Image processing method and device, electronic device and computer storage medium

Abstract

The present disclosure provides an image processing method, the method comprising: scanning the organ and obtaining CT scan images of slices of different levels of the organ at different times within a movement cycle of the organ; performing calculation processing based on the CT scan images at two adjacent times of the same slice position to obtain deformation displacement data of the same slice position; determining the deformation amount and movement speed of the organ at the corresponding same slice position according to the deformation displacement data; generating a deformation distribution image according to the deformation amount and movement speed at the same slice position; in this way, the overall or local changes of the whole organ within a movement cycle can be checked, and the degree of deformation of the organ structure can be quantitatively and objectively evaluated.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing, and in particular, to an image processing method and device, an electronic device, and a computer storage medium. Background Art

[0002] For organ lesions and functional assessments that require observation of some movements, radionuclide-labeled drugs are inhaled and / or injected to observe the function of the organ. However, radionuclides decay, resulting in a limited observation time window. In addition, most of the images are anteroposterior and posterior, with poor image resolution, and can only make qualitative judgments but not quantitative judgments. Moreover, radionuclides have certain radiation risks. In addition, the radionuclides in this examination method have limited resources and a limited scope of use.

[0003] Therefore, there is a need for an image processing device that can effectively perform quantitative analysis and evaluation and has a wide range of applications. Summary of the invention

[0004] The present disclosure provides an image processing method, the method comprising: scanning the organ and obtaining CT scan images of slices of different levels of the organ at different times within a movement cycle of the organ; performing calculation processing based on the CT scan images of two adjacent times at the same slice position to obtain deformation displacement data of the same slice position; determining the deformation amount and movement speed of the organ at the same slice position according to the deformation displacement data; and generating a deformation distribution image according to the deformation amount and movement speed at the same slice position.

[0005] Optionally, the calculation and processing of the CT scan images at two adjacent moments based on the same slice position to obtain the deformation displacement data of the same slice position includes: selecting two interpretation areas corresponding to the two adjacent moments at the same slice position at the same position point in the two CT scan images corresponding to the two adjacent moments; wherein the two interpretation areas have the same size and shape, and each interpretation area has a pixel set formed by a plurality of pixel points; according to the pixel sets of the two interpretation areas corresponding to the two moments, determining a deformation displacement vector characterizing the overall movement of the pixel set from a first moment to a second moment of the two moments; wherein the first moment is earlier than the second moment; and obtaining the deformation displacement data of the same slice position according to a plurality of deformation displacement vectors between the two interpretation areas corresponding to the two moments.

[0006] Optionally, determining the deformation displacement vector representing the overall movement of the pixel set from the first moment to the second moment based on the pixel sets in the two interpretation areas corresponding to the two moments includes: counting multiple motion vectors of the pixel set in the interpretation area from the first moment to the second moment; and determining the deformation displacement vector among the multiple motion vectors that can represent the overall movement of the pixel set from the first moment to the second moment.

[0007] Optionally, determining the deformation amount and movement speed of the organ at the same slice position corresponding to the deformation displacement data includes: obtaining the movement speed according to the deformation displacement data; and obtaining the deformation amount at the same slice position according to the movement speed.

[0008] Optionally, generating a deformation distribution image according to the deformation amount and the movement speed at the same slice position includes: normalizing the deformation amount and the movement speed to obtain display parameters; and generating a deformation distribution image according to the display parameters.

[0009] Optionally, generating a deformation distribution image according to the display parameters further includes: determining, according to the display parameters, a display color for identifying different sizes of deformation variables in the deformation distribution image; and generating a deformation distribution image according to the display color for identifying different sizes of deformation variables and a CT scan image at the initial moment of the corresponding slice.

[0010] According to a second aspect of the present disclosure, there is provided an image processing device, the device comprising: a scanning module, used to scan the organ and obtain CT scan images of slices of different levels of the organ at different times within one movement cycle of the organ; a first determination module, used to perform calculation and processing based on the CT scan images of two adjacent times at the same slice position to obtain deformation displacement data of the same slice position; a second determination module, used to determine the deformation amount and movement speed of the organ at the corresponding same slice position according to the deformation displacement data; and a generation module, used to generate a deformation distribution image according to the deformation amount and movement speed at the same slice position.

[0011] Optionally, the first determination module is configured to: select two interpretation areas corresponding to two adjacent moments at the same slice position at the same position point in two CT scan images corresponding to the two moments; wherein the two interpretation areas have the same size and shape, and each interpretation area has a pixel set formed by multiple pixel points; based on the pixel sets of the two interpretation areas corresponding to the two moments, determine a deformation displacement vector representing the overall movement of the pixel set from the first moment to the second moment of the two moments; wherein the first moment is earlier than the second moment; and based on the deformation displacement vectors between the two interpretation areas corresponding to the two moments, obtain the deformation displacement data of the same slice position.

[0012] Optionally, the first determination module is further configured to: count multiple motion vectors of the pixel set in the interpretation area from the first moment to the second moment; and determine a deformation displacement vector among the multiple motion vectors that can characterize the overall motion of the pixel set from the first moment to the second moment.

[0013] Optionally, the second determination module is configured to: obtain the movement speed according to the deformation displacement data; and obtain the deformation amount at the same slice position according to the movement speed.

[0014] Optionally, the generating module is further configured to: normalize the deformation amount and the movement speed to obtain display parameters; and generate a deformation distribution image according to the display parameters.

[0015] Optionally, the generation module is further configured to: determine the display color indicating different deformation amount sizes in the deformation distribution image according to the display parameters; and generate the deformation distribution image according to the display color indicating different deformation amount sizes and the CT scan image at the initial moment of the corresponding slice.

[0016] A third aspect of the present disclosure provides an electronic device, comprising: a memory for storing processor-executable instructions; and a processor connected to the memory; wherein the processor is configured to execute the steps in the image processing method of the above-mentioned embodiment.

