Dynamic X-ray image matching method and system and dynamic X-ray camera

Abstract

The invention relates to a dynamic X-ray image matching method and system and a dynamic X-ray camera, and relates to the technical field of dynamic X-ray image matching. The dynamic X-ray image matching method comprises the following steps: determining whether a plurality of dynamic X-ray two-dimensional chest images in the breathing process comprise a set target or not; if yes, a set target X-ray two-dimensional chest image corresponding to the set target is extracted from the multiple dynamic X-ray two-dimensional chest images in the breathing process; matching is carried out on the basis of a lung airflow and blood flow mixed image sequence corresponding to the set target X-ray two-dimensional chest image or a lung airflow and blood flow mixed image and a lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple moments in a breath-holding state; and obtaining lung ventilation corresponding to the set target X-ray two-dimensional chest image. According to the embodiment of the invention, dynamic X-ray image matching can be realized, and then lung ventilation is determined.

Description

Technical Field

[0001] This disclosure relates to the field of dynamic X-ray image matching technology, and in particular to a dynamic X-ray image matching method, system and dynamic X-ray camera. Background Technology

[0002] Currently, the routine pulmonary function test (PFT) is the gold standard for grading and diagnosing chronic obstructive pulmonary disease (COPD). However, as the "gold standard" for diagnosis and grading, PFT is not sensitive to early COPD diagnosis, easily leading to missed diagnoses. Furthermore, PFT only provides overall respiratory function parameters and cannot accurately reflect changes in lung tissue as COPD progresses through grading, subtyping, and disease progression, nor can it provide direct morphological information about lung tissue.

[0003] Static quantitative parameters based on deep inspiration phase CT images (such as airway wall thickness, area, and cross-sectional area corresponding to airway morphology) have become important imaging indicators for COPD diagnosis and treatment. However, the pathophysiology of COPD is characterized by airflow limitation during respiration, and lung respiratory motion directly reflects the patient's respiratory capacity. The aforementioned single static quantitative parameters from CT images can only reflect the state of lung tissue structure during deep inspiration phase, lacking corresponding dynamic quantitative parameters and information related to dynamic changes in airflow, which will seriously hinder COPD screening. Furthermore, due to limitations such as CT radiation dose and cost, current chest CT images based on CT imaging only capture images corresponding to deep inspiration and deep expiration phases. Consequently, parameter response maps constructed based on biphasic respiratory CT images can only locate healthy lung areas, emphysematous areas, and functional small airway areas, similarly lacking corresponding dynamic quantitative parameters of lung respiratory motion and related information on dynamic changes in airway morphology and ventilation flow.

[0004] Furthermore, while biphasic CT images of chest breathing corresponding to deep inspiration and deep expiration have high three-dimensional spatial resolution, these images also lack sufficient temporal information and cannot reflect lung respiratory motion and ventilation status directly related to COPD pathophysiology during respiration. Compared to biphasic CT images of chest breathing and static monophasic X-ray spot images, digital dynamic X-ray imaging can acquire multiphasic (multi-time series) X-ray fluoroscopy images of the chest corresponding to the respiratory process, providing lung motion information corresponding to the complete respiratory cycle. This allows for the assessment of lung respiratory motion and ventilation status directly related to COPD pathophysiology, enabling COPD screening or assessment. Therefore, compared to chest CT, digital dynamic chest X-ray imaging, the most widely used and fastest imaging method in routine chest examinations, is expected to become the preferred imaging device for COPD screening or diagnosis.

[0005] Based on the above discussion, the existing technology lacks a lung ventilation detection scheme based on dynamic multiple X-ray two-dimensional chest images during the breathing process, corresponding to the maximum expiration time N seconds, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of the quiet expiration, and the maximum force and fastest speed exhalation to the residual volume at the end of the quiet expiration. This greatly hinders the in-depth research of COPD. Furthermore, the core of the lung ventilation detection technology based on dynamic multi-X-ray two-dimensional chest images during respiration, corresponding to the Nth second of maximum expiration, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of quiet expiration, and the maximum force and fastest speed exhalation to the residual air position within the maximum force and fastest speed exhalation ... Summary of the Invention

[0006] This disclosure proposes a dynamic X-ray image matching method, system, and corresponding technical solution for a dynamic X-ray camera.

[0007] According to one aspect of this disclosure, a dynamic X-ray image matching method is provided, comprising: determining whether the dynamic multiple two-dimensional chest X-ray images during the breathing process include one or more preset targets, such as the maximum expiration at the Nth second, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of the quiet expiration, and the maximum force and fastest speed exhalation to the residual air position within the maximum force inhalation to the total lung capacity at the end of the quiet expiration; wherein, N is greater than or equal to 1 and is a positive integer; if so, extracting the preset target X-ray two-dimensional chest image corresponding to the preset target from the dynamic multiple two-dimensional chest X-ray images during the breathing process; matching the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the preset target X-ray two-dimensional chest image with the lung blood flow image sequence corresponding to the multiple two-dimensional chest X-ray images at multiple times under breath-holding state to obtain the lung ventilation corresponding to the preset target X-ray two-dimensional chest image.

[0008] Preferably, the step of matching the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image with the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding state to obtain the lung airflow corresponding to the set target X-ray two-dimensional chest image includes: matching the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image based on multiple first heart edge distances of the lung airflow and blood flow mixed image sequence or the first heart edge distance of the lung airflow and blood flow mixed image and multiple second heart edge distances of the lung blood flow image sequence.

[0009] Preferably, the matching of the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image based on multiple first heart edge distances or first heart edge distances of the lung airflow and blood flow mixed image and multiple second heart edge distances of the lung blood flow image sequence includes: calculating multiple heart edge distance differences corresponding to each of the multiple first heart edge distances or between the first heart edge distance and the multiple second heart edge distances; matching the lung airflow and blood flow mixed image corresponding to the first heart edge distance with the smallest difference of the multiple heart edge distances with the lung blood flow image corresponding to the second heart edge distance, and determining the corresponding matching relationship between the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image and the lung blood flow image sequence.

[0010] Preferably, determining the plurality of first cardiac edge distances / first cardiac edge distances includes: determining the plurality of first cardiac edge distances / first cardiac edge distances corresponding to the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image or the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image corresponding to the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image, respectively, based on the right lung mask edge image and left lung mask edge image of the first fixed X-ray image to be registered of the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image.

[0011] Preferably, the step of determining multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image based on the right lung mask edge image and left lung mask edge image of the first fixed X-ray image to be registered corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image or the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image, including: determining the corresponding right cardiac border point and left cardiac border point according to the right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image / the lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung airflow and blood flow mixed image / the lung airflow and blood flow mixed image, and determining the multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence.

[0012] Preferably, determining the plurality of second cardiac border distances includes: determining the plurality of second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and the left lung mask edge image of the second right lung X-ray image and the second left lung X-ray image to be registered corresponding to the lung blood flow image sequence.

[0013] Preferably, determining the multiple second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence includes: determining the corresponding right cardiac border point and left cardiac border point based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung blood flow image to determine the multiple second cardiac border distances corresponding to the lung blood flow image sequence.

[0014] Preferably, determining the lung airflow-blood flow mixed image sequence or lung airflow-blood flow mixed image includes: segmenting the lung field of the predetermined target X-ray two-dimensional chest image to obtain a plurality of first right lung mask images, a plurality of first left lung mask images, and one or more first mask images of a plurality of first lung mask images; extracting a plurality of first right lung X-ray images, a plurality of first left lung X-ray images, and one or more first lung X-ray images corresponding to the plurality of first right lung mask images, the plurality of first left lung mask images, and one or more first mask images of a plurality of first lung mask images; and extracting a plurality of first right lung X-ray images, a plurality of first left lung X-ray images, and one or more first X-ray images of a plurality of first lung X-ray images corresponding to the plurality of first right lung mask images, the plurality of first left lung mask images, and one or more first X-ray images of a plurality of first lung X-ray images at adjacent or predetermined time intervals. A right lung X-ray image, multiple first left lung X-ray images, and one or more first lung X-ray images are registered to obtain multiple first right lung X-ray registered images, multiple first left lung X-ray registered images, and one or more first lung X-ray registered images. Pixel-level subtraction processing is performed on multiple first right lung X-ray registered images, multiple first left lung X-ray registered images, and one or more first lung X-ray registered images that are adjacent or at a set time interval to determine the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the acquired dynamic multiple X-ray two-dimensional chest images during the respiratory process.

