WO2024007877A1 - 用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置 - Google Patents

用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置 Download PDF

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WO2024007877A1
WO2024007877A1 PCT/CN2023/102446 CN2023102446W WO2024007877A1 WO 2024007877 A1 WO2024007877 A1 WO 2024007877A1 CN 2023102446 W CN2023102446 W CN 2023102446W WO 2024007877 A1 WO2024007877 A1 WO 2024007877A1
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impedance
respiratory
respiratory impedance
pixel
lung
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PCT/CN2023/102446
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English (en)
French (fr)
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陆之忠
陈书哲
王谊冰
李丽
王璐
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北京华睿博视医学影像技术有限公司
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Publication of WO2024007877A1 publication Critical patent/WO2024007877A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0809—Detecting, measuring or recording devices for evaluating the respiratory organs by impedance pneumography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes

Definitions

  • the present disclosure relates to the field of biomedical imaging technology, and in particular to a method and device for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • the human respiratory process is studied by calculating flow volume loops.
  • This flow volume ring can reflect the relationship between the lung volume and the lung gas flow during the human body's breathing process.
  • this relationship can only roughly reflect the human body's breathing process as a whole, and it is difficult to conduct detailed research on the breathing process. question.
  • the present disclosure proposes a method and device for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • embodiments of the present disclosure provide a method for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • the method includes: obtaining a subject's lung pixel respiratory impedance map; wherein, the lung pixel respiratory impedance map is To reflect the changing state of the conductivity of each pixel in the subject's lung area during the predetermined respiratory stage; based on the lung pixel respiratory impedance map, obtain the respiratory impedance curve on the interest domain; based on the respiratory impedance curve on the interest domain, A curve is obtained that presents the relationship between respiratory impedance and the rate of change of respiratory impedance.
  • inventions of the present disclosure provide a device for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • the device includes: a lung pixel respiratory impedance map acquisition unit, a respiratory impedance curve acquisition unit, and a curve acquisition unit.
  • the lung pixel respiratory impedance map acquisition unit is configured to acquire the subject's lung pixel respiratory impedance map; wherein the lung pixel respiratory impedance map is used to reflect the performance of each pixel in the subject's lung area during a predetermined respiratory stage.
  • the respiratory impedance curve acquisition unit is configured to obtain the respiratory impedance curve on the domain of interest based on the lung pixel respiratory impedance map; the curve acquisition unit is configured to obtain the respiratory impedance curve based on the interest domain for presentation Curve of the relationship between respiratory impedance and rate of change of respiratory impedance.
  • embodiments of the present disclosure provide a computer-readable storage medium.
  • Program code is stored on the computer-readable storage medium.
  • any one of the above embodiments is implemented.
  • inventions of the present disclosure provide an electronic device.
  • the electronic device includes a memory and a processor.
  • the memory stores program code that can be run on the processor.
  • the program code is executed by the processor, the above embodiments are implemented.
  • Figure 1 is a subject’s lung volume change curve obtained by existing technology
  • Figure 2 is a flow volume loop obtained by existing technology
  • Figure 3 is a method flow chart of an embodiment of the present disclosure
  • Figure 4 shows the changes in electrical impedance data measured by the subject during the predetermined breathing stage in the embodiment of the present disclosure
  • Figure 5 is a global respiratory impedance curve in an embodiment of the present disclosure
  • Figure 6 is the global curve corresponding to Figure 5;
  • Figure 7 is a lung pixel respiratory impedance map of a subject in an embodiment of the present disclosure.
  • Figure 8 is a two-dimensional interest domain divided in an embodiment of the present disclosure.
  • Figure 9 is the respiratory impedance curve corresponding to the interest domain shown in Figure 8.
  • Figure 10 is a curve corresponding to the interest domain shown in Figure 8 to present the relationship between respiratory impedance and respiratory impedance change rate;
  • Figure 11 is a three-dimensional domain of interest divided in an embodiment of the present disclosure.
  • Figure 12 is the respiratory impedance curve corresponding to the interest domain shown in Figure 11;
  • Figure 13 is a curve corresponding to the interest domain shown in Figure 11 to present the relationship between respiratory impedance and respiratory impedance change rate;
  • Figure 14 is another three-dimensional interest domain divided in the embodiment of the present disclosure.
  • Figure 15 is the respiratory impedance curve corresponding to the interest domain shown in Figure 14;
  • Figure 16 is a curve corresponding to the interest domain shown in Figure 14 for showing the relationship between respiratory impedance and respiratory impedance change rate;
  • Figure 17 is another three-dimensional interest domain divided in the embodiment of the present disclosure.
  • Figure 18 is the respiratory impedance curve corresponding to the interest domain shown in Figure 17;
  • Figure 19 is a curve corresponding to the interest domain shown in Figure 17 for showing the relationship between respiratory impedance and respiratory impedance change rate.
  • Figure 20 is a device structure diagram of an embodiment of the present disclosure.
  • the human respiratory process is usually studied by calculating flow volume loops.
  • This flow volume ring can reflect the relationship between the lung volume and the lung gas flow during the human body's breathing process, but this relationship can only roughly reflect the human body's breathing process as a whole. If further detailed research on the respiratory process is required, the existing flow volume loop cannot meet the current scientific research needs.
  • the existing technology uses a pulmonary function meter to obtain the subject's overall lung function information.
  • the subject's lung volume change curve as shown in Figure 1 can be obtained.
  • Figure 1 specifically reflects the lung volume changes of a subject measured according to the breathing process of normal tidal flow, deep inhalation, and trying to exhale as quickly as possible.
  • the gas flow in the subject's lungs can be regarded as the change rate of the above-mentioned lung volume, and the curve reflecting the relationship between the lung volume and the change rate of the lung volume is called a flow-volume loop. That is, through the lung volume change curve in Figure 1, the flow volume loop shown in Figure 2 can be further obtained.
  • the existing technology can only roughly study the subject's breathing process and evaluate the subject's overall lung function at a macro level through the flow volume loop, which provides very limited information for subsequent clinical applications.
  • embodiments of the present disclosure provide a method for presenting the relationship between respiratory impedance and respiratory impedance change rate. As shown in Figure 3, the method includes: step S101, step S102 and step S103.
  • Step S101 Obtain the subject's lung pixel respiratory impedance map.
  • the lung pixel respiratory impedance map is used to reflect the changing state of the conductivity of each pixel in the subject's lung area during a predetermined respiratory stage.
  • obtaining the subject's lung pixel respiratory impedance map includes: obtaining the subject's chest EIT measurement data; performing image reconstruction on the chest EIT measurement data to obtain the chest pixel impedance map; Perform pixel filtering on the pixel impedance map to obtain the lung pixel respiratory impedance map.
  • the measurement area of thoracic EIT measurement data is the entire thoracic cavity, which mainly includes physiological structures such as the lungs, heart, and aorta. These structures will undergo dynamic changes in electrical impedance values during the breathing process. Therefore, in order to accurately study the respiratory process, it is necessary to perform pixel screening on the chest pixel impedance map and separate the lung pixel respiratory impedance map.