[0017] A fourth aspect of the present disclosure provides a non-temporary computer-readable storage medium. When instructions in the storage medium are executed by a processor of a computer, the computer is enabled to execute the steps in the image processing method in the above-mentioned embodiment.

[0018] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: the embodiments of the present disclosure may scan the organ and obtain CT scan images of slices of different levels of the organ at different times within one movement cycle of the organ; thereby obtaining CT scan images of the whole continuous slices of the organ at each time; based on the CT scan images of two adjacent moments at the same slice position, calculation and processing are performed to obtain deformation displacement data of the same slice position, so that the deformation amount and movement speed of the organ can be analyzed and determined; in this way, a deformation distribution image can be produced, which provides a basis for quantitative analysis and has a wide range of promotion.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0021] Figure 2 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0022] Figure 3 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0023] Figure 4 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0024] Figure 5 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0025] Figure 6 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0026] Figure 7 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0027] Figure 8 A schematic diagram of displacement vector calculation of an image processing method shown in an exemplary embodiment;

[0028] Fig. 9 A schematic diagram of displacement vector calculation of an image processing method shown in an exemplary embodiment;

[0029] Fig.10 A schematic diagram of displacement vector calculation of an image processing method shown in an exemplary embodiment;

[0030] Fig.11 A schematic flow chart of an image processing method shown in an exemplary embodiment;

[0031] Fig.12 The figure is a schematic diagram of the structure of an image processing device according to an exemplary embodiment. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the embodiments of the present disclosure as detailed in the appended claims.

[0033] Combination Figure 1 As shown, the embodiment of the present disclosure provides an image processing method, the method comprising:

[0034] Step S101, scanning the organ and obtaining CT scan images of slices of different layers of the organ at different times within a movement cycle of the organ;

[0035] Step S102, performing calculation processing based on the CT scan images at two adjacent moments of the same slice position to obtain deformation displacement data of the same slice position;

[0036] Step S103, determining the deformation amount and movement speed of the organ at the same slice position according to the deformation displacement data;

[0037] Step S104: generating a deformation distribution image according to the deformation amount and movement speed at the same slice position.

[0038] In the embodiment of the present disclosure, regarding step S101, a movement cycle of the organ refers to: the time for a return movement of the organ.

[0039] In some embodiments, the organ may be an organ with a certain volume such as a lung or a stomach.

[0040] For example, if the organ is a lung, one movement cycle of the lung is a complete respiratory cycle. A respiratory cycle includes the entire process of inhalation and exhalation.

[0041] In some embodiments, regarding step S101, at each interval within a movement cycle of the organ, the entire organ is scanned to obtain slices of different levels of the organ.

[0042] Here, the time interval △t between any two adjacent moments can be calculated according to the duration T of a motion cycle and the number n of CT scan images at different moments that need to be acquired at the same slice position.

[0043] Then △t=T / n.

[0044] For example, if the duration T of a motion cycle collected is 8 s, and the number of CT scan images required at different times of the same slice position is 80, then the time interval is △t=8s / 80=0.1s.

[0045] Regarding the scanning method, it can be: CT volume scanning mode, that is, the trajectory of CT scanning is a spiral scanning trajectory.

[0046] Here, the CT volume scanning mode can examine various parts, tissues, and organs of the whole body, with fast scanning and imaging speeds, and can perform three-dimensional processing and imaging.

[0047] In this way, a 4DCT image of the organ within a movement cycle can be obtained. The meaning of the 4DCT image is: adding time dimension information on the basis of spatial 3-dimensionality. The scanning range covers the entire organ and the entire operation cycle of the organ, as well as the dynamic morphological changes of the tissue structure of the organ during the entire operation process. The changes in the tissue structure within the organ can be recorded more accurately, which is convenient for subsequent more comprehensive quantitative analysis.

[0048] In some embodiments, regarding step S102, the same slice position refers to a slice of a layer at the same position of the same organ. The number of slices obtained by scanning the same slice position corresponds to the number of selected scanning moments.

[0049] Here, performing calculation processing based on CT scan images at two adjacent moments at the same slice position means performing calculation processing based on CT scan images at a plurality of arbitrary adjacent moments at the same slice position.

[0050] Here, the deformation displacement data at the same slice position includes: data with the maximum deformation displacement between all two adjacent moments, and deformation displacement data between multiple adjacent moments.

[0051] In some embodiments, regarding the calculation processing method, a convolution operation may be performed in combination with a cross-correlation algorithm to determine the deformation displacement data.

[0052] This facilitates the subsequent quantitative analysis of deformation.

[0053] In some embodiments, regarding step S103, the movement speed can be determined according to the deformation displacement data and the time interval.

[0054] For example, the maximum movement speed of the interpretation area at the same slice position can be determined according to the ratio of the data with the maximum deformation displacement to the time interval between all two adjacent moments.

[0055] For another example, the average movement speed of the interpretation area at the same slice position can be determined according to the ratio of the average displacement data of the deformation displacement data between a plurality of adjacent moments to the time interval.

[0056] In this way, the movement speed in multiple directions can be obtained.

[0057] In some embodiments, the deformation amount can be specifically characterized by a force strain amount.

[0058] Therefore, the deformation amount can be calculated and determined based on the deformation displacement data, movement speed and related biomechanical formulas.

[0059] In some embodiments, step S104 includes: generating a deformation distribution image according to the deformation amount distribution and motion speed distribution of multiple interpretation areas at the same slice position.

[0060] Here, the slices at the same slice position can be divided into a plurality of interpretation regions, each of which has a deformation amount distribution and a motion speed distribution.

[0061] In this way, by combining the deformation amount distribution and the motion speed distribution in each interpretation area, a deformation distribution image in the slice at the same slice position can be obtained.