[0015] Preferably, determining the pulmonary blood flow image sequence includes: segmenting the lung fields of multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state to obtain multiple second right lung mask images, multiple second left lung mask images, and one or more second mask images of multiple second lung mask images; extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and one or more second lung X-ray images of the multiple second right lung mask images, multiple second left lung mask images, and one or more second lung mask images corresponding to the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung X-ray images of the multiple lung mask images; and extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and multiple second lung X-ray images at adjacent or predetermined time intervals. One or more second lung X-ray images are registered or Gaussian blurred to obtain multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and one or more second lung X-ray registered or Gaussian blurred images. Pixel-level subtraction is performed on multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and one or more second lung X-ray registered or Gaussian blurred images at adjacent times or at set time intervals to determine the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under the breath-holding state.

[0016] Preferably, the lung ventilation corresponding to the target X-ray two-dimensional chest image is obtained by matching the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding conditions. This includes: registering the left lung airflow and blood flow mixed image sequence in the lung airflow and blood flow mixed image sequence with its matched left lung blood flow image, and registering the left lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence with its matched left lung blood flow image to obtain a left lung airflow and blood flow mixed registration image sequence / left lung airflow and blood flow mixed registration image; performing pixel-level subtraction processing on the left lung airflow and blood flow mixed registration image sequence / left lung airflow and blood flow mixed registration image and its matched left lung blood flow image to determine dynamic left lung ventilation; and / or, respectively, the lung airflow and blood flow mixed image sequence in the lung airflow and blood flow mixed image sequence The right lung airflow and blood flow mixed image sequence is registered with its matching right lung blood flow image; the right lung airflow and blood flow mixed image sequence and its matching right lung blood flow image are registered to obtain a right lung airflow and blood flow mixed registration image sequence / right lung airflow and blood flow mixed registration image; pixel-level subtraction processing is performed on the right lung airflow and blood flow mixed registration image sequence / right lung airflow and blood flow mixed registration image and its matching right lung blood flow image to determine dynamic right lung ventilation; and / or, the right lung airflow and blood flow mixed image sequence and its matching lung blood flow image are registered with its matching lung blood flow image to obtain a lung airflow and blood flow mixed registration image sequence / lung airflow and blood flow mixed registration image; pixel-level subtraction processing is performed on the lung airflow and blood flow mixed registration image sequence / lung airflow and blood flow mixed registration image and its matching lung blood flow image to determine lung ventilation.

[0017] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a determining unit, configured to determine whether the dynamic multiple two-dimensional chest X-ray images during the breathing process include one or more preset targets, such as the maximum expiration at the Nth second, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of the quiet expiration, and the maximum force and fastest speed exhalation to the residual air position within the maximum force inhalation to the total lung capacity at the end of the quiet expiration; wherein N is greater than or equal to 1 and is a positive integer; an extraction unit, configured to extract the preset target X-ray two-dimensional chest image corresponding to the preset target from the dynamic multiple two-dimensional chest X-ray images during the breathing process if the preset target is included; and a matching unit, configured to match the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the preset target X-ray two-dimensional chest image with the lung blood flow image sequence corresponding to the multiple two-dimensional chest X-ray images at multiple times under breath-holding conditions, to obtain the lung ventilation corresponding to the preset target X-ray two-dimensional chest image.

[0018] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the dynamic X-ray image matching method described above.

[0019] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described dynamic X-ray image matching method.

[0020] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a computer program product, the computer program product being configured with computer program instructions that, when executed by a processor, implement the aforementioned dynamic X-ray image matching method.

[0021] According to one aspect of this disclosure, a dynamic X-ray camera is provided, comprising: a dynamic X-ray image matching system as described above.

[0022] This disclosure proposes a dynamic X-ray image matching method, system, and corresponding technical solution for a dynamic X-ray camera. This solution addresses the technical problem of matching a sequence of mixed lung airflow and blood flow images corresponding to the Nth second of maximal expiration, the quiet expiration phase, the maximum force inhalation to total lung capacity at the end of quiet expiration, and the continuous exhalation with maximum force and speed to residual air within the maximum force and speed of inhalation to total lung capacity at the end of quiet expiration, with a sequence of lung blood flow images corresponding to multiple two-dimensional chest X-ray images taken at multiple moments during breath-holding. This solves the existing technical problem of lacking a lung ventilation detection solution based on dynamic two-dimensional chest X-ray images during breathing, specifically the Nth second of maximal expiration, the quiet expiration phase, the maximum force inhalation to total lung capacity at the end of quiet expiration, and the continuous exhalation with maximum force and speed of inhalation to residual air within the maximum force and speed of inhalation to total lung capacity at the end of quiet expiration. This has significantly hindered in-depth research on COPD.

[0023] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0025] Figure 1A flowchart illustrating a dynamic X-ray image matching method according to an embodiment of the present disclosure is shown; Figure 2 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment; Figure 3 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. Detailed Implementation

[0026] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0027] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0028] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0029] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0030] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0031] In addition, this disclosure also provides a dynamic X-ray image matching device or system, electronic device, computer-readable storage medium, and program, all of which can be used to implement any of the dynamic X-ray image matching methods provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding section of the method and will not be repeated here.

[0032] Figure 1 A flowchart illustrating a dynamic X-ray image matching method according to an embodiment of the present disclosure is shown. Figure 1As shown, the dynamic X-ray image matching method includes: Step S101: Determining whether the dynamic multiple two-dimensional chest X-ray images during the breathing process include one or more of the following preset targets: the Nth second of maximum expiration, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of quiet expiration, and the maximum force and fastest speed exhalation to the residual air position within the maximum force inhalation to the total lung capacity at the end of quiet expiration; Step S102: If it is confirmed that the preset targets are included, then extracting the preset target X-ray two-dimensional chest image corresponding to the preset target from the dynamic multiple two-dimensional chest X-ray images during the breathing process; Step S103: Matching the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the preset target X-ray two-dimensional chest image with the lung blood flow image sequence corresponding to the multiple two-dimensional chest X-ray images at multiple times under breath-holding state to obtain the lung ventilation corresponding to the preset target X-ray two-dimensional chest image.

[0033] Step S101: Determine whether the dynamic multiple X-ray two-dimensional chest images during the breathing process include one or more of the following set targets: the Nth second of maximum expiration, the quiet expiration phase, the maximum force to inhale to the total lung capacity at the end of the quiet expiration, and the maximum force and fastest speed of continuous expiration to the residual air position during the maximum force and fastest speed expiration at the end of the quiet expiration.

[0034] In this embodiment of the disclosure, a dynamic X-ray camera is used to capture the subject's breathing process, obtaining multiple dynamic two-dimensional chest X-ray images during the breathing process. Similarly, the same subject is photographed while holding their breath, obtaining multiple two-dimensional chest X-ray images at multiple moments during the breath-holding state.

[0035] In this embodiment, N is greater than or equal to 1 and is a positive integer or a positive decimal. For example, N can be configured as a series of values ​​such as 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, etc. Those skilled in the art can configure the value corresponding to N according to actual needs.

[0036] Step S102: If included, extract the target X-ray two-dimensional chest image corresponding to the target from the multiple dynamic X-ray two-dimensional chest images during the breathing process. The target X-ray two-dimensional chest image includes one or more of the following chest images: a maximum exhalation X-ray two-dimensional chest image at the Nth second, multiple first deep inhalation X-ray two-dimensional chest images, multiple forced exhalation X-ray two-dimensional chest images, and multiple second deep inhalation X-ray two-dimensional chest images.

