedance measurement data. The data reconstruction unit is used to reconstruct a three-dimensional conductivity distribution image within the thoracic cavity using electrical impedance measurement data. The pleural effusion diagnostic unit is used to determine the region of pleural effusion based on the reconstructed three-dimensional conductivity distribution image, denoted as R. PF ; Pleural effusion volume assessment unit, used to assess pleural effusion area R PF Estimate the volume of pleural effusion.

[0016] Preferably, the pleural effusion volume assessment unit obtains the pleural effusion area R. PF The total number of voxels contained is multiplied by the volume per unit voxel to obtain the volume of the pleural effusion.

[0017] Compared with the existing technical solutions, the present invention has the following advantages: (1) it can realize non-invasive diagnosis and assessment of pleural effusion; (2) it can be performed at the patient's bedside for diagnosis and assessment of pleural effusion; (3) the system provided by the present invention can realize continuous monitoring of pleural effusion to clarify the changes in pleural effusion content. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2(a) shows the electrical impedance measurement data generated by simulation using a simulated pleural effusion model; Figure 2(b) shows the reconstructed three-dimensional conductivity distribution map; Figure 2(c) shows the segmented pleural effusion region. Detailed Implementation

[0019] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0020] This embodiment discloses a diagnostic tool for pleural effusion based on electrical impedance tomography, which includes: The electrical impedance measurement unit uses an array of electrodes distributed on the surface of the human chest cavity to perform electrical impedance measurements and obtain electrical impedance measurement data, denoted as vector d.

[0021] In this embodiment, the electrode array contains at least 32 electrodes. To fully probe the tissues inside the thoracic cavity, the electrode array needs to be arranged in three dimensions on the surface of the thoracic cavity, such as using a multi-ring electrode layout. During the measurement process, a constant current excitation is applied to some electrodes in the electrode array while the voltage on other electrodes is measured. The position of the excitation is continuously changed around the surface of the thoracic cavity, and corresponding voltage measurements are performed. The measured voltage is normalized using the excitation current, and finally a frame of impedance measurement data is obtained, denoted as a vector d.

[0022] The data reconstruction unit is used to reconstruct a three-dimensional conductivity distribution image within the thoracic cavity using electrical impedance measurement data.

[0023] When the data reconstruction unit reconstructs the three-dimensional conductivity distribution, it first needs to perform forward modeling on the impedance measurement data. In this embodiment, the forward modeling includes the following steps: For a given constant current excitation, the potential distribution within the thoracic cavity satisfies the conductivity equation and boundary conditions shown below: ; ; ; ; ; ; In the formula: This represents the electrical potential distribution within the thoracic cavity. Let be the normal derivative of the potential; The electrical conductivity distribution within the thoracic cavity; It is a micro-element of area; The region covered by electrode l in the electrode array; and These represent the current and potential on electrode l, respectively; Let be the contact resistance of electrode l.

[0024] The boundary value problem shown in the above equation can be solved using numerical methods (e.g., the finite element method).

[0025] In this invention, the forward model is represented as an operator. Therefore, the electrical impedance tomography measurement model can be represented as follows: ; The image reconstruction problem of electrical impedance tomography is from Solving for conductivity distribution The following example, using an optimization framework, illustrates how to perform image reconstruction in electrical impedance tomography. Image reconstruction in electrical impedance tomography can be expressed as the following optimization problem: ; Where R and α are the regularization matrix and regularization parameter, respectively. The iterative formula for solving the above optimization problem can be obtained using the Gauss-Newton method: ; In the formula, This represents the conductivity distribution in the k-th iteration. The Jacobian matrix representing the forward model is defined as follows: ; By iterating using the above iterative formula until convergence, a three-dimensional conductivity distribution image within the thoracic cavity can be obtained.

[0026] In addition to the reconstruction methods described above, the data reconstruction unit can also use any other available image reconstruction algorithm to reconstruct the three-dimensional conductivity distribution within the thoracic cavity, such as various stochastic or deterministic image reconstruction algorithms, model-based or data-driven image reconstruction algorithms.

[0027] The pleural effusion diagnostic unit is used to determine the region of pleural effusion based on the reconstructed three-dimensional conductivity distribution image. The unit utilizes an image segmentation algorithm to identify areas of abnormally elevated conductivity caused by pleural effusion.

[0028] This embodiment uses the threshold method as an example for illustration: A threshold T is set such that it lies between the conductivity of normal tissue and the conductivity of tissue with pleural effusion. Let the three-dimensional conductivity distribution image obtained by the data reconstruction unit be... , Let be the value of the i-th voxel in the three-dimensional conductivity distribution image, and M be the total number of voxels in the image. Determine the region of pleural effusion based on the following logic: if If the value is greater than T, then the i-th voxel belongs to the pleural effusion region; otherwise, the i-th voxel does not belong to the pleural effusion region.

[0029] Finally, the pleural effusion region was obtained based on all voxels belonging to the pleural effusion region, denoted as R. PF .

[0030] This embodiment also discloses a pleural effusion assessment tool based on electrical impedance tomography, which includes: The aforementioned pleural effusion diagnostic tool based on electrical impedance tomography obtains the R region of the pleural effusion using this tool. PF .