  • the thoracic pixel impedance map includes: the impedance curve of each pixel in the subject's thoracic area; wherein the impedance curve of each pixel in the thoracic area is used to reflect the behavior of the pixel in the predetermined respiratory stage. The changing state of conductivity.
  • this embodiment performs pixel screening on the chest pixel impedance map to obtain the lung pixel respiratory impedance map, including: taking a time domain average of the impedance curve of each pixel in the chest area value, obtain the impedance mean set of all pixels in the chest area; filter out the impedance mean values that are smaller than the preset threshold from the impedance mean set of all pixels in the chest area, and obtain the filtered impedance mean set; filter from the chest pixel impedance map Remove the impedance curve of the pixel corresponding to the filtered impedance mean set to obtain the lung pixel respiratory impedance map.
  • the preset threshold can be set according to the actual situation. For example, the average impedance values of all pixels in the chest area are arranged from large to small, the bottom 20% of the pixels are filtered out, and they are identified as non-pulmonary pixels. The remaining pixels are filtered out, and the remaining 80% of the pixels are lung pixels.
  • the flow volume loop can be used to judge the functional status of the lungs, as shown in Figure 2 Show.
  • EIT Electrometric Impedance Tomography
  • the measured impedance value can approximately linearly reflect the size of the gas volume in the lungs.
  • the change rate of the impedance value can also reflect the change in volume. That is the size of the gas flow in the lungs.
  • EIT is a radiation-free, non-invasive, low-cost, functional imaging technology.
  • the basic principle of EIT is to use various excitation methods to apply a safe current below the cell excitation threshold to the human body according to different electrode arrangements, and then measure the voltage distribution data on the body surface by scanning the array electrodes, and then apply the electromagnetic field inverse problem based on The solved image reconstruction algorithm finally inverts to obtain a two-dimensional or three-dimensional image of the conductivity distribution or change state in the body.
  • bioelectrical impedance The impedance changes exhibited by living organisms, biological tissues, biological organs, and biological cells under the action of safe currents below the excitation threshold are called bioelectrical impedance.
  • the impedances of various tissues in the human body vary greatly; when the physiological and pathological conditions change, the conductivity values of each tissue also change.
  • the gas content in the lungs changes significantly periodically; when the degree of gas filling in the lungs is different, the impedance value also changes accordingly. Therefore, electrical impedance measurement can detect the gas content and its spatiotemporal distribution in the lungs with high time resolution.
  • EIT can continuously and dynamically monitor the subject's lung ventilation and calculate lung function parameters.
  • EIT can conveniently and accurately reconstruct the pixel-level conductivity distribution of the lungs.
  • this embodiment adopts the method of EIT measurement of the subject's chest to obtain the chest EIT measurement data, and then conveniently and accurately obtain the subject's lung pixel respiratory impedance map, and the lung pixel
  • the direction and amplitude of the impedance curve of each pixel contained in the respiratory impedance map contain certain information about the respiratory process.
  • the subject first completes the pulmonary function test, and the change curve of gas volume in the lungs over time is measured according to the breathing sequence of normal tide, deep inhalation, and trying to exhale as quickly as possible, that is, the lung volume change curve is as follows As shown in Figure 1.
  • the subject needs to wear the EIT electrode strap to complete the simultaneous measurement of EIT.
  • the changes in electrical impedance data obtained by EIT due to ventilation are shown in Figure 4. It can be seen that the two are highly consistent.
  • image reconstruction is performed on the thoracic EIT measurement data obtained through EIT measurement.
  • image reconstruction is performed on the measurement data corresponding to the portion of the thoracic EIT measurement data that involves inhaling deeply and exhaling as quickly as possible to obtain the thoracic cavity.
  • Pixel impedance map perform pixel filtering on the chest pixel impedance map, filter out lung pixels from all chest pixels, and obtain the lung pixel respiratory impedance map.
  • the lung pixel respiratory impedance map obtained using the above method is shown in Figure 7. As can be seen from Figure 7, the lung activities at different pixel positions are quite different during the same breathing process. Therefore, studying the characteristics of different areas of the lungs can provide more detailed information on the respiratory process.
  • Step S102 Obtain the respiratory impedance curve in the domain of interest based on the lung pixel respiratory impedance map.
  • the lung pixel respiratory impedance map includes: the impedance curve of each pixel in the lung area; wherein, the impedance curve of each pixel in the lung area is used to reflect the pixel's predetermined respiratory time.
  • obtaining the respiratory impedance curve in the interest domain includes: superimposing the impedance curve of each pixel in the interest domain to obtain the respiratory impedance curve in the interest domain.
  • the region of interest (Region of Interest, ROI) described in this embodiment can be a set of pixels or a single pixel, and the lung area can be divided into different regions of interest.
  • the division of the interest domain includes four-quadrant equal divisions in the two-dimensional electrode plane, and different methods of partition calculation in the three-dimensional imaging area, so as to realize the study of the respiratory process in different interest domains.
  • the interest domain described in this embodiment can be obtained in the following two ways.
  • the first way is to obtain a two-dimensional domain of interest on a two-dimensional area: average the impedance curve of each pixel in the lung area to obtain the impedance mean set of all pixels in the lung area;
  • the impedance mean value set of all pixels is reconstructed on a two-dimensional plane to obtain a two-dimensional conductivity distribution image;
  • the imaging area of the two-dimensional conductivity distribution image is equally divided by the first preset rule to obtain multiple two-dimensional equal parts. Region; obtain each two-dimensional equally divided region as the domain of interest.
  • the impedance curve of each pixel in Figure 7 is averaged to obtain a set of impedance mean values for all pixels in the lung area, and the set of impedance mean values for all pixels in the lung area is calculated in two dimensions.
  • Image reconstruction is performed on the plane to obtain a two-dimensional conductivity distribution image as shown in Figure 8. It can be seen that Figure 8 shows two half-moon shapes as a whole, and the center activity is relatively active.
  • the imaging area of the two-dimensional conductivity distribution image is equally divided into four quadrants to obtain four two-dimensional equally divided areas ROI1, ROI2, ROI3 and ROI4, where each of the above two-dimensional equally divided areas is a domain of interest.
  • the second method is to obtain a three-dimensional domain of interest in a three-dimensional area: obtain a three-dimensional lung image model based on the lung pixel respiratory impedance map; divide the three-dimensional lung image model equally on the preset surface according to the second preset rule , obtain multiple three-dimensional equally divided regions; obtain each three-dimensional equally divided region as a domain of interest.
  • Three-dimensional interest domain energy It can more comprehensively observe the functional differences of the lungs in space, so that the respiratory process can be studied in more detail.
  • a three-dimensional lung image model can be obtained.
  • this embodiment uses a schematic diagram to display The three-dimensional model of the lung image is shown in Figure 11, Figure 14 and Figure 17.
  • the lungs were divided into four pieces on the coronal plane.
  • the schematic diagram of the three-dimensional lung image model was evenly divided into four pieces on the coronal plane of the three-dimensional space. fields of interest, labeled 1, 2, 3, and 4.
  • fields of interest labeled 1, 2, 3, and 4.
  • the lungs are divided into four pieces in the sagittal plane, coronal plane, and horizontal plane.
