Analysis method of dr imaging and dr imaging apparatus

Abstract

An analysis method and a DR imaging device for DR imaging, the method comprising: acquiring a digital radiography DR image of a target object by the DR imaging device, the DR image comprising at least one of a DR original image and an image processed from the DR original image; extracting an image feature from the DR image; acquiring a device parameter feature according to a device parameter of the DR imaging device, and / or acquiring an object parameter feature according to an object parameter of the target object; determining a DR feature index according to the image feature, and the device parameter feature and / or the object parameter feature, the DR feature index being used to represent a comprehensive level between a radiation dose of X-ray suffered by the target object and an image quality of the DR image. According to the image feature and the parameter feature of the DR image, the DR feature index which can objectively represent the comprehensive level between the radiation dose of X-ray suffered by the target object and the image quality of the DR image is obtained, so as to provide an objective judgment basis for an operator.

Description

Technical Field

[0001] The present application relates to the field of medical imaging technology, and more specifically to a DR imaging analysis method and a DR imaging device. Background Art

[0002] Digital radiography (DR) images, a common type of medical digital image, are widely used in physical examinations and routine medical imaging diagnostics. With the development of digital X-ray detectors and digital image processing systems, DR images often produce diagnostic images over a wide range of exposure doses. However, this process often relies on the operator's experience to determine the final image quality, and the accuracy of this judgment is easily affected by various factors, including experience differences, subjective differences, and post-processing.

[0003] To determine the exposure dose of DR images, one approach is to use the exposure index (EI) to indicate the size of a single exposure. However, the exposure index is usually simply calculated based on the grayscale information of the DR image and cannot adapt well to complex and changing clinical scenarios. Summary of the Invention

[0004] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] An embodiment of the present invention provides a method for analyzing DR imaging, the method comprising:

[0006] Acquire a digital X-ray DR image of the target object by a DR imaging device, wherein the DR image includes at least one of an original DR image and an image obtained by processing the original DR image;

[0007] Extracting image features from the DR image, the image features comprising at least one of grayscale entropy features, texture features, noise features, gradient features, and divergence features;

[0008] Acquiring device parameter characteristics according to device parameters of the DR imaging device, and / or acquiring object parameter characteristics according to object parameters of the target object;

[0009] A DR feature index is determined based on the image features, the device parameter features and / or the object parameter features. The DR feature index is used to characterize the comprehensive level between the X-ray radiation dose received by the target object and the image quality of the DR image.

[0010] Another aspect of the present invention provides a DR imaging device, the DR imaging device comprising: an X-ray generator, a detector, a processor, and a display;

[0011] The X-ray generator is used to generate X-rays, emit X-rays toward a target tissue site of a target object, and control the X-rays to pass through the target tissue site;

[0012] The detector is used to receive X-rays after passing through the target tissue site;

[0013] The processor is configured to process the X-rays after passing through the target tissue site to obtain a digital X-ray photography (DR) image, wherein the DR image includes at least one of an original DR image and an image obtained by processing the original DR image;

[0014] The processor is further configured to obtain a DR characteristic index according to the DR imaging analysis method described above, wherein the DR characteristic index is configured to represent a comprehensive level between the X-ray radiation dose received by the target object and the image quality of the DR image;

[0015] The display is used to display the DR characteristic index.

[0016] The DR imaging analysis method and DR imaging device of the embodiment of the present invention obtain a DR feature index that can objectively characterize the comprehensive level between the X-ray radiation dose received by the target object and the image quality of the DR image based on the image characteristics of the DR image and the device parameter characteristics and / or the object parameter characteristics, thereby providing an objective judgment basis for the operator. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 A schematic flow chart showing a method for analyzing DR imaging according to one embodiment of the present invention;

[0019] Figure 2 A schematic diagram illustrating model training and DR imaging analysis using the trained model according to one embodiment of the present invention;

[0020] Figure 3 A schematic block diagram of a DR imaging device according to an embodiment of the present invention is shown;

[0021] Figure 4 A structural block diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present application more apparent, the following is a detailed description of example embodiments of the present application with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in this application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this application.

[0023] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present application. However, it will be apparent to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, some technical features well known in the art are not described in order to avoid confusion with the present application.

[0024] It should be understood that the present application can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, providing these embodiments will make the disclosure thorough and complete and will fully convey the scope of the present application to those skilled in the art.

[0025] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present application. When used herein, the singular forms "a", "an", and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0026] In order to fully understand the present application, a detailed structure will be provided in the following description to illustrate the technical solution proposed by the present application. The optional embodiments of the present application are described in detail below. However, in addition to these detailed descriptions, the present application may also have other implementation methods.