[0037] In this embodiment of the disclosure, determining whether the dynamic multiple X-ray two-dimensional chest images during the breathing process include one or more of the following preset targets: the Nth second of maximum expiration, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of quiet expiration, and the maximum force and fastest speed exhalation to the residual air position within the maximum force and fastest speed exhalation position within the maximum force and fastest speed exhalation position at the end of quiet expiration. This includes: if the dynamic multiple X-ray two-dimensional chest images taken during the breathing process of the subject include one or more of the following preset targets: the Nth second of maximum expiration, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of quiet expiration, and the maximum force and fastest speed exhalation to the residual air position within the maximum force and fastest speed exhalation position within the maximum force and fastest speed exhalation position at the end of quiet expiration, then extracting the preset target X-ray two-dimensional chest image from the dynamic multiple X-ray two-dimensional chest images during the breathing process.

[0038] In this embodiment of the disclosure, the step of extracting the target X-ray two-dimensional chest image corresponding to the target from multiple dynamic X-ray two-dimensional chest images during the breathing process includes: segmenting the lung fields of the multiple dynamic X-ray two-dimensional chest images during the breathing process to obtain multiple corresponding X-ray two-dimensional lung field mask images; determining the X-ray two-dimensional lung field mask image with the largest lung field mask area among the multiple X-ray two-dimensional lung field mask images as the maximum deep inspiration X-ray two-dimensional chest image; determining the maximum exhalation at the Nth second based on the frame rate during the capture of the multiple dynamic X-ray two-dimensional chest images during the breathing process; and / or segmenting the lung fields of the multiple dynamic X-ray two-dimensional chest images during the breathing process to obtain multiple corresponding X-ray two-dimensional lung field mask images; determining the calm exhalation during the breathing process based on the multiple lung field mask areas of the multiple X-ray two-dimensional lung field mask images and the rate of change of the lung field mask areas corresponding to the multiple lung field mask areas. The multiple first deep inhalation X-ray two-dimensional chest images are determined as follows: The X-ray two-dimensional chest image corresponding to the next moment after the end of the calm exhalation phase, up to the X-ray two-dimensional chest image corresponding to the largest lung field mask area among multiple next moments corresponding to the end of the calm exhalation phase; the X-ray two-dimensional chest image corresponding to the next moment after the X-ray two-dimensional chest image corresponding to the largest lung field mask area among multiple next moments corresponding to the largest lung field mask area among the multiple first deep inhalation X-ray two-dimensional chest images, up to the X-ray two-dimensional chest image corresponding to the smallest lung field mask area among the multiple next moments corresponding to the largest lung field mask area among the multiple first deep inhalation X-ray two-dimensional chest images, up to the multiple forced exhalation X-ray two-dimensional chest images; the X-ray two-dimensional chest image corresponding to the next moment after the X-ray two-dimensional chest image corresponding to the smallest mask area among the multiple forced exhalation X-ray two-dimensional chest images, up to the X-ray two-dimensional chest image corresponding to the largest lung field mask area among the multiple next moments corresponding to the smallest mask area among the multiple forced exhalation X-ray two-dimensional chest images, up to the multiple second deep inhalation X-ray two-dimensional chest images.

[0039] For example, determining the first second of maximum expiration X-ray two-dimensional chest image from multiple dynamic X-ray two-dimensional chest images during the breathing process based on the maximum inhalation X-ray two-dimensional chest image includes: acquiring the frame rate (frames / second) during the process of capturing multiple dynamic X-ray two-dimensional chest images during the breathing process; and determining the first second of maximum expiration X-ray two-dimensional chest image corresponding to the first second of expiration after capturing the maximum inhalation X-ray two-dimensional chest image based on the frame rate during the process of capturing multiple dynamic X-ray two-dimensional chest images during the breathing process. Specifically, determining the first second of the maximum exhalation X-ray two-dimensional chest image corresponding to the first second of exhalation after the maximum inspiratory X-ray two-dimensional chest image is determined based on the frame rate during the process of capturing multiple dynamic X-ray two-dimensional chest images during breathing. This includes: configuring the X-ray two-dimensional chest image corresponding to the first reduction in lung field mask area after the maximum lung field mask area corresponding to the maximum inspiratory X-ray two-dimensional chest image as the exhalation start X-ray two-dimensional chest image; and determining the first second of the maximum exhalation X-ray two-dimensional chest image corresponding to the first second based on the frame rate during the process of capturing multiple dynamic X-ray two-dimensional chest images during breathing and the exhalation start X-ray two-dimensional chest image.

[0040] Step S103: Based on the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image, match it with the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding state to obtain the lung ventilation corresponding to the set target X-ray two-dimensional chest image.

[0041] In this embodiment of the disclosure, the step of matching the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image with the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding state to obtain the lung airflow corresponding to the set target X-ray two-dimensional chest image includes: matching the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image based on multiple first heart edge distances of the lung airflow and blood flow mixed image sequence or the first heart edge distance of the lung airflow and blood flow mixed image and multiple second heart edge distances of the lung blood flow image sequence.

[0042] In this embodiment of the disclosure, the matching of the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image based on multiple first heart edge distances or first heart edge distances of the lung airflow and blood flow mixed image and multiple second heart edge distances of the lung blood flow image sequence includes: calculating multiple heart edge distance differences corresponding to each of the multiple first heart edge distances or between the first heart edge distance and the multiple second heart edge distances; matching the lung airflow and blood flow mixed image corresponding to the first heart edge distance with the smallest difference of the multiple heart edge distances with the lung blood flow image corresponding to the second heart edge distance, and determining the corresponding matching relationship between the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image and the lung blood flow image sequence.

[0043] In this embodiment of the disclosure, determining the plurality of first cardiac edge distances / first cardiac edge distances includes: determining the plurality of first cardiac edge distances / first cardiac edge distances corresponding to the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image or the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image corresponding to the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image, based on the right lung mask edge image and left lung mask edge image of the first fixed X-ray image to be registered of the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image.

[0044] In this embodiment of the disclosure, the step of determining multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image based on the right lung mask edge image and left lung mask edge image of the first fixed X-ray image to be registered corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image or the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image, including: determining the corresponding right cardiac border point and left cardiac border point according to the right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image / the lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung airflow and blood flow mixed image / the lung airflow and blood flow mixed image, and determining the multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence.

[0045] In this embodiment of the disclosure, before determining the multiple first heart border distances corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image based on the right lung mask edge image and left lung mask edge image of the first fixed X-ray image to be registered corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image or the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image, the method includes: acquiring multiple first right lung mask registration images / first right lung mask registration images corresponding to the multiple first right lung X-ray registration images / the multiple first left lung X-ray registration images / the multiple first left lung X-ray registration images / the multiple first left lung X-ray registration images / the multiple first right lung mask registration images corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image during the determination of the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image; and registering the multiple first right lung mask registration images / the multiple first right lung mask registration images respectively. Edge detection is performed on multiple first left lung mask registration images / first left lung mask registration images to obtain the right lung mask edge image and left lung mask edge image / right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence; or, it includes: acquiring multiple first right lung mask images corresponding to multiple first right lung X-ray images and multiple first left lung mask images corresponding to multiple first left lung X-ray images / first right lung mask image and first left lung mask image corresponding to the first right lung X-ray image during the determination of the lung airflow and blood flow mixed image sequence; edge detection is performed on the multiple first right lung mask images and multiple first left lung mask images / first right lung mask image and first left lung mask image respectively to obtain the right lung mask edge image and left lung mask edge image / right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence.

[0046] In this embodiment of the disclosure, determining the plurality of second cardiac edge distances includes: determining the plurality of second cardiac edge distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and the left lung mask edge image of the second right lung X-ray image and the second left lung X-ray image to be registered corresponding to the lung blood flow image sequence.

[0047] In this embodiment of the disclosure, determining the multiple second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence includes: determining the corresponding right cardiac border point and left cardiac border point based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung blood flow image to determine the multiple second cardiac border distances corresponding to the lung blood flow image sequence.