  • the schematic diagram of the three-dimensional lung image model is evenly divided into eight areas of interest on the coronal plane of the three-dimensional space and marked as 1, 2, 3, 4, 5, 6, 7, and 8.
  • the eight respiratory impedance curves corresponding to the four interest areas in Figure 14 are shown in Figure 15.
  • the lungs are cut into six pieces in the sagittal plane and the coronal plane, and divided into four pieces in the horizontal plane.
  • the schematic diagram of the three-dimensional lung image model is evenly divided into twelve areas of interest on the coronal plane of the three-dimensional space and marked as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 .
  • the respiratory impedance curve on the interest domain can be obtained.
  • the eight respiratory impedance curves corresponding to the four interest areas in Figure 17 are shown in Figure 18.
  • Step S103 Based on the respiratory impedance curve in the interest domain, obtain a curve showing the relationship between respiratory impedance and respiratory impedance change rate.
  • a curve showing the relationship between the respiratory impedance and the respiratory impedance change rate is obtained, including: performing noise reduction processing on the respiratory impedance curve in the interest domain, and obtaining the denoised The respiratory impedance curve of and the respiratory impedance change rate after noise reduction, to obtain a curve showing the relationship between the respiratory impedance and the respiratory impedance change rate.
  • a curve showing the relationship between respiratory impedance and respiratory impedance change rate is obtained, including: using the respiratory impedance curve after noise reduction
  • the reflected data is used as the abscissa
  • the data reflected by the respiratory impedance change rate after noise reduction is used as the ordinate
  • a curve is obtained by plotting the relationship between the respiratory impedance and the respiratory impedance change rate.
  • the corresponding respiratory impedance curve can be obtained.
  • rate of change of respiratory impedance The respiratory impedance change rate is also subjected to noise reduction processing.
  • the data reflected by the noise-reduced respiratory impedance curve is used as the abscissa, and the data reflected by the noise-reduced respiratory impedance change rate is used as the ordinate, and the graph is obtained.
  • Corresponding to the respiratory impedance curve is a curve showing the relationship between respiratory impedance and respiratory impedance change rate.
  • FIG. 10 shows four curves representing the relationship between respiratory impedance and respiratory impedance change rate corresponding to the four interest areas shown in FIG. 8 .
  • This curve can be used to study the subject's breathing process similar to the flow volume loop shown in Figure 2. The difference is that the flow volume loop in Figure 2 can only roughly reflect the respiratory process as a whole, while the above-mentioned curve obtained in this embodiment to present the relationship between respiratory impedance and respiratory impedance change rate can be studied at the pixel level.
  • the subject s breathing process.
  • the activities of different lung areas are quite different, and the breathing processes they reflect are also different.
  • the lungs located in ROI1 are the most active during the breathing process, and the lungs located in ROI 2 are the least active during the breathing process. active.
  • the curve corresponding to the interest domain shown in Figure 11 for presenting the relationship between respiratory impedance and respiratory impedance change rate, and the curve corresponding to the interest domain shown in Figure 14 for presenting respiratory impedance can be obtained.
  • the curve of the relationship between the respiratory impedance change rate and the curve corresponding to the interest domain shown in Figure 17 for presenting the relationship between the respiratory impedance and the respiratory impedance change rate are shown in Figure 13, Figure 16 and Figure 19 respectively.
  • the domain of interest in this embodiment may also be the subject's entire lung area.
  • the global respiratory impedance curve can be obtained by superimposing the impedance curves of all pixels shown in Figure 7, as shown in Figure 5.
  • the global curve shown in Figure 6 enables an overall global study of the subject's breathing process.
  • the domain of interest in this embodiment may also be a certain pixel in the subject's lung area.
  • the respiratory impedance curve of the domain of interest is the impedance curve of the pixel.
  • the curve obtained through this embodiment to present the relationship between respiratory impedance and respiratory impedance change rate can not only be used to study the subject's breathing process more precisely, but can also be used to conduct experiments on the subject's lung function. Evaluate.
  • the lung function in the interest domain can also be evaluated.
  • ROI2 the front half of the lung represented by ROI2, ROI5, ROI8 and ROI11 is more active during breathing.
  • the global curve shown in Figure 6 can also provide an overall global assessment of the subject's lung function. That is to say, the technical solution provided by the embodiments of the present disclosure provides a calculation method for the global curve and the local curve in a certain domain of interest, and can comprehensively and accurately evaluate the subject's breathing process and lung function from both global and local levels. research and evaluation.
  • the pulmonary function test results obtained through the existing technology can be used as a standard to verify the reliability of the EIT results obtained in this embodiment; for the local curve, the EIT results show the local lung function characteristics.
  • Embodiments of the present disclosure provide a method for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • the respiratory tract in the area of interest is obtained.
  • Impedance curve based on the respiratory impedance curve on this interest domain, obtains a curve used to present the relationship between respiratory impedance and respiratory impedance change rate, because the lung pixel respiratory impedance map can reflect every pixel in the subject's lung area
  • the changing state of conductivity in a predetermined breathing stage enables embodiments of the present disclosure to study the subject's breathing process at the pixel level, so the research process is more refined. That is to say, the technical solution provided by the embodiments of the present disclosure can study the respiratory process in a more detailed manner.
  • the present disclosure also provides a device for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • the device includes: a lung pixel respiratory impedance map acquisition unit 201, a respiratory impedance Curve acquisition unit 202 and curve acquisition unit 203.
  • the lung pixel respiratory impedance map acquisition unit 201 is configured to acquire the subject's lung pixel respiratory impedance map; wherein the lung pixel respiratory impedance map is used to reflect the predetermined respiratory stage of each pixel in the subject's lung area. The changing state of conductivity.
  • the respiratory impedance curve acquisition unit 202 is configured to obtain the respiratory impedance curve in the domain of interest based on the lung pixel respiratory impedance map.
  • the curve acquisition unit 203 is configured to obtain a curve showing the relationship between the respiratory impedance and the respiratory impedance change rate based on the respiratory impedance curve on the domain of interest.
  • the lung pixel respiratory impedance map acquisition unit 201 includes: a measurement data acquisition unit, an image reconstruction unit, and a pixel screening unit.
  • the measurement data acquisition unit is configured to obtain the subject's chest EIT measurement data; the image reconstruction unit is configured to perform image reconstruction on the chest EIT measurement data and obtain the chest pixel impedance map; the pixel filtering unit is configured to perform image reconstruction on the chest pixel impedance map. Pixel screening is performed to obtain the lung pixel respiratory impedance map.
  • the chest pixel impedance map includes: the impedance curve of each pixel in the subject's chest area; wherein the impedance curve of each pixel in the chest area is used to reflect the conductivity of the pixel in the predetermined respiratory stage. Change status.
  • the pixel filtering unit performs pixel filtering on the chest pixel impedance map in the following manner: Obtain the lung pixel respiratory impedance map: take the time domain average of the impedance curve of each pixel in the chest area to obtain the impedance mean set of all pixels in the chest area; filter out values from the impedance mean set of all pixels in the thorax area If the impedance mean value is less than the preset threshold, a filtered impedance mean set is obtained; the impedance curve of the pixel corresponding to the filtered impedance mean set is filtered out from the chest pixel impedance map, and the lung pixel respiratory impedance map is obtained.