[0062] In other embodiments, based on the comprehensive deformation amount distribution and movement speed distribution in each interpretation area, the CT image of the corresponding slice position at the initial moment in a movement cycle can be combined to generate a deformation distribution image with an overall display effect.

[0063] In this way, it is possible to determine whether there are any abnormally deformed parts in the tissue structure of the organ based on the deformation distribution image. If so, it can be accurately determined.

[0064] In the disclosed embodiment, by collecting CT scan images of the same slice position at multiple adjacent moments, the accurate deformation amount can be determined quantitatively through deformation displacement analysis, thereby generating a deformation distribution image that can be used for quantitative and qualitative analysis.

[0065] Combination Figure 2 As shown, in the embodiment of the present disclosure, step S102 includes:

[0066] Step S1021, selecting two interpretation regions corresponding to two adjacent moments at the same slice position from two CT scan images corresponding to the two moments at the same position point; wherein the two interpretation regions have the same size and shape, and each interpretation region has a pixel set formed by a plurality of pixel points;

[0067] Step S1022, determining a deformation displacement vector representing the overall movement of the pixel set from a first moment to a second moment according to the pixel sets of the two interpretation areas corresponding to the two moments; wherein the first moment is earlier than the second moment;

[0068] Step S1023, obtaining deformation displacement data of the same slice position according to a plurality of deformation displacement vectors between the two interpretation regions corresponding to the two moments.

[0069] Combination Figure 7 As shown, regarding step S1021, at two adjacent moments at the same slice position, such as the first moment t 0 The corresponding slice has a first interpretation area 701, and at the second time t 0 The slice corresponding to +Δt has a second interpretation area 702 .

[0070] Here, the first interpretation region 701 and the second interpretation region 702 may overlap when the two corresponding slices to which they belong overlap.

[0071] In some embodiments, each reading area is a pixel set formed by a plurality of pixel points.

[0072] For example, it may be a pixel set formed by k times m pixels.

[0073] Here, both k and m are non-zero natural numbers, and k may be equal to m or may not be equal to m.

[0074] Specifically, for example, it may be a pixel set formed by 10 times 10 pixels.

[0075] In some embodiments, since the movement of a single pixel cannot be accurately determined, the overall movement of a pixel set is determined to analyze the deformation displacement of the judgment area at different times.

[0076] For example, regarding step S1022, a plurality of displacement vectors of the pixel set from the first moment to the second moment may be determined by counting a plurality of pixel points in the pixel set.

[0077] Then, the most likely displacement vector is selected from the multiple displacement vectors as the deformation displacement vector s representing the overall movement of the pixel set from the first moment to the second moment.

[0078] In this way, the deformation displacement data of the same corresponding area at the same slice position corresponding to the first interpretation area and the second interpretation area can be determined.

[0079] In some embodiments, before executing step S1021, the region of the organ that does not need to be calculated may be masked. When executing step S1022, the displacement vector s between the first interpretation region and the second interpretation region is calculated in combination with the cross-correlation algorithm.

[0080] Here, the principle of calculating the deformation displacement vector s by the cross-correlation algorithm in step S1022 can be specifically combined with Figure 8 shown.

[0081] Specifically, at the first moment t 0 The corresponding slice selects f(m, n) of the first interpretation area 701, and at the second time t 0 The slice corresponding to +Δt selects g(m, n) of the second interpretation area 702 .

[0082] Here, m and n indicate that the coordinates of the first interpretation region 701 and the second interpretation region 702 are consistent and have the same position.

[0083] Here, the digital signal transfer function relationship between the reading area f, g and the displacement vector s is as follows: Figure 8 As shown (the capital letters in the figure correspond to the lowercase letters Fourier transform).

[0084] Figure 8 In the above equation, f(m, n) represents the system input, g(m, n) represents the system output, s(m, n) represents the spatial displacement function, and d(m, n) represents the additional noise. Of course, the original samples of f(m, n) and g(m, n) must also contain noise. The main task of image analysis is to calculate the spatial displacement function s(m, n), but the appearance of noise d(m, n) makes the problem complicated. The working relationship of the whole system is:

[0085] g(m,n)=[f(m,n)*s(m,n)]+d(m,n) Formula 1.1

[0086] * represents the convolution operation of f and s. The time interval Δt obtained by shifting the two images is the average speed of the interpretation area.

[0087] In order to achieve fast calculation, assuming that the noise signal can be ignored, Fourier operation is performed on both sides of formula 1.1 to obtain:

[0088]

[0089] The uppercase letters represent the discrete Fourier transform of their lowercase counterparts. The approximate result of S(u, v) can be obtained by equation 1.1. If the effect of d(m, n) can be ignored, the inverse transform S(u, v) can recover the displacement function s(m, n).

[0090] In order to speed up the operation of the above steps, Fast Fourier Transform (FFT) is used to speed up the operation when performing discrete Fourier transform. The system analyzes the displacement from the image as follows: Fig. 9 shown.

[0091] Fig. 9 The dx and dy in the figure are the changes in the coordinates of the maximum value of the result after the inverse Fourier operation relative to the center position in the x and y directions, which are the displacement values.

[0092] Here, about Fig. 9 , input f(m, n) of the first judgment area and g(m, n) of the second judgment area, perform Fast Fourier Transform (FFT) to obtain F(u, v) and G(u, v), and then determine the maximum displacement vector s(m, n) based on F(u, v) and G(u, v). Fig. 9 The cross-correlation peak in .

[0093] In this way, the deformation amount can be quantitatively analyzed through the calculated displacement vector s(m, n).

[0094] Combination Figure 3 As shown, in the embodiment of the present disclosure, step S1022 includes:

[0095] Step S10221, counting a plurality of motion vectors of the pixel set in the reading area from the first moment to the second moment;

[0096] Step S10222: determine a deformation displacement vector among the multiple motion vectors that can represent the overall motion of the pixel set from the first moment to the second moment.