[0048] In this embodiment of the disclosure, determining the lung airflow-bloodflow mixed image sequence or lung airflow-bloodflow mixed image includes: segmenting the lung field of the set target X-ray two-dimensional chest image to obtain a plurality of first right lung mask images, a plurality of first left lung mask images, and one or more first mask images of a plurality of first lung masks; extracting a plurality of first right lung X-ray images, a plurality of first left lung X-ray images, and one or more first lung X-ray images corresponding to the plurality of first right lung mask images, the plurality of first left lung mask images, and one or more first mask images of a plurality of first lung masks; and extracting a plurality of first right lung X-ray images, a plurality of first left lung X-ray images, and one or more first X-ray images of a plurality of first lung X-ray images corresponding to the plurality of first right lung mask images, the plurality of first left lung mask ... A first right lung X-ray image, multiple first left lung X-ray images, and one or more first lung X-ray images are registered to obtain multiple first right lung X-ray registered images, multiple first left lung X-ray registered images, and one or more first lung X-ray registered images. Pixel-level subtraction processing is performed on multiple first right lung X-ray registered images, multiple first left lung X-ray registered images, and one or more first lung X-ray registered images that are adjacent or at a set time interval to determine the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the acquired dynamic multiple X-ray two-dimensional chest images during the respiratory process.

[0049] In this embodiment of the disclosure, before determining the multiple second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and left lung mask edge image of the second X-ray image to be fixed and registered, respectively based on the lung blood flow image sequence or the multiple second right lung X-ray images and multiple second left lung X-ray images to be registered corresponding to the lung blood flow image sequence, the method includes: acquiring multiple second right lung mask registration or Gaussian blur images and multiple second left lung mask registration or Gaussian blur images corresponding to the multiple second right lung X-ray registration or Gaussian blur images during the determination of the lung blood flow image sequence; and respectively processing the multiple second right lung mask registration or Gaussian blur images and multiple second... Edge detection is performed on the left lung mask registration or Gaussian blurred image to obtain the right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence; or, it includes: acquiring multiple second right lung mask images and multiple second left lung mask images corresponding to the multiple second right lung X-ray images and multiple second left lung X-ray images to be registered during the determination of the lung blood flow image sequence; edge detection is performed on the multiple second right lung mask images and multiple second left lung mask images respectively to obtain the right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence.

[0050] In this embodiment of the disclosure, determining the pulmonary blood flow image sequence includes: segmenting the lung fields of multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state to obtain multiple second right lung mask images, multiple second left lung mask images, and one or more second mask images of multiple second lung masks; extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and one or more second lung X-ray images of the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung masks corresponding to the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung X-ray images of the multiple lung masks; and processing multiple second right lung X-ray images and multiple second left lung X-ray images at adjacent or predetermined time intervals. One or more second X-ray images of the second lung are registered or Gaussian blurred to obtain multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and one or more second X-ray registered or Gaussian blurred images of the second lung. Pixel-level subtraction is performed on multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and one or more second lung X-ray registered or Gaussian blurred images at adjacent times or at a set time interval to determine the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under the breath-holding state.

[0051] In this embodiment of the disclosure, the lung ventilation corresponding to the target X-ray two-dimensional chest image is obtained by matching the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding conditions. This includes: registering the left lung airflow and blood flow mixed image sequence in the lung airflow and blood flow mixed image sequence with its matched left lung blood flow image, and registering the left lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence with its matched left lung blood flow image to obtain a left lung airflow and blood flow mixed registration image sequence / left lung airflow and blood flow mixed registration image; performing pixel-level subtraction processing on the left lung airflow and blood flow mixed registration image sequence / left lung airflow and blood flow mixed registration image and its matched left lung blood flow image to determine dynamic left lung ventilation; and / or, respectively, the lung airflow and blood flow mixed image sequence The sequence of right lung airflow and blood flow mixed images in the column is registered with its matching right lung blood flow image to obtain a right lung airflow and blood flow mixed registration image sequence / right lung airflow and blood flow mixed registration image; pixel-level subtraction processing is performed on the right lung airflow and blood flow mixed registration image sequence / right lung airflow and blood flow mixed registration image and its matching right lung blood flow image to determine dynamic right lung ventilation; and / or, the sequence of right lung airflow and blood flow mixed images is registered with its matching lung blood flow image to obtain a lung airflow and blood flow mixed registration image sequence / lung airflow and blood flow mixed registration image; pixel-level subtraction processing is performed on the lung airflow and blood flow mixed registration image sequence / lung airflow and blood flow mixed registration image and its matching lung blood flow image to determine lung ventilation.

[0052] In this embodiment of the disclosure, determining the quiet exhalation phase includes: segmenting the lung fields of multiple dynamic two-dimensional chest X-ray images during the breathing process to obtain multiple corresponding two-dimensional lung field mask images; and determining the quiet exhalation phase during the breathing process based on the multiple lung field mask areas of the multiple two-dimensional lung field mask images and the lung field mask area change rate corresponding to the multiple lung field mask areas.

[0053] In this embodiment of the disclosure, determining the quiet exhalation phase of the respiratory process based on the multiple lung field mask areas of the multiple X-ray two-dimensional lung field mask images and the lung field mask area change rate corresponding to the multiple lung field mask areas includes: extracting continuous lung field mask areas to be processed within a set lung field mask area interval according to the time series corresponding to the multiple lung field mask areas; calculating the lung field mask area change rate of the lung field mask areas to be processed; if the lung field mask area change rate is less than or equal to the set lung field mask area change rate, then determining the time interval corresponding to the lung field mask area change rate less than or equal to the set lung field mask area change rate as the quiet exhalation phase of the respiratory process.

[0054] In this embodiment of the disclosure, determining forced vital capacity includes: determining the calm exhalation phase during the breathing process using the method for determining the calm exhalation phase as described above; extracting multiple first deep inhalation two-dimensional chest images corresponding to the calm exhalation phase at the end of the calm exhalation with maximum force to the total lung capacity from multiple dynamic two-dimensional chest X-ray images during the breathing process; multiple forced exhalation two-dimensional chest images corresponding to the ... respectively; and determining one or more types of forced vital capacity based on the multiple first deep inhalation two-dimensional chest images, the multiple forced exhalation two-dimensional chest images, and the multiple second deep inhalation two-dimensional chest images respectively.

[0055] In this embodiment of the disclosure, the step of extracting multiple first deep inhalation X-ray two-dimensional chest images corresponding to the end of the quiet exhalation phase with maximum force to the total lung capacity from multiple dynamic X-ray two-dimensional chest images during the breathing process, multiple forced exhalation X-ray two-dimensional chest images corresponding to the explosive exhalation with maximum force and speed to the residual volume after the total lung capacity within a set time (immediately), and multiple second deep inhalation X-ray two-dimensional chest images corresponding to the deep inhalation at a set rate (rapidly) to the total lung capacity after the residual volume, includes: determining the X-ray two-dimensional chest image corresponding to the next moment after the end of the quiet exhalation phase in the quiet exhalation phase to the X-ray two-dimensional chest image corresponding to the largest lung field mask area among multiple next moments corresponding to the end of the quiet exhalation phase as the multiple The first deep inhalation X-ray two-dimensional chest image; the X-ray two-dimensional chest image corresponding to the next moment of the X-ray two-dimensional chest image with the largest lung field mask area among the multiple next moments corresponding to the largest lung field mask area of ​​the multiple first deep inhalation X-ray two-dimensional chest images is determined as the multiple forced exhalation X-ray two-dimensional chest images; the X-ray two-dimensional chest image corresponding to the next moment of the X-ray two-dimensional chest image with the smallest mask area among the multiple forced exhalation X-ray two-dimensional chest images is determined as the multiple second deep inhalation X-ray two-dimensional chest images.