  • the lung pixel respiratory impedance map includes: the impedance curve of each pixel in the lung area; wherein the impedance curve of each pixel in the lung area is used to reflect the change in conductivity of the pixel during the predetermined respiratory stage. state.
  • the respiratory impedance curve acquisition unit 202 obtains the respiratory impedance curve in the interest domain in the following manner: superimposing the impedance curves of each pixel in the interest domain to obtain the respiratory impedance curve in the interest domain.
  • the interest domain is obtained in the following manner: averaging the impedance curves of each pixel in the lung area to obtain a set of impedance averages for all pixels in the lung area; The image is reconstructed on a two-dimensional plane to obtain a two-dimensional conductivity distribution image; the imaging area of the two-dimensional conductivity distribution image is equally divided by the first preset rule to obtain multiple two-dimensional equally divided areas; each of the two-dimensional conductivity distribution images is obtained.
  • the two-dimensional equally divided area serves as the domain of interest.
  • the interest domain is obtained in the following manner: based on the lung pixel respiratory impedance map, a three-dimensional lung image model is obtained; the three-dimensional lung image model is equally divided by the second preset rule on the preset surface to obtain multiple three-dimensional equally divided regions; obtain each three-dimensional equally divided region as a domain of interest.
  • the curve acquisition unit 203 includes: a first noise reduction unit, a derivative solving unit, a second noise reduction unit, and a curve acquisition subunit.
  • the first noise reduction unit is configured to perform noise reduction processing on the respiratory impedance curve in the domain of interest to obtain the noise-reduced respiratory impedance curve;
  • the derivative solving unit is configured to solve the derivative of the noise-reduced respiratory impedance curve to obtain the respiratory impedance. rate of change;
  • the second noise reduction unit is configured to perform noise reduction processing on the respiratory impedance change rate to obtain the respiratory impedance change rate after noise reduction;
  • the curve acquisition subunit is configured to perform noise reduction based on the respiratory impedance curve after noise reduction and the noise reduction rate.
  • the respiratory impedance change rate is , and a curve showing the relationship between the respiratory impedance and the respiratory impedance change rate is obtained.
  • the curve acquisition subunit uses the following method to obtain a curve showing the relationship between respiratory impedance and respiratory impedance change rate: the data reflected by the noise-reduced respiratory impedance curve is used as the abscissa, and the noise-reduced respiratory impedance curve is used as the abscissa.
  • the data reflected by the respiratory impedance change rate is used as the ordinate, and a curve is obtained by plotting the relationship between the respiratory impedance and the respiratory impedance change rate.
  • An embodiment of the present disclosure provides a device for displaying the relationship between respiratory impedance and respiratory impedance change rate, By obtaining the lung pixel respiratory impedance map of the subject, based on the lung pixel respiratory impedance map, the respiratory impedance curve in the interest domain is obtained, and based on the respiratory impedance curve in the interest domain, the respiratory impedance and respiratory impedance are obtained.
  • the curve of the relationship between the change rate because the lung pixel respiratory impedance map can reflect the changing state of the conductivity of each pixel in the subject's lung area during the predetermined respiratory stage, so that the embodiment of the present disclosure can measure the conductivity at the pixel level.
  • the subject's breathing process is studied, so the research process is more delicate. That is to say, the technical solution provided by the embodiments of the present disclosure can study the respiratory process in a more detailed manner.
  • a computer-readable storage medium is also provided.
  • Program code is stored on the computer-readable storage medium.
  • the program code is executed by a processor, the method described in any one of the above method embodiments is implemented.
  • an electronic device includes a memory and a processor.
  • the memory stores program code that can be run on the processor.
  • the program code is executed by the processor, the above practical method is implemented.
  • the method for presenting the relationship between respiratory impedance and respiratory impedance change rate according to any one of the embodiments.
  • Embodiments of the present disclosure provide a method, device, storage medium and electronic device for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • the subject's lung pixel respiratory impedance map based on the lung pixel respiratory impedance Figure, obtain the respiratory impedance curve on the interest domain.
  • the respiratory impedance curve on the interest domain Based on the respiratory impedance curve on the interest domain, obtain a curve showing the relationship between respiratory impedance and respiratory impedance change rate. Since the lung pixel respiratory impedance map can reflect the subject The changing state of the conductivity of each pixel in the lung area during a predetermined breathing stage enables embodiments of the present disclosure to study the subject's breathing process at the pixel level, so the research process is more refined. That is to say, the technical solution provided by the embodiments of the present disclosure can study the respiratory process in a more detailed manner.
  • the technical solution provided by the embodiments of the present disclosure can not only conduct a more detailed study of the subject's breathing process and conduct an overall functional assessment of the subject's lung area, but also enable pixel-level lung function assessment. Compared with the existing technology, the research accuracy and evaluation accuracy are greatly improved.
  • the disclosed devices and methods can be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented.
  • the units described as separate components may or may not be physically separate.
  • the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be separate. deployed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.
  • each functional unit in various embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above integrated units can be implemented in the form of hardware or software functional units.
  • Integrated units may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as independent products.
  • the technical solution of the present disclosure is essentially or contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to cause an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present disclosure.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .
  • Embodiments of the present disclosure provide a method, device, storage medium and electronic device for presenting the relationship between respiratory impedance and respiratory impedance change rate.
  • the subject's lung pixel respiratory impedance map based on the lung pixel respiratory impedance Figure, obtain the respiratory impedance curve on the interest domain.
  • the respiratory impedance curve on the interest domain Based on the respiratory impedance curve on the interest domain, obtain a curve showing the relationship between respiratory impedance and respiratory impedance change rate. Since the lung pixel respiratory impedance map can reflect the subject The changing state of the conductivity of each pixel in the lung area during a predetermined breathing stage enables embodiments of the present disclosure to study the subject's breathing process at the pixel level, so the research process is more refined. That is to say, the technical solution provided by the embodiments of the present disclosure can study the respiratory process in a more detailed manner.