[0097] Here, the process of calculating the deformation displacement vector s by the cross-correlation algorithm can be specifically combined with Fig. 9 shown.

[0098] Regarding step S10221, combined Fig. 9 , input f(m, n) of the first judgment area and g(m, n) of the second judgment area, and perform Fast Fourier Transform (FFT) to obtain F(u, v) and G(u, v) respectively.

[0099] Then, F(u, v) and G(u, v) are operated in the spatial frequency domain to obtain the cross-correlation function of multiple motion vectors φ'(u, v) = F(u, v)G * (u,v).

[0100] G *(u,v)* represents the inverse convolution operation of G.

[0101] Regarding step S10222, combined Fig. 9 , by performing Fourier inverse operation on φ'(u,v), the cross-correlation peak is found, that is, the deformation displacement vector s(m,n) of the overall motion from the first moment to the second moment.

[0102] Here, through the above calculation, the deformation displacement vector s(m, n) can be accurately determined.

[0103] Combination Figure 4 As shown, in the embodiment of the present disclosure, step S103 includes:

[0104] Step S1031, obtaining the movement speed according to the deformation displacement data;

[0105] Step S1032: obtaining the deformation amount at the same slice position according to the movement speed.

[0106] Combination Fig. 9 As shown, regarding the step S1031, the deformation displacement vector s(m, n) is derived to obtain the movement speed in the x direction and the movement speed vx(i, j) and vy(i, j) in the y direction.

[0107] Here, the overall movement speed is obtained based on the movement speed vx(i, j) and the movement speed vx(i, j) in the x direction and the movement speed vy(i, j) in the y direction.

[0108] Here, the movement speed can be specifically the value of the displacement vector divided by the time interval △t

[0109] Since different adjacent moments have different displacement vectors and movement speeds, the maximum displacement vector and average displacement vector of the same slice position can be determined based on multiple displacement vectors at multiple different adjacent moments; and the maximum movement speed can be determined based on multiple movement speeds at multiple different adjacent moments.

[0110] Regarding step S1032, it is necessary to obtain the deformation amount at the same slice position according to the movement speed and the biomechanical formula.

[0111] For example, the transverse strain, the longitudinal strain and the shear strain are determined according to the movement speed and the displacement vector.

[0112] Here, the deformation amount includes: the maximum principal strain value (PS max ); Average principal strain value (PS mean ) and the maximum movement speed (Speed max )].

[0113] In some embodiments, the maximum principal strain value (PS max ) is calculated as follows:

[0114]

[0115] Where Exx is the transverse strain, Eyy is the longitudinal strain, and Exy and Eyx are the shear strains.

[0116] In this way, the strain amount of the same slice can be quantitatively determined.

[0117] Combination Figure 5 As shown, in the embodiment of the present disclosure, step S104 includes:

[0118] Step S1041, normalizing the deformation amount and the movement speed to obtain display parameters;

[0119] Step S1042: generating a deformation distribution image according to the display parameters.

[0120] In the embodiment of the present disclosure, regarding step S1041, the maximum principal strain value, the average principal strain value, and the maximum movement speed of the deformation variable may be normalized to obtain normalized values.

[0121] The normalized value described here conforms to a certain numerical range, such as the range from 0 to 1.

[0122] Then, the display parameters are obtained based on the normalized values.

[0123] Here, regarding step S1042, the quantitative values ​​and degree identifiers corresponding to different deformation amounts in the deformation distribution image may be determined according to the display parameters.

[0124] In this way, the distribution image can be displayed more clearly.

[0125] Combination Figure 6 As shown, the step S1042 includes:

[0126] Step S10421, determining display colors for identifying different deformation magnitudes in the deformation distribution image according to the display parameters;

[0127] Step S10422: Generate a deformation distribution image based on the display colors indicating different deformation magnitudes and the CT scan image of the corresponding slice at the initial moment.

[0128] In the disclosed embodiment, regarding step 10421, the color scale of the display color can be determined according to the display parameters; and the display color indicating different deformation magnitudes in the deformation distribution image can be determined according to the color scale of the display color.

[0129] Combination Fig.10 As shown, regarding step S10422, the obtained images of the display colors of (2) and (5) are overlapped with the CT scan image of the corresponding position at the initial time according to the position coordinates to obtain the following: Fig.10 The deformation distribution image shown in (5) in .

[0130] Furthermore, in some embodiments, the data of all slices and the three-dimensional strain distribution structure may be sorted in chronological order, and a three-dimensional deformation distribution image may be obtained.

[0131] In this way, the deformation of organs can be quantitatively analyzed, and the results of the deformation distribution images based on the full-time dynamic CT images are relatively objective, and the changes in the target area can be dynamically evaluated. It can simplify the clinical examination operation and process, and provide "one-stop" examinations for patients as much as possible, that is, while obtaining the degree of organ structure deformation, it also provides regionalized organ function assessments. Accurate quantitative analysis is conducive to long-term longitudinal follow-up. Moreover, almost all hospitals are currently equipped with high-end CT equipment, which can provide more effective information for the clinic with one examination and improve the utilization rate of medical resources. The supporting stress analysis software will have greater clinical application prospects.

[0132] Now, the following examples are provided in combination with the above embodiments:

[0133] Example 1: An image processing method.

[0134] With the decline in air quality and the increasing number of smokers under the influence of industrial development, chronic obstructive pulmonary disease (COPD) and interstitial lung disease and other chronic lung diseases have become common diseases that seriously endanger human health, seriously affecting the quality of life of patients and are also an important cause of death. The results of the "China Adult Lung Health Study" led by Academician Wang Chen in 2018 showed that the prevalence of COPD in adults aged 20 and above in my country was 8.6%, and the prevalence of people over 40 years old was as high as 13.7%. The estimated number of patients nationwide is nearly 100 million. Regular monitoring and evaluation of the lung function of these patients, and timely intervention and treatment, are very important for improving the quality of life and prognosis of patients. At present, the clinical evaluation and examination methods for lung function mainly include pulmonary function tests and pulmonary ventilation / perfusion imaging.