[0027] Next, first refer to Figure 1 The following describes a method 100 for analyzing DR imaging according to one embodiment of the present application. Figure 1 As shown, the DR imaging analysis method 100 may include the following steps:

[0028] In step S110, a digital X-ray DR image of the target object is acquired by a DR imaging device, wherein the DR image includes at least one of a DR original image and an image obtained by processing the DR original image;

[0029] In step S120, image features are extracted from the DR image, where the image features include at least one of grayscale entropy features, texture features, noise features, gradient features, and divergence features;

[0030] In step S130, device parameter characteristics are acquired according to device parameters of the DR imaging device, and / or object parameter characteristics are acquired according to object parameters of the target object;

[0031] In step S140, a DR feature index is determined based on the image features, the device parameter features and / or the object parameter features. The DR feature index is used to characterize the comprehensive level between the X-ray radiation dose received by the target object and the image quality of the DR image.

[0032] The DR imaging analysis method 100 of the embodiment of the present application extracts image features and parameter features from a DR image and, based on the extracted image features and parameter features, obtains a DR feature index that objectively characterizes the comprehensive relationship between the X-ray radiation dose received by the target object and the image quality of the DR image. This provides an objective basis for judgment by the operator and can guide in-hospital quality control and dose reduction imaging. The DR feature index may also be referred to as a DR feature index, a DR objectification index, or any other appropriate name.

[0033] In step S110, the DR image may be acquired locally from the DR imaging device or from another external device, which is not specifically limited herein. Acquiring the DR image locally may involve the DR imaging device acquiring the DR image in real time or acquiring the DR image non-real-time and stored locally on the DR imaging device.

[0034] In one possible implementation, the specific process for acquiring a DR image locally involves controlling a DR imaging device to emit X-rays toward a target tissue site of a target subject and controlling the X-rays to pass through the target tissue site; receiving the X-rays after they pass through the target tissue site; and processing the X-rays after they pass through the target tissue site to obtain a digital radiographic DR image. The target tissue site can be a tissue site to be examined in a human or other animal body, such as the head or abdomen.

[0035] The DR image in embodiments of the present invention can be at least one of an original DR image and a processed DR image. During the DR imaging process, X-rays emitted by an X-ray generator are attenuated to varying degrees after passing through the human body and are received by a detector. For example, the upper layer of the detector comprises an X-ray conversion medium, which has photoconductive properties and converts X-rays into electronic signals, generating positive and negative charges. These charges, under the action of a bias voltage, migrate along an electric field in the form of currents and are collected by the detector's array of detection units, each corresponding to a pixel in the DR image. Amplification and analog-to-digital conversion of the electrical signal can produce a digital signal, namely, the original DR image. The original DR image is a grayscale image without post-processing such as contrast adjustment or brightness adjustment. The processed DR image can be an image obtained by subjecting the original DR image to some transformation or post-processing such as image contrast or brightness adjustment. In other words, the processed DR image can be considered an image obtained by subjecting the original DR image to any post-processing, without specific limitations herein.

[0036] In step S120, image features are extracted from the DR image. The image features include at least one of grayscale entropy features, texture features, noise features, gradient features, and divergence features. That is, the image features extracted in step S120 may be all of the grayscale entropy features, texture features, noise features, gradient features, and divergence features, or any one or more of these features. The grayscale entropy features, texture features, noise features, gradient features, and divergence features reflect the grayscale information, texture information, noise information, gradient information, and divergence information of the DR image, respectively. Multi-dimensional image features are conducive to obtaining a more accurate DR feature index.

[0037] In one embodiment, the grayscale entropy feature is the information content obtained by statistically filtering out redundant grayscale signal sources in the DR image. Based on the transmission characteristics of X-ray quanta converted into digital signals by the detector of the DR system, each independent grayscale level in the DR image can be regarded as a signal source, and the DR imaging process can be regarded as a process of transmitting information through the grayscale signal source. Among all the grayscale levels, some grayscale levels do not have grayscale values ​​and are therefore regarded as redundant grayscale signal sources. Because the grayscale entropy feature extraction process of the embodiment of the present invention filters out redundant grayscale signal sources, it is possible to extract more effective image information.

[0038] For example, the grayscale entropy feature can be extracted from the DR image in the following manner:

[0039] First, the first probability statistical distribution pH(i) of each grayscale signal source in the DR image is obtained, where i is the image grayscale value of the DR image. Then, according to the size of the first probability statistical distribution, the first probability statistical distribution pNH(i) of the non-redundant grayscale signal source is determined from the first probability statistical distribution pH(i) of the grayscale signal source. Exemplarily, if the first probability statistical distribution pH(i) is greater than 0, it is determined to be the first probability statistical distribution pNH(i) of the non-redundant grayscale signal source. Conversely, if the first probability statistical distribution pH(i) is greater than 0, it is determined to be the first probability statistical distribution pNH(i) of the non-redundant grayscale signal source, that is:

[0040]

[0041] After obtaining the first probability statistical distribution pNH(i) of the non-redundant grayscale signal source, the second probability statistical distribution pH'(i) of each non-redundant grayscale signal source is obtained according to the ratio of the first probability statistical distribution pNH(i) of each non-redundant grayscale signal source to the sum of the first probability statistical distributions ∑pNH(i) of all non-redundant grayscale signal sources, that is:

[0042] pH'(i)=pNH(i) / ∑pNH(i) Formula (2)

[0043] Finally, entropy is calculated based on the second probability statistical distribution pH'(i) of the non-redundant grayscale signal source to obtain the grayscale entropy feature H(Image). The entropy calculation method includes but is not limited to taking the natural logarithm ln with e as the base or taking the logarithm with other values ​​as the base, which can be expressed as follows:

[0044]

[0045] Thus, the grayscale entropy feature can be obtained after filtering out redundant grayscale signal sources.