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Abstract

本公开提供了一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置，涉及生物医学成像技术领域，该方法包括：获取受试者的肺部像素呼吸阻抗图；其中，肺部像素呼吸阻抗图用于反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态；基于肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线；基于兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。

Description

用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置
相关申请的交叉引用
本公开要求享有2022年07月04日提交的名称为“用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置”的中国专利申请CN202210786846.7的优先权，其全部内容通过引用并入本公开中。
技术领域
本公开涉及生物医学成像技术领域，特别地涉及一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置。
背景技术
一些情形下，通过计算流量容积环来研究人体的呼吸过程。该流量容积环能够反映人体在呼吸过程中肺部容积与肺部气体流量之间的关系，然而这种关系仅能从整体上大致反映人体的呼吸过程，存在难以对呼吸过程进行精细研究的技术问题。
发明内容
针对上述技术问题，本公开提出了一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置。
第一方面，本公开实施例提供了一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，方法包括：获取受试者的肺部像素呼吸阻抗图；其中，肺部像素呼吸阻抗图用于反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态；基于肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线；基于兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
第二方面，本公开实施例提供了一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的装置，装置包括：肺部像素呼吸阻抗图获取单元、呼吸阻抗曲线获取单元、曲线获取单元。肺部像素呼吸阻抗图获取单元，配置为获取受试者的肺部像素呼吸阻抗图；其中，肺部像素呼吸阻抗图用于反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态；呼吸阻抗曲线获取单元，配置为基于肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线；曲线获取单元，配置为基于兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
第三方面，本公开实施例提供了一种计算机可读存储介质，计算机可读存储介质上存储有程序代码，程序代码被处理器执行时，实现如上述实施例中任一项 所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法。
第四方面，本公开实施例提供了一种电子设备，电子设备包括存储器、处理器，存储器上存储有可在处理器上运行的程序代码，程序代码被处理器执行时，实现如上述实施例中任一项所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法。
附图说明
通过结合附图阅读下文示例性实施例的详细描述可更好地理解本公开公开的范围。其中所包括的附图是：
图1为通过现有技术所获得的受试者的肺容积变化曲线；
图2为通过现有技术所获得的流量容积环；
图3为本公开实施例的方法流程图；
图4为本公开实施例中受试者在预定呼吸阶段所测得的电阻抗数据变化；
图5为本公开实施例中的全局呼吸阻抗曲线；
图6为图5所对应的全局曲线；
图7为本公开实施例中的受试者的肺部像素呼吸阻抗图；
图8为本公开实施例中所划分的二维兴趣域；
图9为图8所示的兴趣域所对应的呼吸阻抗曲线；
图10为图8所示的兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线；
图11为本公开实施例中所划分的一种三维兴趣域；
图12为图11所示的兴趣域所对应的呼吸阻抗曲线；
图13为图11所示的兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线；
图14为本公开实施例中所划分的另一种三维兴趣域；
图15为图14所示的兴趣域所对应的呼吸阻抗曲线；
图16为图14所示的兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线；
图17为本公开实施例中所划分的另一种三维兴趣域；
图18为图17所示的兴趣域所对应的呼吸阻抗曲线；
图19为图17所示的兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线；以及
图20为本公开实施例的装置结构图。
具体实施方式
为了使本公开的目的、技术方案和优点更加清楚，以下将结合附图及实施例来详细说明本公开的实施方法，借此对本公开如何应用技术手段来解决技术问题，并达成技术效果的实现过程能充分理解并据以实施。
在下面的描述中阐述了很多具体细节以便于充分理解本公开，但是，本公开还可以采用其他不同于在此描述的其他方式来实施，因此，本公开的保护范围并不受下面公开的具体实施例的限制。
一些情形下，通常通过计算流量容积环来研究人体的呼吸过程。该流量容积环能够反映人体在呼吸过程中肺部容积与肺部气体流量之间的关系，而这种关系仅能从整体上大致反映人体的呼吸过程。若需要对呼吸过程进行进一步精细研究，现有的流量容积环无法满足目前的科研需求。
实施例一
现有技术通过肺功能仪来获得受试者的整体肺功能信息，例如，可以获得如图1所示的受试者的肺容积变化曲线。图1具体反映了一受试者按照正常潮气、深吸气、尽力尽快呼气的呼吸过程进行检测的肺容积变化。而受试者肺部的气体流量可以看作是上述肺容积的变化率，将反映肺容积和肺容积的变化率之间的关系的曲线称为流量容积环。即通过图1的肺容积变化曲线，能够进一步获得图2所示的流量容积环。现有技术通过该流量容积环只能在宏观上大致研究受试者的呼吸过程及评估受试者的整体肺功能情况，这对后续的临床应用提供的信息非常有限。
针对上述技术问题，本公开实施例提供了一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，如图3所示，方法包括：步骤S101、步骤S102和步骤S103。
步骤S101，获取受试者的肺部像素呼吸阻抗图。肺部像素呼吸阻抗图用于反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态。
为了方便操作，本实施例中，获取受试者的肺部像素呼吸阻抗图，包括：获取受试者的胸腔EIT测量数据；对胸腔EIT测量数据进行图像重建，获得胸腔像素阻抗图；对胸腔像素阻抗图进行像素筛选，获得肺部像素呼吸阻抗图。
胸腔EIT测量数据的测量区域是整个胸腔，主要包括肺、心脏、主动脉等生理结构，这些结构在呼吸过程中均会发生电阻抗数值的动态变化。因此，为精确地研究呼吸过程，需要对胸腔像素阻抗图进行像素筛选，分离得到肺部像素呼吸阻抗图。
本实施例中，胸腔像素阻抗图包括：受试者胸腔区域中的每个像素的阻抗曲线；其中，胸腔区域中的每个像素的阻抗曲线用于反映该像素在预定呼吸阶段的 电导率的变化状态。
为了快速、准确地获得肺部像素呼吸阻抗图，本实施例对胸腔像素阻抗图进行像素筛选，获得肺部像素呼吸阻抗图，包括：对胸腔区域中的每个像素的阻抗曲线取时域平均值，获得胸腔区域中所有像素的阻抗均值集合；从胸腔区域中所有像素的阻抗均值集合中筛选出数值小于预设阈值的阻抗均值，获得筛选后的阻抗均值集合；从胸腔像素阻抗图中过滤掉筛选后的阻抗均值集合对应的像素的阻抗曲线，获得肺部像素呼吸阻抗图。
其中，预设阈值可以根据实际情况设定，例如，将胸腔区域中所有像素的阻抗均值按从大到小排列，筛选出后20％的像素，将其认定为非肺部像素，将非肺部像素过滤掉，剩下的前80％的像素即为肺部像素。
在正常呼吸状态下，肺内气体体积随正常潮气周期性增减，流量与容积的对应关系反映了肺功能情况和状态信息，因此流量容积环可以用于判断肺的功能状态，如图2所示。在EIT(Electrical Impedance Tomography，电阻抗断层成像)测量中，所测得的阻抗值的大小可以近似线性地反映肺内气体容积的大小，相对应地，阻抗值的变化率也可以反映容积的改变即肺内气体流量的大小。