[0135] Pulmonary function test is one of the necessary examinations for respiratory diseases. It is completed by pulmonary function testing instruments. The pulmonary function test instruments are mainly composed of spirometers, gas analyzers and pressure gauges. Through their combination, most indicators of lung function can be measured, such as lung capacity, ventilation, diffusion, respiratory muscle strength, oxygen consumption, carbon dioxide production, etc. It is mainly used for early detection of lung and respiratory tract lesions, identification of the cause of dyspnea, determination of the location of airway obstruction, assessment of the severity of lung disease (such as the severity classification of COPD), preoperative assessment of surgical tolerance (especially chest surgery) and the incidence of postoperative complications. For long-term smokers, regular pulmonary function tests can also observe the damage to lung function, provide timely intervention treatment, and even urge patients to quit smoking.

[0136] The pulmonary function tests currently used in clinical practice have certain limitations, as follows:

[0137] 1. Pulmonary function tests need to be completed by qualified and experienced pulmonary function test technicians and cannot be widely carried out in primary medical institutions;

[0138] 2. This examination requires the patient to sit upright and cooperate well with the technician's instructions. Some elderly, frail or bedridden patients cannot cooperate with this examination. However, with the progress of aging, this group of patients will grow day by day.

[0139] 3. The results of pulmonary function tests reflect the overall functional changes of both lungs, but cannot well reflect the distribution and specific pathogenic patterns of pulmonary lesions (such as emphysema, bronchial lesions, etc.);

[0140] 4. It is difficult to establish the relationship between imaging lesions and local lung ventilation. In lung function tests, it is not possible to simultaneously evaluate imaging and lung function, thereby accurately and quantitatively measuring regional lesions and lung function. Even if there is significant disease progression in imaging, changes in lung function may not be obvious.

[0141] 5. Pulmonary function tests require close contact with patients. Combined with the current epidemic situation, its clinical application is limited. There is an urgent need for an alternative examination method that can reduce close contact.

[0142] Pulmonary ventilation / perfusion imaging is an evaluation of lung lesions and lung function by inhaling / intravenously injecting radionuclide labeled drugs (such as Tc-99m labeled albumin, aerosol, etc.). It is often used in clinical diagnosis and efficacy observation of pulmonary embolism, evaluation of airway patency and ventilation function of various lung diseases, and assistance in surgical selection and estimation of residual lung function.

[0143] The limitations of clinical application are more significant than those of pulmonary function tests: 1) This test dynamically records the isotope decay process with a narrow time window, and most of the radiographs are taken in the anteroposterior position with poor image resolution, which makes it impossible to make quantitative assessments and only allow for qualitative judgments; 2) This test poses certain radiation risks not only to patients but also to medical staff performing the operation; 3) The half-life of radionuclide-labeled drugs is short and targeted. If the patient currently gives up or does not undergo the test for various reasons, the drugs can only be scrapped and cannot be retained for storage; 4) Manufacturers of such drugs require specific qualifications and must prevent environmental pollution, so their implementation in primary hospitals and even in small and medium-sized cities is more limited.

[0144] There is currently no commercial software on the market for lung strain analysis.

[0145] The problem to be solved by this invention is to design a method that can diagnose lung diseases, which is different from traditional technologies and is based on medical imaging data combined with relevant displacement, strain and stress analysis algorithms to quantitatively analyze the degree of three-dimensional deformation of the lungs.

[0146] Combination Fig.11 As shown, the overall technical solution of the embodiment of the present disclosure is as follows:

[0147] Step S301 , completing 4D dynamic ventilation CT scanning using a wide-body detector volume scanning mode, that is, acquiring and collecting raw data.

[0148] 4D-CT is a continuous scan during the patient's breathing process, obtaining lung imaging information throughout the entire breathing process, that is, adding time dimension information on the basis of spatial 3D. The scanning range covers the entire lung, which can restore the three-dimensional structure of the lung and the dynamic morphological changes of the structure and tissue in the lung during breathing. The volume scanning mode is used during the scanning process, without moving the examination bed, and more accurately records the changes in the structure of the lung tissue.

[0149] When performing a 4D-CT scan, the CT machine needs to be equipped with a respiratory gating system, that is, it needs to detect the respiratory signal and obtain the patient's respiratory waveform. Before the CT examination, the patient is trained to breathe and fit the respiratory wave to estimate the patient's tidal volume and maximum expiratory volume. During the formal examination, the patient is given a breathing instruction (exhale or inhale) and CT data is collected with reference to the previous fitted respiratory wave and respiratory amplitude, and the patient's respiratory curve is recorded simultaneously. The acquisition cycle is a complete respiratory cycle, that is, including the entire process of inhalation and exhalation. The total acquisition time is about 8s, and the data is split and reconstructed at an interval of 0.1s to obtain data information from 80 different phases. Each phase contains the image information of the entire lung in that phase. Based on the 80 phase information, volume reconstruction can intuitively show the process of the lungs gradually expanding and collapsing in a respiratory cycle (during 8s).

[0150] Step S302: extracting and converting the acquired 4D-CT data into images

[0151] Use software to extract all-time DICOM data of different slices in 4D-CT data in groups. Convert DICOM format medical image files into TIFF or BMP universal formats suitable for image calculation and save them on the hard disk for subsequent calculations; or directly read DICOM data and place them in the data cache for subsequent calculations to prepare for subsequent calculations.

[0152] Step S303: firstly perform image pre-processing on the full-time image sequence of the same slice position to extract data, and then perform cross-correlation calculation to obtain displacement data.