[0056] In this embodiment of the disclosure, the step of determining one or more of the forced vital capacity (FVC) among the first deep inspiratory FVC, forced expiratory FVC, and second deep inspiratory FVC based on the plurality of first deep inspiratory two-dimensional chest X-ray images, the plurality of forced expiratory two-dimensional chest X-ray images, and the plurality of second deep inspiratory two-dimensional chest X-ray images includes: performing registration processing on the plurality of first deep inspiratory two-dimensional lung field images after segmenting the plurality of first deep inspiratory two-dimensional chest X-ray images to obtain a plurality of first deep inspiratory two-dimensional lung field registration images; performing pixel-level subtraction processing on the plurality of first deep inspiratory two-dimensional lung field registration images at adjacent or predetermined intervals to determine the first deep inspiratory FVC; and performing the... Multiple forced expiratory two-dimensional chest X-ray images, after being segmented, are used to obtain multiple forced expiratory two-dimensional lung field images. Registration processing is then performed on these images to obtain multiple registered forced expiratory two-dimensional lung field images. Pixel-level subtraction processing is then performed on these multiple registered forced expiratory two-dimensional lung field images at adjacent or predetermined time intervals to determine the forced expiratory vital capacity. Similarly, multiple second deep inspiratory two-dimensional chest X-ray images, after being segmented, are used to obtain multiple registered second deep inspiratory two-dimensional lung field images. Pixel-level subtraction processing is then performed on these multiple registered second deep inspiratory two-dimensional lung field images at adjacent or predetermined time intervals to determine the second deep inspiratory vital capacity.

[0057] In this embodiment of the disclosure, the step of determining one or more of the forced vital capacity (FVC) among the first inspiratory vital capacity, forced expiratory vital capacity, and second inspiratory vital capacity based on the plurality of first deep inspiratory two-dimensional chest X-ray images, the plurality of forced expiratory two-dimensional chest X-ray images, and the plurality of second deep inspiratory two-dimensional chest X-ray images further includes: performing pixel-level subtraction processing on one or more of the lung field registration images of the plurality of first deep inspiratory two-dimensional chest X-ray images, the plurality of forced expiratory two-dimensional chest X-ray images, and the plurality of second deep inspiratory two-dimensional chest X-ray images at adjacent or predetermined intervals to obtain a plurality of corresponding mixed lung airflow and blood flow images; and determining one or more of the forced vital capacity (FVC) among the first deep inspiratory vital capacity, forced expiratory vital capacity, and second deep inspiratory vital capacity based on the plurality of mixed lung airflow and blood flow images and their matched lung blood flow images.

[0058] In this embodiment of the disclosure, determining one or more of the forced vital capacity (FVC) among the first inspiratory vital capacity (FVC), forced expiratory vital capacity (FVC), and second inspiratory vital capacity (DVC) based on the plurality of mixed lung airflow and blood flow images and their matched lung blood flow images includes: performing registration processing on the plurality of mixed lung airflow and blood flow images and their matched lung blood flow images respectively to obtain a plurality of mixed lung airflow and blood flow registration images; and performing pixel-level subtraction processing on the plurality of mixed lung airflow and blood flow registration images and their matched lung blood flow images to determine one or more of the forced vital capacity (FVC) among the first inspiratory vital capacity (FVC), forced expiratory vital capacity (DVC), and second inspiratory vital capacity (DVC).

[0059] In this embodiment of the disclosure, determining the lung blood flow image matching the plurality of mixed lung airflow and blood flow images includes: acquiring a lung blood flow image sequence corresponding to multiple two-dimensional chest X-ray images taken at multiple times during breath-holding; calculating multiple first cardiac border distances corresponding to the plurality of mixed lung airflow and blood flow images and multiple second cardiac border distances of the lung blood flow image sequence; and determining the lung blood flow image matching the plurality of mixed lung airflow and blood flow images based on the first cardiac border distances and the multiple second cardiac border distances. The mixed lung airflow and blood flow images include: a mixed left lung airflow and blood flow image and a mixed right lung airflow and blood flow image; the lung blood flow image sequence includes: a left lung blood flow image sequence and a right lung blood flow image sequence; the multiple dynamic two-dimensional chest X-ray images during breathing and the multiple two-dimensional chest X-ray images taken at multiple times during breath-holding are captured by the same subject.

[0060] In this embodiment of the disclosure, determining the pulmonary blood flow image sequence corresponding to multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state includes: segmenting the lung fields (left lung field and / or right lung field and / or both lung fields) of the multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state to obtain one or more second mask images of multiple second right lung mask images, multiple second left lung mask images, and multiple second lung mask images (second bilateral lung mask images) corresponding to the multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state; extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and one or more second lung X-ray images (second bilateral lung X-ray images) corresponding to the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung mask images; and extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and one or more second lung X-ray images (second bilateral lung X-ray images) corresponding to the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung mask images; and extracting multiple second lung X-ray images of adjacent or predetermined time intervals. Multiple second right lung X-ray images, multiple second left lung X-ray images, and multiple second lung X-ray images (bilateral lung X-ray images) are registered or Gaussian blurred to obtain multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and multiple second lung X-ray registered or Gaussian blurred images (bilateral lung X-ray registered or Gaussian blurred images). Pixel-level subtraction is performed on multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and multiple second lung X-ray registered or Gaussian blurred images (bilateral lung X-ray registered or Gaussian blurred images) at adjacent times or at a set time interval to determine the lung blood flow image sequence corresponding to multiple two-dimensional chest X-ray images at multiple times under the breath-holding state.

[0061] In this embodiment of the disclosure, determining multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence based on the right lung mask edge image and the left lung mask edge image of each lung airflow and blood flow mixed image sequence includes: determining the corresponding right cardiac border point and left cardiac border point based on the right lung mask edge image and the left lung mask edge image of each lung airflow and blood flow mixed image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung airflow and blood flow mixed image, and determining multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence.

[0062] In this embodiment of the disclosure, before determining the multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image based on the right lung mask edge image and the left lung mask edge image of the first right lung X-ray image and the first left lung X-ray image to be registered corresponding to the lung airflow and blood flow mixed image, the method includes: acquiring the first right lung mask registration image corresponding to the first right lung X-ray registration image and the first left lung mask registration image corresponding to the first left lung X-ray registration image during the process of determining the lung airflow and blood flow mixed image; Edge detection is performed on the first right lung mask registration image and the first left lung mask registration image respectively to obtain the right lung mask edge image and the left lung mask edge image of the lung airflow and blood flow mixed image; or, it includes: acquiring the first right lung mask image corresponding to the first right lung X-ray image and the first left lung mask image corresponding to the first left lung X-ray image during the determination of the lung airflow and blood flow mixed image; edge detection is performed on the first right lung mask image and the first left lung mask image respectively to obtain the right lung mask edge image and the left lung mask edge image of the lung airflow and blood flow mixed image.

[0063] In this embodiment of the disclosure, determining the first cardiac border distance corresponding to the lung airflow and blood flow mixed image based on the right lung mask edge image and the left lung mask edge image of the first right lung X-ray image and the first left lung X-ray image to be registered corresponding to the lung airflow and blood flow mixed image includes: determining the corresponding right cardiac border point and left cardiac border point according to the right lung mask edge image and the left lung mask edge image of the lung airflow and blood flow mixed image; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to the lung airflow and blood flow mixed image, and determining multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence.

[0064] In this embodiment of the disclosure, determining a lung blood flow image matching the lung airflow and blood flow mixed image based on a plurality of first cardiac edge distances and a plurality of second cardiac edge distances includes: calculating a plurality of cardiac edge distance differences between the plurality of first cardiac edge distances and the plurality of second cardiac edge distances respectively; matching the lung blood flow image corresponding to the second cardiac edge distance with the smallest cardiac edge distance difference among the plurality of cardiac edge distance differences with the lung airflow and blood flow mixed image corresponding to the first cardiac edge distance, thereby determining a lung blood flow image matching the lung airflow and blood flow mixed image.

[0065] In this embodiment of the disclosure, determining the maximum exhalation volume in the first second includes: acquiring multiple dynamic two-dimensional chest X-ray images during the respiratory process, including the maximum inhalation and maximum exhalation; determining the maximum inhalation two-dimensional chest X-ray image among the multiple dynamic two-dimensional chest X-ray images during the respiratory process; determining the maximum exhalation two-dimensional chest X-ray image in the first second among the multiple dynamic two-dimensional chest X-ray images during the respiratory process based on the maximum inhalation two-dimensional chest X-ray image; and determining the maximum exhalation volume in the first second based on the ventilation volume corresponding to the maximum exhalation two-dimensional chest X-ray image in the first second.