EIT是一种无辐射、非侵入、低成本、可功能成像的技术。EIT的基本原理是，按照不同的电极排布方案，采用多种激励方式对人体施加低于细胞兴奋阈值的安全电流，然后通过扫描阵列电极测得体表的电压分布数据，进而应用基于电磁场逆问题求解的图像重构算法，最终反演得到体内电导率分布状态或变化状态的二维或三维图像。
生物体或生物组织、生物器官、生物细胞在低于兴奋阈值的安全电流作用下所表现出的阻抗变化称为生物电阻抗。正常状态下，人体各组织的阻抗差异较大；当生理病理状态发生变化时，各组织的电导率值也随之改变。呼吸过程中，肺内气体含量出现显著的周期性改变；肺内气体充盈程度不同时，其阻抗值也相应变化。因此，电阻抗测量能够以较高的时间分辨率探知肺内气体含量及其时空分布。
EIT可以连续动态地监测受试者的肺部通气情况，实现肺功能参数的计算。在一示例性实施例中，EIT可以方便、准确地重建得到肺部像素级的电导率分布。基于EIT的上述优点，本实施例采用对受试者的胸腔进行EIT测量的方式来获得胸腔EIT测量数据，进而方便、准确地获得受试者的肺部像素呼吸阻抗图，而该肺部像素呼吸阻抗图中所包含的每个像素的阻抗曲线的走向和幅度都蕴含着一定的呼吸过程信息。
在一示例性实施例中，首先受试者完成肺功能测试，按照正常潮气、深吸气、尽力尽快呼气的呼吸顺序测得肺内气体体积随时间的变化曲线，即肺容积变化曲线如图1所示。与此同时，受试者需佩戴EIT电极带，完成EIT的同步测量。EIT得到的通气所致的电阻抗数据变化如图4所示。可以看到，二者具有高度的一致性。
之后，对通过EIT测量所获得的胸腔EIT测量数据进行图像重建，在一示例性实施例中，对胸腔EIT测量数据中深吸气、尽力尽快呼气部分对应的测量数据进行图像重建，获得胸腔像素阻抗图，对该胸腔像素阻抗图进行像素筛选，从所有胸腔像素中筛选出肺部像素，获得肺部像素呼吸阻抗图。采用上述方式所获得的肺部像素呼吸阻抗图如图7所示。从图7中可以看到，不同像素点对应位置的肺在同一呼吸过程中的活动差异较大。因此，分区研究肺部不同区域的特性可以得到更加精细的呼吸过程信息。
步骤S102，基于肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线。
如图7所示，本实施例中，肺部像素呼吸阻抗图包括：肺部区域中每个像素的阻抗曲线；其中，肺部区域中每个像素的阻抗曲线用于反映该像素在预定呼吸阶段的电导率的变化状态。本实施例中基于肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线，包括：对兴趣域上的每个像素的阻抗曲线进行叠加，获得兴趣域上的呼吸阻抗曲线。
本实施例中所述的兴趣域(Region of Interest,ROI)可以是一组像素的集合，也可以是单个像素，肺部区域可以被划分到不同的兴趣域中。本实施例对兴趣域的划分包括在二维电极平面内进行四象限等分，以及在三维成像区域内进行不同方式的分区计算，以实现在不同兴趣域上对呼吸过程的研究。
即本实施例所述的兴趣域可以采用以下两种方式获取。
第一种方式为，在二维区域上获取二维兴趣域：对肺部区域中的每个像素的阻抗曲线取平均值，获得肺部区域中所有像素的阻抗均值集合；对肺部区域中所有像素的阻抗均值集合在二维平面上进行图像重建，获得二维电导率分布图像；对二维电导率分布图像的成像区域进行第一预设规则的等分，获得多个二维等分区域；获取每个二维等分区域作为兴趣域。
在一示例性实施例中，对图7中的每个像素的阻抗曲线取平均值，获得肺部区域中所有像素的阻抗均值集合，对该肺部区域中所有像素的阻抗均值集合在二维平面上进行图像重建，获得如图8所示的二维电导率分布图像。可以看到，图8整体呈现两个半月形，且中心活动较为活跃。对该二维电导率分布图像的成像区域进行四象限等分，获得四个二维等分区域ROI1、ROI2、ROI3和ROI4，其中，上述每个二维等分区域为一个兴趣域。
之后，将上述图8中每个兴趣域上的每个像素的阻抗曲线进行叠加，即可获得该兴趣域上的呼吸阻抗曲线。图8中四个兴趣域所对应的四条呼吸阻抗曲线如图9所示。
第二种方式为，在三维区域上获取三维兴趣域：基于肺部像素呼吸阻抗图，获得肺部图像三维模型；对肺部图像三维模型在预设面上进行第二预设规则的等分，获得多个三维等分区域；获取每个三维等分区域作为兴趣域。三维兴趣域能 够更全面地观察到肺在空间上的功能差异，从而能够更加精细地研究呼吸过程。
在一示例性实施例中，基于图7所示的肺部像素呼吸阻抗图，可以获得肺部图像三维模型，为了更方便地描述对该三维模型的划分，本实施例采用示意图的方式来展示该肺部图像三维模型，如图11、图14和图17所示。
为了更全面地观察到肺在空间上的功能差异，将肺在冠状面分割为四块，相对应地，如图11所示，将肺部图像三维模型示意图在三维空间冠状面均匀划分为四个兴趣域，并标记为1、2、3、4。将该四个兴趣域中每个兴趣域上的每个像素的阻抗曲线进行叠加，即可获得该兴趣域上的呼吸阻抗曲线。图11中四个兴趣域所对应的四条呼吸阻抗曲线如图12所示。
在一示例性实施例中，为了更全面地观察到肺在空间上的功能差异，将肺在矢状面、冠状面、水平面分别分割为四块，相对应地，如图14所示，将肺部图像三维模型示意图在三维空间冠状面均匀划分为八个兴趣域，并标记为1、2、3、4、5、6、7、8。将该八个兴趣域中每个兴趣域上的每个像素的阻抗曲线进行叠加，即可获得该兴趣域上的呼吸阻抗曲线。图14中四个兴趣域所对应的八条呼吸阻抗曲线如图15所示。
在一示例性实施例中，为了更全面地观察到肺在空间上的功能差异，将肺在矢状面、冠状面分别切割为六块，水平面分割为四块，相对应地，如图17所示，将肺部图像三维模型示意图在三维空间冠状面均匀划分为十二个兴趣域，并标记为1、2、3、4、5、6、7、8、9、10、11、12。将该十二个兴趣域中每个兴趣域上的每个像素的阻抗曲线进行叠加，即可获得该兴趣域上的呼吸阻抗曲线。图17中四个兴趣域所对应的八条呼吸阻抗曲线如图18所示。
步骤S103，基于兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
本实施例中，基于兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，包括：对兴趣域上的呼吸阻抗曲线进行降噪处理，获得降噪后的呼吸阻抗曲线；对降噪后的呼吸阻抗曲线求解导数，获得呼吸阻抗变化率；对呼吸阻抗变化率进行降噪处理，获得降噪后的呼吸阻抗变化率；基于降噪后的呼吸阻抗曲线和降噪后的呼吸阻抗变化率，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
本实施例中，基于降噪后的呼吸阻抗曲线和降噪后的呼吸阻抗变化率，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，包括：以降噪后的呼吸阻抗曲线所反映的数据作为横坐标，以降噪后的呼吸阻抗变化率所反映的数据作为纵坐标，作图获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
在一示例性实施例中，以图9中的其中一条呼吸阻抗曲线为例，对该条呼吸阻抗曲线进行降噪处理后，再对其求解导数，即可获得该条呼吸阻抗曲线所对应 的呼吸阻抗变化率。对该呼吸阻抗变化率也进行降噪处理，之后，以降噪后的呼吸阻抗曲线所反映的数据作为横坐标，以降噪后的呼吸阻抗变化率所反映的数据作为纵坐标，作图获得与该条呼吸阻抗曲线所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。图10示出了图8所示的四个兴趣域所分别对应的四条用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。该曲线可类似于图2所示的流量容积环对受试者的呼吸过程进行研究。所不同的是，图2的流量容积环只能在整体上大致反映呼吸过程，而本实施例所获得的上述用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线能够在像素级别上研究受试者的呼吸过程。
从图10中可以看到，不同肺区之间活动差别较大，反映的呼吸过程也不相同，ROI1所在部位的肺在呼吸过程中最为活跃，ROI 2所在部位的肺在呼吸过程中最不活跃。
采用同样的处理方式，分别可获得图11所示的兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线、图14所示的兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线和图17所示的兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，分别如图13、图16和图19所示。