[0153] Based on the CT images of the same slice position at two moments before and after, combined with the cross-correlation algorithm, the deformation displacement information of the lung tissue can be calculated. Import the full-time data of the same slice into the analysis software, such as MicroVec (MicroVec Pte Ltd, Beijing, China) analysis software of Beijing Cube World Technology, first use the Mask function to mask the irrelevant areas that are not calculated, and then use the cross-correlation algorithm to perform lung image cross-correlation calculation, calculate the maximum displacement time of the lung image data, and judge the accuracy of the result.

[0154] Regarding the cross-correlation theory, the interpretation area when analyzing the collected image: it refers to a square image of a certain size at a certain position in the image, and the pixel value of the displacement is obtained by signal processing in the interpretation area. Assume that the system obtains the following at t0 and t0+△t respectively: Figure 7 In the slice image 1 where 701 is located and the slice image 2 where 702 is located, two interpretation areas f(m, n) and g(m, n) of the same size are obtained at the same position in the slice image 1 and the slice image 2. (m, n) represents the relative position of f and g in the slice image 1 and the slice image 2 respectively. By processing f and g, the corresponding displacement s of the interpretation area can be obtained. The schematic diagram is shown in FIG. Figure 7 As shown:

[0155] The digital signal transfer function relationship between the judgment area f, g and the displacement vector s is as follows: Figure 8 As shown (the capital letters in the figure correspond to the lowercase letters Fourier transform)

[0156] Figure 8In which f(m, n) represents the system input, g(m, n) represents the system output, s(m, n) represents the spatial displacement function, and d(m, n) represents the additional noise. Of course, the original sampling of f(m, n) and g(m, n) must also contain noise. The main task of image analysis is to calculate the spatial displacement function s(m, n), but the appearance of noise d(m, n) makes the problem complicated. The working relationship of the whole system is:

[0157] g(m,n)=[f(m,n)*s(m,n)]+d(m,n) Formula 2.1

[0158] * represents the convolution operation of f and s. The time interval Δt obtained by shifting the two images is the average speed of the interpretation area.

[0159] In order to achieve fast calculation, assuming that the noise signal can be ignored, Fourier operation is performed on both sides of formula 2.1 to obtain:

[0160]

[0161] The uppercase letters represent the discrete Fourier transform of their lowercase letters. The approximate result of S(u, v) can be obtained by formula 2.1. If the effect of d(m, n) can be ignored, the inverse transform S(u, v) can restore the displacement function s(m, n).

[0162] In order to speed up the operation of the above steps, Fast Fourier Transform (FFT) is used to speed up the operation when performing discrete Fourier transform. The system analyzes the process of obtaining displacement from the image as follows: Fig. 9 shown.

[0163] Fig. 9 Here, dx and dy are the changes in the coordinates of the maximum value of the result after Fourier inverse operation relative to the center position in the x and y directions, which are the displacement values.

[0164] Step S304: Based on the calculated data, the local strain value is further calculated by the local displacement to obtain the lung strain data.

[0165] Based on the displacement information of deformation and the relevant mechanical formulas, we can calculate the distribution of lung strain. By statistically analyzing this strain distribution and combining it with clinical medical records, we can establish a new lung disease analysis and diagnosis program.

[0166] According to formula 2.3, the strain-related parameter values ​​[maximum principal strain value (PS max ), average principal strain value (PS mean ), Maximum Movement Speed max)], and then normalize the results.

[0167]

[0168] Where Exx is the transverse strain, Eyy is the longitudinal strain, and Exy and Eyx are the shear strains.

[0169] Step S305: Fusing different strain-related parameter values ​​with the original CT image in pseudo-color.

[0170] Strain analysis uses biomechanical analysis methods to divide the airway and lung tissue according to pixels, regards each pixel as a displacement vector, calculates the overall displacement change of the macro vector for quantitative analysis, and observes the mechanical changes and regional deformation of the lungs. Strain analysis can quantitatively extract the degree of strain of the lung parenchyma structure during breathing, which helps to further explore the related mechanisms of changes in lung structure and function.

[0171] The calculated strain distribution and the original CT image are superimposed and displayed to intuitively display the strain distribution of the current slice, which makes it easier for doctors to locate the lesion at a later stage.

[0172] Similar to the original 4D-CT reconstruction of the 3D lung structure, this superimposed display of images can also reconstruct the 3D lung strain distribution structure. By arranging the data of all slices and the 3D strain distribution structure in chronological order, a 3D strain image can be obtained.

[0173] In the embodiments of the present disclosure, the above technical solution can bring the following technical effects:

[0174] Existing clinical pulmonary function tests reflect the overall changes of the entire lung as a functional unit and are unable to assess the distribution and pathogenicity of lesions within the lung. Lung strain analysis based on dynamic ventilation CT can not only reflect the above-mentioned anatomical changes, but also perform quantitative assessment.

[0175] Lung strain analysis based on dynamic ventilation CT can further reduce the functional unit of quantitative analysis, such as a lung lobe or even a lung segment, and can better interpret the heterogeneity of changes in the lungs (such as COPD, asthma, interstitial fibrosis, etc.).

[0176] Dynamic ventilation CT examination can complete the scan at the same time as a routine CT examination. It is easy to operate and does not require direct contact between the examiner and the patient being examined (pulmonary function examination requires close guidance from professional technicians throughout the process, which is not conducive to epidemic prevention and control). Under the current severe epidemic situation, non-contact pulmonary function assessment is more clinically significant.

[0177] The lung strain analysis results based on dynamic ventilation CT are relatively objective and can dynamically evaluate the changes in the target area. It can simplify clinical examination operations and processes, and provide "one-stop" examinations for patients with lung diseases as much as possible, that is, while obtaining changes in lung structure and morphology, it can also provide regionalized lung function assessments. Accurate quantitative analysis is conducive to long-term longitudinal follow-up. Moreover, almost all county and city hospitals across the country are currently equipped with high-end CT equipment, which can provide more effective information for the clinic with one examination and improve the utilization rate of medical resources. The supporting stress analysis software will have greater clinical application prospects.