[0066] In this embodiment of the disclosure, determining the maximum inspiratory two-dimensional chest image among the multiple dynamic two-dimensional chest images during the breathing process includes: segmenting the lung fields of the multiple dynamic two-dimensional chest images during the breathing process to obtain corresponding multiple two-dimensional lung field mask images; and determining the two-dimensional lung field mask image with the largest lung field mask area among the multiple two-dimensional lung field mask images as the maximum inspiratory two-dimensional chest image.

[0067] In this embodiment of the disclosure, determining the first second of the maximum expiration X-ray two-dimensional chest image from multiple dynamic X-ray two-dimensional chest images during the breathing process based on the maximum inhalation X-ray two-dimensional chest image includes: acquiring the frame rate (frames / second) during the process of capturing multiple dynamic X-ray two-dimensional chest images during the breathing process; and determining the first second of the maximum expiration X-ray two-dimensional chest image corresponding to the first second of expiration after capturing the maximum inhalation X-ray two-dimensional chest image based on the frame rate during the process of capturing multiple dynamic X-ray two-dimensional chest images during the breathing process.

[0068] In this embodiment of the disclosure, determining the first second of the maximum exhalation X-ray two-dimensional chest image corresponding to the first second of exhalation after the maximum inspiratory X-ray two-dimensional chest image is based on the frame rate during the process of capturing multiple dynamic X-ray two-dimensional chest images during breathing. This includes: configuring the X-ray two-dimensional chest image corresponding to the first reduction in lung field mask area after the maximum lung field mask area corresponding to the maximum inspiratory X-ray two-dimensional chest image as the exhalation start X-ray two-dimensional chest image; and determining the first second of the maximum exhalation X-ray two-dimensional chest image corresponding to the first second based on the frame rate during the process of capturing multiple dynamic X-ray two-dimensional chest images during breathing and the exhalation start X-ray two-dimensional chest image.

[0069] In this embodiment of the disclosure, determining the exhaled volume in the first second of maximum expiration based on the ventilation volume corresponding to the tidal volume of the two-dimensional chest X-ray image of the maximum expiration includes: extracting a first two-dimensional lung field image corresponding to the lung field segmentation of the two-dimensional chest X-ray image of the maximum inhalation or the two-dimensional chest X-ray image of the start of expiration, and a second two-dimensional lung field image corresponding to the lung field segmentation of the two-dimensional chest X-ray image of the maximum expiration; registering the first two-dimensional lung field image and the second two-dimensional lung field image to obtain a registered two-dimensional lung field image; and performing pixel-level subtraction processing on the first two-dimensional lung field image or the second two-dimensional lung field image and the registered two-dimensional lung field image to determine the exhaled volume in the first second of maximum expiration.

[0070] In this embodiment of the disclosure, the step of extracting the first two-dimensional lung field image corresponding to the lung field segmentation of the maximum inhalation two-dimensional chest X-ray image or the expiratory two-dimensional chest X-ray image and the second two-dimensional lung field image corresponding to the lung field segmentation of the maximum expiratory two-dimensional chest X-ray image in the first second includes: extracting the first two-dimensional lung field mask image corresponding to the maximum inhalation two-dimensional chest X-ray image or the expiratory two-dimensional chest X-ray image in the first second from multiple X-ray two-dimensional chest X-ray images dynamically segmented during the breathing process, and the second two-dimensional lung field image corresponding to the maximum expiratory two-dimensional chest X-ray image in the first second. The first X-ray two-dimensional lung field mask image is obtained by performing pixel-level multiplication on the first X-ray two-dimensional lung field mask image and the maximum inhalation X-ray two-dimensional chest image or the exhalation start X-ray two-dimensional chest image, and extracting the first X-ray two-dimensional lung field image corresponding to the lung field segmentation of the maximum inhalation X-ray two-dimensional chest image or the exhalation start X-ray two-dimensional chest image; the second X-ray two-dimensional lung field mask image is obtained by performing pixel-level multiplication on the first second of the maximum exhalation X-ray two-dimensional chest image, and extracting the second X-ray two-dimensional lung field image corresponding to the lung field segmentation of the first second of the maximum exhalation X-ray two-dimensional chest image.

[0071] In this embodiment of the disclosure, the step of performing pixel-level subtraction processing on the first X-ray two-dimensional lung field image or the second X-ray two-dimensional lung field image and the X-ray two-dimensional lung field registration image to determine the maximum exhalation volume in the first second includes: performing pixel-level subtraction processing on the first X-ray two-dimensional lung field image or the second X-ray two-dimensional lung field image and the X-ray two-dimensional lung field registration image to obtain a mixed lung airflow and blood flow image; and determining the maximum exhalation volume in the first second based on the mixed lung airflow and blood flow image and its matching lung blood flow image.

[0072] In this embodiment of the disclosure, determining the maximum exhalation volume in the first second based on the mixed lung airflow and blood flow image and its matching lung blood flow image includes: performing registration operations on the mixed lung airflow and blood flow image and its matching lung blood flow image respectively to obtain a mixed registration image of lung airflow and blood flow; and performing pixel-level subtraction processing on the mixed registration image of lung airflow and blood flow and the lung blood flow image to determine the maximum exhalation volume in the first second.

[0073] In this embodiment of the disclosure, determining the lung blood flow image matching the lung airflow and blood flow mixed image includes: acquiring a lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images taken at multiple times during breath-holding; calculating a first cardiac border distance corresponding to the lung airflow and blood flow mixed image and multiple second cardiac border distances of the lung blood flow image sequence; and determining the lung blood flow image matching the lung airflow and blood flow mixed image based on the first cardiac border distance and the multiple second cardiac border distances. The lung airflow and blood flow mixed image includes a left lung airflow and blood flow mixed image and a right lung airflow and blood flow mixed image; the lung blood flow image sequence includes a left lung blood flow image sequence and a right lung blood flow image sequence; the dynamic multiple X-ray two-dimensional chest images during breathing and the multiple X-ray two-dimensional chest images taken at multiple times during breath-holding are captured by the same subject.

[0074] In this embodiment of the disclosure, determining the pulmonary blood flow image sequence corresponding to multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state includes: segmenting the lung fields (left lung field and / or right lung field and / or both lung fields) of the multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state to obtain one or more second mask images of multiple second right lung mask images, multiple second left lung mask images, and multiple second lung mask images (second bilateral lung mask images) corresponding to the multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state; extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and one or more second lung X-ray images (second bilateral lung X-ray images) corresponding to the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung mask images; and extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and one or more second lung X-ray images (second bilateral lung X-ray images) corresponding to the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung mask images; and extracting multiple second lung X-ray images of adjacent or predetermined time intervals. Multiple second right lung X-ray images, multiple second left lung X-ray images, and multiple second lung X-ray images (bilateral lung X-ray images) are registered or Gaussian blurred to obtain multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and multiple second lung X-ray registered or Gaussian blurred images (bilateral lung X-ray registered or Gaussian blurred images). Pixel-level subtraction is performed on multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and multiple second lung X-ray registered or Gaussian blurred images (bilateral lung X-ray registered or Gaussian blurred images) at adjacent times or at a set time interval to determine the lung blood flow image sequence corresponding to multiple two-dimensional chest X-ray images at multiple times under the breath-holding state.

[0075] In this embodiment of the disclosure, calculating the first cardiac edge distance corresponding to the lung airflow and blood flow mixed image includes: determining the first cardiac edge distance corresponding to the lung airflow and blood flow mixed image based on the right lung mask edge image and the left lung mask edge image of the first right lung X-ray image and the first left lung X-ray image to be registered corresponding to the lung airflow and blood flow mixed image.

[0076] In this embodiment of the disclosure, before determining the first cardiac border distance corresponding to the lung airflow and blood flow mixed image based on the right lung mask edge image and the left lung mask edge image of the first right lung X-ray image and the first left lung X-ray image to be registered corresponding to the lung airflow and blood flow mixed image, the method includes: acquiring the first right lung mask registration image corresponding to the first right lung X-ray registration image and the first left lung mask registration image corresponding to the first left lung X-ray registration image during the determination of the lung airflow and blood flow mixed image; The method includes: performing edge detection on the first right lung mask registration image and the first left lung mask registration image to obtain the right lung mask edge image and the left lung mask edge image of the lung airflow and blood flow mixed image; or, acquiring the first right lung mask image corresponding to the first right lung X-ray image and the first left lung mask image corresponding to the first left lung X-ray image during the determination of the lung airflow and blood flow mixed image; performing edge detection on the first right lung mask image and the first left lung mask image respectively to obtain the right lung mask edge image and the left lung mask edge image of the lung airflow and blood flow mixed image.