需要说明的是，本实施例中的兴趣域也可以为受试者的整体肺部区域。当兴趣域为受试者的整体肺部区域时，将图7中所示的所有像素的阻抗曲线进行叠加，可获得全局呼吸阻抗曲线，如图5所示。对图5所示的全局呼吸阻抗曲线采用与上述同样的处理方式，可获得全局曲线，如图6所示。图6所示的全局曲线能够对受试者的呼吸过程进行整体上的全局研究。
需要说明的是，本实施例中的兴趣域也可以为受试者肺部区域中的某个像素。当兴趣域为受试者肺部区域中的某个像素时，该兴趣域的呼吸阻抗曲线即为该像素的阻抗曲线。
通过本实施例所获得的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线不仅能够用于更精细地研究受试者的呼吸过程，而且还能够用于对受试者的肺功能进行评估。
即在本实施例中，根据步骤S103所获得的各兴趣域所对应的用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，还可以对该兴趣域上的肺功能进行评估。
在一示例性实施例中，从图13中可以看到，不同肺区之间活动差别较大，整体来看，ROI2和ROI4所代表的肺的上半部在呼吸过程中更为活跃。
从图16中可以看到，不同肺区之间活动差别较大，整体来看，ROI1、ROI2、ROI3和ROI4所代表的肺的前半部在呼吸过程中更为活跃。
从图19中可以看到，不同肺区之间活动差别较大，整体来看，ROI2、ROI5、ROI8和ROI11所代表的肺的前半部在呼吸过程中更为活跃。
本实施例中，图6所示的全局曲线还能够对受试者的肺功能进行整体上的全局评估。即本公开实施例提供的技术方案，给出了全局曲线和某个兴趣域上的局部曲线的计算方法，能够从全局和局部两个层次对受试者的呼吸过程及肺功能进行全面、精确地研究和评估。
此外，对于上述全局曲线，通过现有技术获得的肺功能检查结果可以作为标准来验证本实施例所获得的EIT结果的可靠性；对于局部曲线，EIT结果则展示出了局部的肺功能特征。
本公开实施例提供的一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，通过获取受试者的肺部像素呼吸阻抗图，基于该肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线，基于该兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，由于肺部像素呼吸阻抗图能够反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态，使得本公开实施例能够在像素级别上对受试者的呼吸过程进行研究，因此研究过程更加精细。即本公开实施例提供的技术方案，能够更加精细地对呼吸过程进行研究。
实施例二
与上述方法实施例相对应地，本公开还提供一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的装置，如图20所示，装置包括：肺部像素呼吸阻抗图获取单元201、呼吸阻抗曲线获取单元202、曲线获取单元203。
肺部像素呼吸阻抗图获取单元201，配置为获取受试者的肺部像素呼吸阻抗图；其中，肺部像素呼吸阻抗图用于反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态。
呼吸阻抗曲线获取单元202，配置为基于肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线。
曲线获取单元203，配置为基于兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
本实施例中，肺部像素呼吸阻抗图获取单元201包括：测量数据获取单元、图像重建单元、像素筛选单元。
测量数据获取单元，配置为获取受试者的胸腔EIT测量数据；图像重建单元，配置为对胸腔EIT测量数据进行图像重建，获得胸腔像素阻抗图；像素筛选单元，配置为对胸腔像素阻抗图进行像素筛选，获得肺部像素呼吸阻抗图。
本实施例中，胸腔像素阻抗图包括：受试者胸腔区域中的每个像素的阻抗曲线；其中，胸腔区域中的每个像素的阻抗曲线用于反映该像素在预定呼吸阶段的电导率的变化状态。
本实施例中，像素筛选单元采用以下方式对胸腔像素阻抗图进行像素筛选， 获得肺部像素呼吸阻抗图：对胸腔区域中的每个像素的阻抗曲线取时域平均值，获得胸腔区域中所有像素的阻抗均值集合；从胸腔区域中所有像素的阻抗均值集合中筛选出数值小于预设阈值的阻抗均值，获得筛选后的阻抗均值集合；从胸腔像素阻抗图中过滤掉筛选后的阻抗均值集合对应的像素的阻抗曲线，获得肺部像素呼吸阻抗图。
本实施例中，肺部像素呼吸阻抗图包括：肺部区域中每个像素的阻抗曲线；其中，肺部区域中每个像素的阻抗曲线用于反映该像素在预定呼吸阶段的电导率的变化状态。
本实施例中，呼吸阻抗曲线获取单元202采用以下方式获得兴趣域上的呼吸阻抗曲线：对兴趣域上的每个像素的阻抗曲线进行叠加，获得兴趣域上的呼吸阻抗曲线。
本实施例中，兴趣域采用以下方式获取：对肺部区域中的每个像素的阻抗曲线取平均值，获得肺部区域中所有像素的阻抗均值集合；对肺部区域中所有像素的阻抗均值集合在二维平面上进行图像重建，获得二维电导率分布图像；对二维电导率分布图像的成像区域进行第一预设规则的等分，获得多个二维等分区域；获取每个二维等分区域作为兴趣域。
本实施例中，兴趣域采用以下方式获取：基于肺部像素呼吸阻抗图，获得肺部图像三维模型；对肺部图像三维模型在预设面上进行第二预设规则的等分，获得多个三维等分区域；获取每个三维等分区域作为兴趣域。
本实施例中，曲线获取单元203包括：第一降噪单元、导数求解单元、第二降噪单元、曲线获取子单元。
第一降噪单元，配置为对兴趣域上的呼吸阻抗曲线进行降噪处理，获得降噪后的呼吸阻抗曲线；导数求解单元，配置为对降噪后的呼吸阻抗曲线求解导数，获得呼吸阻抗变化率；第二降噪单元，配置为对呼吸阻抗变化率进行降噪处理，获得降噪后的呼吸阻抗变化率；曲线获取子单元，配置为基于降噪后的呼吸阻抗曲线和降噪后的呼吸阻抗变化率，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
本实施例中，曲线获取子单元采用以下方式获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线：以降噪后的呼吸阻抗曲线所反映的数据作为横坐标，以降噪后的呼吸阻抗变化率所反映的数据作为纵坐标，作图获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
上述装置的工作原理、工作流程等涉及具体实施方式的内容可参见本公开所提供的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法的具体实施方式，此处不再对相同的技术内容进行详细描述。
本公开实施例提供的一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的装置， 通过获取受试者的肺部像素呼吸阻抗图，基于该肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线，基于该兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，由于肺部像素呼吸阻抗图能够反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态，使得本公开实施例能够在像素级别上对受试者的呼吸过程进行研究，因此研究过程更加精细。即本公开实施例提供的技术方案，能够更加精细地对呼吸过程进行研究。
实施例三
根据本公开的实施例，还提供了一种计算机可读存储介质，计算机可读存储介质上存储有程序代码，程序代码被处理器执行时，实现如上述方法实施例中任一项所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法。
实施例四
根据本公开的实施例，还提供了一种电子设备，电子设备包括存储器、处理器，存储器上存储有可在处理器上运行的程序代码，程序代码被处理器执行时，实现如上述实方法施例中任一项所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法。
本公开实施例提供的一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法、装置、存储介质及电子设备，通过获取受试者的肺部像素呼吸阻抗图，基于该肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线，基于该兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，由于肺部像素呼吸阻抗图能够反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态，使得本公开实施例能够在像素级别上对受试者的呼吸过程进行研究，因此研究过程更加精细。即本公开实施例提供的技术方案，能够更加精细地对呼吸过程进行研究。