[0178] Example 2: An image processing method, comprising:

[0179] Step S401, extract lung strain-related data and CT composite image, mark the abnormal deformation area in the lung. Extract the same slice of 4D-CT at all times and prepare data;

[0180] Step S402: Using the Mask function, extract the lung area data that needs to be cross-correlated.

[0181] Step S403, importing the slice data into MicroVec software, and using the cross-correlation algorithm to calculate the displacement vector data of the region.

[0182] Step S404, obtaining data such as maximum principal strain and maximum displacement velocity of deformation through a biomechanical strain calculation method.

[0183] Step S405: The strain-related data is fused with the original CT using image processing software.

[0184] Example 3: Application of an image processing method.

[0185] In one embodiment, the image processing method is used for patients suffering from chronic respiratory diseases who need to monitor their lung function regularly.

[0186] Patients with chronic respiratory diseases need to monitor their lung function regularly, including but not limited to chronic obstructive pulmonary disease, long-term smokers, occupational diseases such as pneumoconiosis, and interstitial lung diseases such as interstitial fibrosis. The advantage of dynamic ventilation CT lung strain analysis is that it can detect focal lesions or mild lesions that have not yet affected global lung function, determine the impact of focal lung lesions on ventilation function, sensitively detect changes in lung ventilation function, evaluate the lung ventilation perfusion ratio, and determine the relationship between imaging lesions and local lung ventilation. In severe epidemic situations, as an alternative to pulmonary function tests, regular monitoring of lung function changes in patients with COPD (or chronic lung disease patients with relevant needs) is used.

[0187] Example 4: Application of an image processing method.

[0188] In one embodiment, it can be applied to lung function assessment before tumor resection and predict residual lung function.

[0189] For patients with larger tumors or other lung diseases such as interstitial lung disease, preoperative prediction of residual lung function is particularly important for the choice of treatment. Lung cancer is the third leading cause of death in my country. In addition to early-stage lung cancer, surgical resection of advanced lung cancer needs to consider residual lung function. Most patients in my country are already in the middle and late stages when they are discovered, and chemotherapy, radiotherapy, and targeted drug treatment may cause secondary interstitial lung disease and lead to damage to lung function. Dynamic ventilation CT lung strain analysis can be used to assess tolerance to surgical operations (especially thoracic surgery), and to perform preoperative simulated lobectomy to estimate the possibility of postoperative complications. For patients who already have obstructive lung disease or interstitial lung disease combined with lung tumors and need to undergo resection, preoperative lung function can be quantified and postoperative residual lung function can be predicted.

[0190] Example 5: Application of an image processing method.

[0191] In one embodiment, it can be applied to the evaluation of lung function in the recovery period after acute lung injury and the monitoring of critically ill patients.

[0192] Acute lung injury and critically ill patients (such as infected patients) are unable to cooperate with pulmonary function tests, but CT examinations or reexaminations are relatively routine clinical examination procedures, and there is no need to consider the spread of the epidemic caused by close contact. During the CT reexamination, local damage to the lungs is evaluated, and the absorption of lesions in the lungs is observed. At the same time, 4D dynamic ventilation CT is used to compare the improvement of lung function before and after the two times, which provides timely feedback for clinical treatment and will greatly accelerate the progress of clinical diagnosis and treatment. It is of great help to improve both patient prognosis and clinical treatment.

[0193] Example 6: Application of an image processing method.

[0194] In one embodiment, it can be applied to the evaluation of drug side effects or efficacy.

[0195] During the development of new drugs, changes in the lung structure may occur, such as damage to the peripheral vascular structure and interstitial structure of the lungs. Routine pulmonary function tests cannot detect abnormalities in the early stages. Although routine chest CT imaging is now a routine item, it has a low weight due to the lack of quantitative indicators. Dynamic ventilation CT lung strain analysis can monitor lung function for drugs with potential side effects on lung function, especially targeted drugs in recent years, or drugs used to treat obstructive pulmonary disease, interstitial lung disease, etc., and can evaluate lung function while performing imaging evaluation.

[0196] Combination Fig.12As shown, in an embodiment of the present disclosure, an image processing device 400 is provided, and the device 400 includes:

[0197] A scanning module 401 is used to scan the organ and obtain CT scan images of slices of different layers of the organ at different times within a movement cycle of the organ;

[0198] A first determination module 402 is used to perform calculation processing based on CT scan images at two adjacent moments of the same slice position to obtain deformation displacement data of the same slice position;

[0199] A second determination module 403 is used to determine the deformation amount and movement speed of the organ at the same slice position according to the deformation displacement data;

[0200] The generating module 404 is used to generate a deformation distribution image according to the deformation amount and movement speed at the same slice position.

[0201] In the embodiment of the present disclosure, the first determining module 402 is configured as follows:

[0202] In two CT scan images corresponding to two adjacent moments at the same slice position, two interpretation regions corresponding to the two moments at the same position point are selected; wherein the two interpretation regions have the same size and shape, and each interpretation region has a pixel set formed by a plurality of pixel points;

[0203] Determine, according to the pixel sets of the two interpretation areas corresponding to the two moments, a deformation displacement vector representing the overall movement of the pixel set from a first moment to a second moment of the two moments; wherein the first moment is earlier than the second moment;

[0204] The deformation displacement data of the same slice position is obtained according to the deformation displacement vectors between the two interpretation areas corresponding to the two moments.

[0205] In the embodiment of the present disclosure, the first determining module 402 is further configured to:

[0206] Counting a plurality of motion vectors of a pixel set in the reading area from the first moment to the second moment;

[0207] A deformation displacement vector among the plurality of motion vectors that can represent the overall motion of the pixel set from the first moment to the second moment is determined.