[0077] In this embodiment of the disclosure, determining the first cardiac border distance corresponding to the lung airflow and blood flow mixed image based on the right lung mask edge image and the left lung mask edge image of the first right lung X-ray image and the first left lung X-ray image to be registered corresponding to the lung airflow and blood flow mixed image includes: determining the corresponding right cardiac border point and left cardiac border point according to the right lung mask edge image and the left lung mask edge image of the lung airflow and blood flow mixed image; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to the lung airflow and blood flow mixed image, and determining multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence.

[0078] In this embodiment of the disclosure, calculating multiple second cardiac border distances of the pulmonary blood flow image sequence includes: determining multiple second cardiac border distances corresponding to the pulmonary blood flow image sequence based on the right lung mask edge image and left lung mask edge image of the second X-ray image to be fixed and registered, which are based on the pulmonary blood flow image sequence or multiple second right lung X-ray images and multiple second left lung X-ray images to be registered corresponding to the pulmonary blood flow image sequence.

[0079] In this embodiment of the disclosure, determining the multiple second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence includes: determining the corresponding right cardiac border point and left cardiac border point based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung blood flow image to determine the multiple second cardiac border distances corresponding to the lung blood flow image sequence.

[0080] In this embodiment of the disclosure, before determining the multiple second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge images and left lung mask edge images of the second X-ray images to be fixed and registered, respectively, based on the lung blood flow image sequence or the multiple second right lung X-ray images and multiple second left lung X-ray images to be registered corresponding to the lung blood flow image sequence, the method includes: acquiring multiple second right lung mask registration or Gaussian blur images and multiple second left lung mask registration or Gaussian blur images corresponding to the multiple second right lung X-ray registration or Gaussian blur images during the determination of the lung blood flow image sequence; performing edge detection on the multiple second right lung mask registration or Gaussian blur images and the multiple second left lung mask registration or Gaussian blur images respectively to obtain each lung airflow and blood flow mixed image sequence. The image includes: right lung mask edge image and left lung mask edge image of the combined image; or, it includes: acquiring multiple second right lung mask images and multiple second left lung mask images corresponding to the multiple second right lung X-ray images and multiple second left lung X-ray images to be registered during the determination of the lung blood flow image sequence; performing edge detection on the multiple second right lung mask images and multiple second left lung mask images respectively to obtain the right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence; and / or, wherein the multiple second right lung X-ray registration images are the same as the multiple third deformation fields corresponding to the multiple second right lung mask registration images, and the multiple second left lung X-ray registration images are the same as the multiple fourth deformation fields corresponding to the multiple second left lung mask registration images.

[0081] Specifically, in determining the lung blood flow image sequence, multiple second right lung X-ray images that are adjacent or at a set time interval are registered to obtain multiple corresponding second right lung X-ray registered images and generate multiple corresponding third deformation fields; the multiple third deformation fields are used to perform lung field mask deformation processing on multiple second right lung mask images corresponding to the multiple second right lung X-ray images to obtain multiple second right lung mask registered images.

[0082] Similarly, specifically, in the process of determining the lung blood flow image sequence, multiple second left lung X-ray images that are adjacent or at a set time interval are registered to obtain multiple corresponding second left lung X-ray registered images and generate multiple corresponding fourth deformation fields; the multiple fourth deformation fields are used to perform lung field mask deformation processing on multiple second left lung mask images corresponding to the multiple second left lung X-ray images to obtain multiple second left lung mask registered images.

[0083] In this embodiment of the disclosure, determining the lung blood flow image matching the lung airflow and blood flow mixed image based on the first cardiac edge distance and the plurality of second cardiac edge distances includes: calculating a plurality of cardiac edge distance differences corresponding to the first cardiac edge distance and the plurality of second cardiac edge distances respectively; matching the lung blood flow image corresponding to the second cardiac edge distance with the smallest cardiac edge distance difference among the plurality of cardiac edge distance differences with the lung airflow and blood flow mixed image to determine the lung blood flow image matching the lung airflow and blood flow mixed image.

[0084] In this embodiment of the disclosure, determining the maximum exhalation volume in the first second based on the mixed lung airflow and blood flow image and its matched lung blood flow image includes: performing registration processing on the mixed lung airflow and blood flow image and the lung blood flow image to obtain a lung blood flow registration image; and performing pixel-level subtraction processing on the mixed lung airflow and blood flow image and the lung blood flow registration image to determine the maximum exhalation volume in the first second.

[0085] The entity executing the image processing method can be a dynamic X-ray image matching device or system. For example, the dynamic X-ray image matching method can be executed by a terminal device, server, or other processing device. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the dynamic X-ray image matching method can be implemented by a processor calling computer-readable instructions stored in memory.

[0086] Those skilled in the art will understand that in the above-described dynamic X-ray image matching method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0087] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a determining unit, configured to determine whether the dynamic multiple two-dimensional chest X-ray images during the breathing process include one or more preset targets, such as the maximum expiration at the Nth second, the quiet expiration phase, the maximum force inhalation to the total lung capacity at the end of the quiet expiration, and the maximum force and fastest speed exhalation to the residual air position within the maximum force inhalation to the total lung capacity at the end of the quiet expiration; wherein N is greater than or equal to 1 and is a positive integer; an extraction unit, configured to extract the preset target X-ray two-dimensional chest image corresponding to the preset target from the dynamic multiple two-dimensional chest X-ray images during the breathing process if the preset target is included; and a matching unit, configured to match the lung airflow and blood flow mixed image sequence or the lung airflow and blood flow mixed image corresponding to the preset target X-ray two-dimensional chest image with the lung blood flow image sequence corresponding to the multiple two-dimensional chest X-ray images at multiple times under breath-holding conditions, to obtain the lung ventilation corresponding to the preset target X-ray two-dimensional chest image.

[0088] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the dynamic X-ray image matching method described above.

[0089] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described dynamic X-ray image matching method.

[0090] According to one aspect of this disclosure, a dynamic X-ray image matching system is provided, comprising: a computer program product, the computer program product being configured with computer program instructions that, when executed by a processor, implement the aforementioned dynamic X-ray image matching method.

[0091] According to one aspect of this disclosure, a dynamic X-ray camera is provided, comprising: a dynamic X-ray image matching system as described above.

[0092] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to execute the dynamic X-ray image matching method described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0093] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0094] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0095] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured as described above. The electronic device can be provided as a terminal, a server, or other type of device.

[0096] Figure 2 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0097] Reference Figure 2 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0098] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0099] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of such data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0100] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0101] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0102] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0103] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0104] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0105] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0106] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0107] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0108] Figure 3 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 3The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0109] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0110] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0111] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0112] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0113] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0114] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0115] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0116] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0117] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0119] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A dynamic X-ray image matching method, characterized in that, include: Determine whether the dynamic multiple X-ray two-dimensional chest images during the breathing process include one or more of the following set targets: the Nth second of maximum expiration, the quiet expiration phase, the maximum force to inhale to the total lung capacity at the end of the quiet expiration, and the maximum force and fastest speed of exhalation to the residual air position during the maximum force and fastest speed exhalation at the end of the quiet expiration. If included, the target X-ray two-dimensional chest image corresponding to the target is extracted from multiple dynamic X-ray two-dimensional chest images during the breathing process; Based on the lung airflow and blood flow mixed image sequence corresponding to the set target X-ray two-dimensional chest image, or the lung airflow and blood flow mixed image sequence and the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding state, the lung ventilation corresponding to the set target X-ray two-dimensional chest image is obtained.