[0031] Pleural effusion volume assessment unit, used to assess pleural effusion area R PF Estimating the volume of pleural effusion involves the following steps: Assuming the volume of each voxel in the three-dimensional conductivity distribution image is V0, based on the pleural effusion region R... PF The number of voxels N contained in the pleural effusion is used to calculate the volume V of the pleural effusion. PF : V PF =N×V0; Figure 2(a) shows the electrical impedance measurement data generated using a simulated pleural effusion model. Figure 2(b) shows the three-dimensional conductivity distribution image reconstructed using the Gauss-Newton method, where α is set to 1×10⁻⁶. -3 The iteration step count was set to 10. In the reconstructed images above, the pleural effusion region showed an abnormally high conductivity (approximately 500 mS / m, while the conductivity of normal lungs is approximately 100 mS / m). Figure 2(c) shows the pleural effusion region segmented using the thresholding method (T = 400 mS / m). Furthermore, it was statistically determined that the pleural effusion region contained N = 516 voxels, and the volume of each voxel was V0 = 0.216 cm3. Therefore, the volume of the pleural effusion could be estimated as V. PF =N×V0=111ml.

Claims

1. A pleural effusion diagnostic tool based on electrical impedance tomography, characterized in that, The application comprises: an electrical impedance measurement unit for performing electrical impedance measurement using an electrode array distributed on the surface of the chest cavity of a human body to obtain electrical impedance measurement data; a data reconstruction unit for reconstructing a three-dimensional conductivity distribution image in the chest cavity using the electrical impedance measurement data; the data reconstruction unit uses an image reconstruction algorithm to perform forward modeling on the electrical impedance measurement data, and the forward modeling comprises the following steps: for a determined constant current excitation, the potential distribution in the chest cavity satisfies the conductivity equation and the boundary condition shown in the following formula: ； ； ； ； ； ； where: is the potential distribution inside the thorax, is the normal derivative of the potential; is the conductivity distribution inside the thorax; is the area element; is the area covered by electrode / in the electrode array; and are the current and the potential on electrode / , respectively; is the contact impedance of electrode / ; the boundary value problem shown in the above equations is solved by numerical methods; The forward model is represented as an operator The electrical impedance tomography measurement model is represented as follows: ； The image reconstruction problem of electrical impedance tomography is to solve the conductivity distribution from ; the image reconstruction of electrical impedance tomography is expressed as the following optimization problem: ； wherein R and a are a regularization matrix and a regularization parameter respectively, and the iteration formula for solving the above optimization problem is obtained by using the Gauss-Newton method: ； wherein denotes the conductivity distribution of the kth iteration, denotes the Jacobian matrix of the forward model, defined as ; the three-dimensional conductivity distribution image in the chest cavity is obtained by using the above iteration formula to iterate until convergence; a pleural effusion diagnosis unit for determining a pleural effusion region according to the reconstructed three-dimensional conductivity distribution image.

2. The pleural effusion diagnostic tool based on electrical impedance tomography as claimed in claim 1, wherein, The electrical impedance measurement unit comprises an electrode array, a constant current excitation and a voltage measurement module, wherein: the electrode array comprises at least 32 electrodes and is arranged in a multi-circle electrode layout on the surface of the chest cavity in a three-dimensional manner; the constant current excitation is used to apply a constant current excitation to some electrodes in the electrode array, and the position of the excitation is changed constantly during the electrical impedance measurement process; the voltage measurement module is used to measure the voltage on the electrodes in the electrode array which are not subjected to the constant current excitation, and the electrical impedance measurement data is obtained by normalizing the measured voltage with the excitation current.

3. The electrical impedance tomography based pleural effusion diagnostic tool of claim 1, wherein, The data reconstruction unit also uses a random or deterministic image reconstruction algorithm, a model-based or data-driven image reconstruction algorithm to reconstruct the three-dimensional conductivity distribution in the chest cavity.

4. The electrical impedance tomography based pleural effusion diagnostic tool of claim 1, wherein, The pleural effusion diagnosis unit determines the conductivity abnormally high region caused by pleural effusion by using an image segmentation algorithm.

5. A thoracic effusion diagnostic tool based on electrical impedance tomography as claimed in claim 4, wherein, The image segmentation algorithm is a threshold T-based image segmentation algorithm, and the three-dimensional conductivity distribution image obtained by the data reconstruction unit is , is the i-th voxel value in the three-dimensional conductivity distribution image, M is the total number of voxels in the image, and for any i-th voxel, if >T, the i-th voxel belongs to the pleural effusion region. Otherwise, the i-th voxel does not belong to the pleural effusion region; the pleural effusion diagnosis unit obtains a pleural effusion region based on all voxels belonging to the pleural effusion region, denoted as R PF .

6. A pleural effusion assessment tool based on electrical impedance tomography, characterized in that, The application comprises: an electrical impedance measurement unit for performing electrical impedance measurement using an electrode array distributed on the surface of the chest cavity of a human body to obtain electrical impedance measurement data; a data reconstruction unit for reconstructing a three-dimensional conductivity distribution image in the chest cavity using the electrical impedance measurement data; A pleural effusion diagnosis unit for determining a pleural effusion region from a reconstructed three-dimensional conductivity profile image, denoted R PF ; A pleural effusion volume assessment unit for estimating a pleural effusion volume from a pleural effusion region R PF estimating a pleural effusion volume.

7. A resistance plethysmography-based pleural effusion assessment tool as claimed in claim 6, wherein, The pleural effusion volume evaluation unit obtains a pleural effusion region R PF The total number of voxels contained is multiplied by the volume of a unit voxel to obtain the pleural effusion volume.

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