[0208] In the embodiment of the present disclosure, the second determining module 403 is configured as follows:

[0209] According to the deformation displacement data, the movement speed is obtained;

[0210] The deformation amount at the same slice position is obtained according to the movement speed.

[0211] In the embodiment of the present disclosure, the generating module 404 is further configured to:

[0212] Normalizing the deformation amount and the movement speed to obtain display parameters;

[0213] A deformation distribution image is generated according to the display parameters.

[0214] In the embodiment of the present disclosure, the generating module 404 is further configured to:

[0215] According to the display parameters, display colors for identifying different deformation magnitudes in the deformation distribution image are determined; and according to the display colors for identifying different deformation magnitudes and the CT scan image at the initial moment of the corresponding slice, a deformation distribution image is generated.

[0216] In an embodiment of the present disclosure, an electronic device is provided, the electronic device comprising:

[0217] processor;

[0218] a memory for storing processor-executable instructions;

[0219] The processor is used to implement the steps in the above-mentioned image processing method when running the computer service.

[0220] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0221] In an embodiment of the present disclosure, a storage medium is provided, wherein the storage medium contains computer executable instructions, and the computer executable instructions are executed by a processor to implement the steps in the above-mentioned image processing method.

[0222] Alternatively, if the above-mentioned integrated unit of the embodiment of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes.

[0223] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. An image processing method, characterized in that: The method comprises: Scanning the organ and obtaining CT scan images of slices of different layers of the organ at different times within a movement cycle of the organ; In two CT scan images corresponding to two adjacent moments at the same slice position, two interpretation regions corresponding to the two moments at the same position point are selected; wherein the two interpretation regions have the same size and shape, and each interpretation region has a pixel set formed by a plurality of pixel points; Determine, according to the pixel sets of the two interpretation areas corresponding to the two moments, a deformation displacement vector representing the overall movement of the pixel set from a first moment to a second moment of the two moments; wherein the first moment is earlier than the second moment; Obtaining deformation displacement data of the same slice position according to a plurality of deformation displacement vectors between the two interpretation areas corresponding to the two moments; Determining the deformation amount and movement speed of the organ at the same slice position according to the deformation displacement data; A deformation distribution image is generated based on the deformation amount and movement speed at the same slice position.

2. The image processing method according to claim 1, characterized in that: The step of determining, based on the pixel sets of the two interpretation areas corresponding to the two moments, a deformation displacement vector representing the overall movement of the pixel set from the first moment to the second moment of the two moments comprises: Counting a plurality of motion vectors of a pixel set in the reading area from the first moment to the second moment; A deformation displacement vector among the plurality of motion vectors that can represent the overall motion of the pixel set from the first moment to the second moment is determined.

3. The image processing method according to claim 1, characterized in that: Determining the deformation amount and movement speed of the organ at the same slice position according to the deformation displacement data includes: According to the deformation displacement data, the movement speed is obtained; The deformation amount at the same slice position is obtained according to the movement speed.

4. The image processing method according to claim 2, characterized in that: The step of generating a deformation distribution image according to the deformation amount and movement speed at the same slice position includes: Normalizing the deformation amount and the movement speed to obtain display parameters; A deformation distribution image is generated according to the display parameters.

5. The image processing method according to claim 4, characterized in that: The step of generating a deformation distribution image according to the display parameters further comprises: Determining, according to the display parameters, display colors for identifying different sizes of deformation amounts in the deformation distribution image; A deformation distribution image is generated according to the display colors indicating different deformation magnitudes and the CT scan image of the corresponding slice at the initial moment.

6. An image processing device, characterized in that: The device comprises: A scanning module, used for scanning the organ and obtaining CT scan images of slices of different layers of the organ at different times within a movement cycle of the organ; A first determination module is used to select two interpretation areas corresponding to two adjacent moments at the same slice position from two CT scan images corresponding to the two moments at the same position point; wherein the two interpretation areas have the same size and shape, and each interpretation area has a pixel set formed by a plurality of pixel points; according to the pixel sets of the two interpretation areas corresponding to the two moments, determine a deformation displacement vector representing the overall movement of the pixel set from a first moment to a second moment of the two moments; wherein the first moment is earlier than the second moment; and according to a plurality of deformation displacement vectors between the two interpretation areas corresponding to the two moments, obtain deformation displacement data of the same slice position; A second determination module is used to determine the deformation amount and movement speed of the organ at the same slice position according to the deformation displacement data; The generation module is used to generate a deformation distribution image according to the deformation amount and movement speed at the same slice position.

7. The image processing device according to claim 6, characterized in that: The first determining module is further configured to: Counting a plurality of motion vectors of a pixel set in the reading area from the first moment to the second moment; A deformation displacement vector among the plurality of motion vectors that can represent the overall motion of the pixel set from the first moment to the second moment is determined.

8. The image processing device according to claim 6, characterized in that: The second determination module is configured as follows: According to the deformation displacement data, the movement speed is obtained; The deformation amount at the same slice position is obtained according to the movement speed.

9. The image processing device according to claim 7, characterized in that: The generation module is further configured as follows: Normalizing the deformation amount and the movement speed to obtain display parameters; A deformation distribution image is generated according to the display parameters.

10. The image processing device according to claim 9, characterized in that: The generation module is further configured as follows: According to the display parameters, display colors for identifying different deformation magnitudes in the deformation distribution image are determined; and according to the display colors for identifying different deformation magnitudes and the CT scan image at the initial moment of the corresponding slice, a deformation distribution image is generated.

11. An electronic device, characterized in that: include: a memory for storing processor-executable instructions; A processor connected to the memory; The processor is configured to execute the image processing method provided in any one of claims 1 to 5. 12 . A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of a computer, enables the computer to perform the image processing method provided in any one of claims 1 to 5.

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