2. The dynamic X-ray image matching method according to claim 1, characterized in that, The step of matching the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image with the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding conditions to obtain the lung airflow corresponding to the set target X-ray two-dimensional chest image includes: matching the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image based on multiple first heart edge distances of the lung airflow and blood flow mixed image sequence or multiple second heart edge distances of the lung blood flow image sequence; and / or, The matching of the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image based on multiple first heart edge distances or first heart edge distances of the lung airflow and blood flow mixed image and multiple second heart edge distances of the lung blood flow image sequence includes: calculating multiple heart edge distance differences corresponding to each of the multiple first heart edge distances or between the first heart edge distance and the multiple second heart edge distances; and matching the lung airflow and blood flow mixed image corresponding to the first heart edge distance with the smallest difference of the multiple heart edge distances with the lung blood flow image corresponding to the second heart edge distance.

3. The dynamic X-ray image matching method according to any one of claims 1 or 2, characterized in that, Determining the plurality of first cardiac edge distances / first cardiac edge distances includes: determining the plurality of first cardiac edge distances / first cardiac edge distances corresponding to the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image or the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image corresponding to the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image, based on the right lung mask edge image and left lung mask edge image of the first fixed X-ray image to be registered for the lung airflow and blood flow mixed image sequence / lung airflow and blood flow mixed image; and / or, The step of determining multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image based on the right lung mask edge image and left lung mask edge image of the first fixed X-ray image to be registered corresponding to the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image or the lung airflow and blood flow mixed image sequence / the lung airflow and blood flow mixed image, respectively, includes: determining the corresponding right cardiac border point and left cardiac border point according to the right lung mask edge image and left lung mask edge image of each lung airflow and blood flow mixed image / the lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung airflow and blood flow mixed image / the lung airflow and blood flow mixed image, and determining the multiple first cardiac border distances corresponding to the lung airflow and blood flow mixed image sequence.

4. The dynamic X-ray image matching method according to claims 1-3, characterized in that, Determining the plurality of second cardiac border distances includes: determining the plurality of second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and the left lung mask edge image of the second right lung X-ray image and the second left lung X-ray image to be registered corresponding to the lung blood flow image sequence; and / or, The step of determining multiple second cardiac border distances corresponding to the lung blood flow image sequence based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence includes: determining the corresponding right cardiac border point and left cardiac border point based on the right lung mask edge image and the left lung mask edge image of each lung blood flow image in the lung blood flow image sequence; calculating the horizontal distance between the right cardiac border point and the left cardiac border point corresponding to each lung blood flow image to determine multiple second cardiac border distances corresponding to the lung blood flow image sequence.

5. The dynamic X-ray image matching method according to claims 1-4, characterized in that, Determining the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image includes: segmenting the lung field of the predetermined target X-ray two-dimensional chest image to obtain a plurality of first right lung mask images, a plurality of first left lung mask images, and one or more first mask images of a plurality of first lung masks; extracting a plurality of first right lung X-ray images, a plurality of first left lung X-ray images, and one or more first lung X-ray images corresponding to the plurality of first right lung mask images, the plurality of first left lung mask images, and one or more first mask images of a plurality of first lung masks; and extracting a plurality of first right lung X-ray images, a plurality of first left lung X-ray images, and one or more first lung X-ray images corresponding to the plurality of first right lung mask images, the plurality of first left lung mask images, and one or more first lung X-ray images of a plurality of first right lung X-ray images at adjacent or predetermined time intervals. Register the lung X-ray image, multiple first left lung X-ray images, and one or more first lung X-ray images to obtain multiple first right lung X-ray registered images, multiple first left lung X-ray registered images, and one or more first lung X-ray registered images; perform pixel-level subtraction processing on multiple first right lung X-ray registered images, multiple first left lung X-ray registered images, and one or more first lung X-ray registered images at adjacent or set time intervals to determine the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the acquired dynamic multiple X-ray two-dimensional chest images during the respiratory process.

6. The dynamic X-ray image matching method according to claims 1-5, characterized in that, Determining the pulmonary blood flow image sequence includes: segmenting the lung fields of multiple two-dimensional chest X-ray images taken at multiple time points during the breath-holding state to obtain multiple second right lung mask images, multiple second left lung mask images, and one or more second mask images of multiple second lung mask images; extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and one or more second lung X-ray images of the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung mask images corresponding to the multiple second right lung mask images, the multiple second left lung mask images, and one or more second lung X-ray images of the multiple lung mask images; and extracting multiple second right lung X-ray images, multiple second left lung X-ray images, and multiple second lung X-ray images at adjacent or predetermined time intervals. One or more second X-ray images of the lung X-ray image are registered or Gaussian blurred to obtain multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and one or more second X-ray registered or Gaussian blurred images of multiple second lung X-ray registered or Gaussian blurred images; pixel-level subtraction processing is performed on multiple second right lung X-ray registered or Gaussian blurred images, multiple second left lung X-ray registered or Gaussian blurred images, and one or more second lung X-ray registered or Gaussian blurred images of multiple times at a set time interval to determine the lung blood flow image sequence corresponding to multiple two-dimensional chest X-ray images at multiple time points under the breath-holding state.

7. The dynamic X-ray image matching method according to claims 1-6, characterized in that, Based on the lung airflow and blood flow mixed image sequence or lung airflow and blood flow mixed image corresponding to the set target X-ray two-dimensional chest image, and the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding conditions, the lung ventilation corresponding to the set target X-ray two-dimensional chest image is obtained, including: registering the left lung airflow and blood flow mixed image sequence in the lung airflow and blood flow mixed image sequence with its matched left lung blood flow image / registering the left lung airflow and blood flow mixed image in the lung airflow and blood flow mixed image with its matched left lung blood flow image to obtain a left lung airflow and blood flow mixed registration image sequence / left lung airflow and blood flow mixed registration image; performing pixel-level subtraction processing on the left lung airflow and blood flow mixed registration image sequence / left lung airflow and blood flow mixed registration image with its matched left lung blood flow image to determine dynamic left lung ventilation; and / or, respectively, the right lung airflow and blood flow mixed image sequence in the lung airflow and blood flow mixed image sequence A sequence of mixed lung airflow and blood flow images is registered with its matching right lung blood flow image. This results in a right lung airflow and blood flow registration image sequence / right lung airflow and blood flow registration image. Pixel-level subtraction is performed on the right lung airflow and blood flow registration image sequence / right lung airflow and blood flow registration image and its matching right lung blood flow image to determine dynamic right lung ventilation. Alternatively, the sequence of mixed lung airflow and blood flow images is registered with its matching lung blood flow image to obtain a lung airflow and blood flow registration image sequence / lung airflow and blood flow registration image. Pixel-level subtraction is performed on the lung airflow and blood flow registration image sequence / lung airflow and blood flow registration image and its matching lung blood flow image to determine lung ventilation.

8. A dynamic X-ray image matching system, characterized in that, include: The determining unit is used to determine whether the dynamic multiple X-ray two-dimensional chest images during the breathing process include one or more of the following preset targets: the Nth second of maximum expiration, the quiet expiration phase, the maximum force to inhale to the total lung capacity at the end of the quiet expiration, and the maximum force and fastest speed of continuous expiration to the residual air position during the maximum force to inhale to the total lung capacity at the end of the quiet expiration. An extraction unit is used, if applicable, to extract the target X-ray two-dimensional chest image corresponding to the target from multiple dynamic X-ray two-dimensional chest images during the breathing process; The matching unit is used to match the lung airflow and blood flow mixed image sequence corresponding to the set target X-ray two-dimensional chest image or the lung airflow and blood flow mixed image with the lung blood flow image sequence corresponding to multiple X-ray two-dimensional chest images at multiple times under breath-holding state, so as to obtain the lung ventilation corresponding to the set target X-ray two-dimensional chest image.

9. A dynamic X-ray image matching system, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the dynamic X-ray image matching method according to any one of claims 1 to 7; or, Includes: a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the dynamic X-ray image matching method according to any one of claims 1 to 7; or, Includes: a computer program product, wherein the computer program product is configured with computer program instructions that, when executed by a processor, implement the dynamic X-ray image matching method according to any one of claims 1 to 7.

10. A dynamic X-ray camera, characterized in that, include: The dynamic X-ray image matching system as described in claim 8 or 9.

Images