本公开实施例所提供的技术方案，不仅能够对受试者的呼吸过程进行更加精细地研究、对受试者的肺部区域进行整体上的功能评估，而且能够实现像素级的肺功能评估，与现有技术相比，大大提高了研究精度与评估精度。
在本公开所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分 布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本公开实施例方案的目的。
另外，在本公开各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本公开的技术方案本质上或者说对现有技术做出贡献的部分，或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台电子设备(可以是个人计算机，服务器，或者网络设备等)执行本公开各个实施例方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
本公开实施例提供的一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法、装置、存储介质及电子设备，通过获取受试者的肺部像素呼吸阻抗图，基于该肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线，基于该兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，由于肺部像素呼吸阻抗图能够反映受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态，使得本公开实施例能够在像素级别上对受试者的呼吸过程进行研究，因此研究过程更加精细。即本公开实施例提供的技术方案，能够更加精细地对呼吸过程进行研究。
虽然本公开所公开的实施方式如上，但所述的内容只是为了便于理解本公开而采用的实施方式，并非用以限定本公开。任何本公开所属技术领域内的技术人员，在不脱离本公开所公开的精神和范围的前提下，可以在实施的形式上及细节上作任何的修改与变化，但本公开的保护范围，仍须以所附的权利要求书所界定的范围为准。

Claims (11)

  1. 一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，包括：
    获取受试者的肺部像素呼吸阻抗图；其中，所述肺部像素呼吸阻抗图用于反映所述受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态；
    基于所述肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线；
    基于所述兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
  2. 根据权利要求1所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，其中，所述获取受试者的肺部像素呼吸阻抗图，包括：
    获取所述受试者的胸腔EIT测量数据；
    对所述胸腔EIT测量数据进行图像重建，获得胸腔像素阻抗图；
    对所述胸腔像素阻抗图进行像素筛选，获得所述肺部像素呼吸阻抗图。
  3. 根据权利要求2所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，其中，所述胸腔像素阻抗图包括：所述受试者胸腔区域中的每个像素的阻抗曲线；其中，所述胸腔区域中的每个像素的阻抗曲线用于反映该像素在所述预定呼吸阶段的电导率的变化状态；所述对所述胸腔像素阻抗图进行像素筛选，获得所述肺部像素呼吸阻抗图，包括：
    对所述胸腔区域中的每个像素的阻抗曲线取时域平均值，获得所述胸腔区域中所有像素的阻抗均值集合；
    从所述胸腔区域中所有像素的阻抗均值集合中筛选出数值小于预设阈值的阻抗均值，获得筛选后的阻抗均值集合；
    从所述胸腔像素阻抗图中过滤掉所述筛选后的阻抗均值集合对应的像素的阻抗曲线，获得所述肺部像素呼吸阻抗图。
  4. 根据权利要求1所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，其中，所述肺部像素呼吸阻抗图包括：所述肺部区域中每个像素的阻抗曲线；其中，所述肺部区域中每个像素的阻抗曲线用于反映该像素在所述预定呼吸阶段的电导率的变化状态；所述基于所述肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线，包括：
    对所述兴趣域上的每个像素的阻抗曲线进行叠加，获得所述兴趣域上的呼吸 阻抗曲线。
  5. 根据权利要求4所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，其中，所述兴趣域采用以下方式获取：
    对所述肺部区域中的每个像素的阻抗曲线取平均值，获得所述肺部区域中所有像素的阻抗均值集合；
    对所述肺部区域中所有像素的阻抗均值集合在二维平面上进行图像重建，获得二维电导率分布图像；
    对所述二维电导率分布图像的成像区域进行第一预设规则的等分，获得多个二维等分区域；
    获取每个所述二维等分区域作为所述兴趣域。
  6. 根据权利要求4所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，其中，所述兴趣域采用以下方式获取：
    基于所述肺部像素呼吸阻抗图，获得肺部图像三维模型；
    对所述肺部图像三维模型在预设面上进行第二预设规则的等分，获得多个三维等分区域；
    获取每个所述三维等分区域作为所述兴趣域。
  7. 根据权利要求1所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，其中，所述基于所述兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，包括：
    对所述兴趣域上的呼吸阻抗曲线进行降噪处理，获得降噪后的呼吸阻抗曲线；
    对所述降噪后的呼吸阻抗曲线求解导数，获得呼吸阻抗变化率；
    对所述呼吸阻抗变化率进行降噪处理，获得降噪后的呼吸阻抗变化率；
    基于所述降噪后的呼吸阻抗曲线和所述降噪后的呼吸阻抗变化率，获得所述用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
  8. 根据权利要求7所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法，其中，所述基于所述降噪后的呼吸阻抗曲线和所述降噪后的呼吸阻抗变化率，获得所述用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线，包括：
    以所述降噪后的呼吸阻抗曲线所反映的数据作为横坐标，以所述降噪后的呼吸阻抗变化率所反映的数据作为纵坐标，作图获得所述用于呈现呼吸阻抗与呼吸 阻抗变化率之间关系的曲线。
  9. 一种用于呈现呼吸阻抗与呼吸阻抗变化率关系的装置，包括：
    肺部像素呼吸阻抗图获取单元，配置为获取受试者的肺部像素呼吸阻抗图；其中，所述肺部像素呼吸阻抗图用于反映所述受试者肺部区域中的每个像素在预定呼吸阶段的电导率的变化状态；
    呼吸阻抗曲线获取单元，配置为基于所述肺部像素呼吸阻抗图，获得兴趣域上的呼吸阻抗曲线；
    曲线获取单元，配置为基于所述兴趣域上的呼吸阻抗曲线，获得用于呈现呼吸阻抗与呼吸阻抗变化率之间关系的曲线。
  10. 一种计算机可读存储介质，所述计算机可读存储介质上存储有程序代码，所述程序代码被处理器执行时，实现如权利要求1至8中任一项所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法。
  11. 一种电子设备，所述电子设备包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的程序代码，所述程序代码被所述处理器执行时，实现如权利要求1至8中任一项所述的用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法。
PCT/CN2023/102446 2022-07-04 2023-06-26 用于呈现呼吸阻抗与呼吸阻抗变化率关系的方法及装置 WO2024007877A1 (zh)

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