[0046] Texture features represent the spatial relationship between different grayscale values ​​in a DR image and can reflect abstract features of the changing patterns of human tissue. For example, a statistical method can be used to extract texture features from DR images. This method derives the statistical characteristics of the texture region based on the grayscale attributes of the pixel and its neighborhood. The following describes the specific details of statistical methods for extracting texture features, but other methods for extracting texture features may also include geometric methods, model methods, or other suitable texture feature extraction methods.

[0047] Exemplarily, when using statistical methods to extract texture features from DR images, a texture feature description matrix is ​​first obtained based on the grayscale value of each pixel in the DR image; then, at least one two-dimensional component of the texture feature description matrix is ​​extracted as at least one texture feature.

[0048] In one embodiment, the texture feature description matrix describes the distance between different pixels in the DR image and the change in the grayscale value in the direction between different pixels. Assuming that the DR image Image = f(x, y), its texture feature description matrix P(i, j) is:

[0049] P(i,j)=#{(x1,y1),(x2,y2)∈M*N|f(x1,y1)=i,f(x2,y2)=j} Formula (4)

[0050] Among them, #(x) represents the number of elements in the set x. Assuming that the distance between two points (x1, y1) and (x2, y2) in the image is k, the texture feature description matrix in different directions l can be expanded to P(i, j, k, l). The texture feature description matrix can be expanded by counting the changes in grayscale values ​​at different distances and directions to obtain information in more dimensions. For example, in order to calculate the actual eigenvalues ​​at different angles, super-resolution interpolation can be performed on the DR image in each direction to obtain the sub-pixel grayscale values ​​corresponding to the new target position, and the texture feature description matrix in different directions can be obtained based on the sub-pixel grayscale values.

[0051] After obtaining the texture feature description matrix, extract at least one two-dimensional component of the texture feature description matrix to obtain a texture feature, which reflects the key characteristics of the texture feature description matrix. Exemplarily, the texture feature includes at least one of the following:

[0052] The first texture feature P1 is used to calculate the value distribution of the texture feature description matrix and the overall distribution of texture changes in the DR image. The value of P1 can reflect the clarity of the DR image and the depth of the texture grooves. The deeper the texture grooves, the larger the P1 value, and vice versa, the shallower the grooves, the smaller the P1 value.

[0053] The second texture feature P2 is used to count the similarity between the value distribution in the texture feature description matrix and the parallel and normal directions in the DR image. The larger the P2 value, the greater the similarity of the image grayscale in different directions.

[0054] The third texture feature P3 is used to statistically analyze the value distribution in the texture feature description matrix and the uniformity of the grayscale change distribution in the DR image. The size of the P3 value can reflect the uniformity of the image grayscale distribution and the coarseness of the texture. The larger the P3 value, the more stable the texture change of the DR image;

[0055] The fourth texture feature P4 is used to measure the distribution of values ​​in the texture feature description matrix and the local variation of the texture of the DR image. The larger the P4 value is, the stronger the regularity of the texture is.

[0056] The texture features extracted from the texture feature description matrix are not limited to the above four types. In other embodiments, other two-dimensional components of the texture feature description matrix may also be extracted as texture features.

[0057] Image noise mainly includes X-ray quantum noise, and the distribution of X-ray quanta obeys Poisson distribution, and its variance is proportional to the average number of quantum detections. Based on this characteristic of X-ray quantum noise, the fluctuation degree of X-ray quanta can be statistically analyzed from the DR image as a noise feature, that is, the noise feature reflects the fluctuation degree of X-ray quanta in the DR image.

[0058] In one embodiment, extracting noise features from a DR image includes the following steps: filtering the DR image to obtain a high-frequency image, where the high-frequency image obtained from the DR image mainly contains noise information; calculating the root mean square of the neighborhood of each pixel point in the high-frequency image to obtain a noise distribution image; and performing statistics on the distribution of noise values ​​in the noise distribution image to obtain the noise features.

[0059] As one implementation, filtering the DR image to obtain a high-frequency image includes: performing low-frequency filtering on the DR image I to remove low-frequency components therein to obtain a low-frequency image I1; and obtaining the high-frequency image I2 based on the difference between the low-frequency image and the DR image. In other implementations, high-frequency filtering may be performed directly on the DR image I to obtain the high-frequency image I2.

[0060] For example, the DR image I can be subjected to Gaussian low-pass filtering to obtain a low-frequency image I1. Since the image is a two-dimensional signal, a two-dimensional Gaussian function is used for Gaussian low-pass filtering, and the two-dimensional Gaussian filter kernel is:

[0061]

[0062] The high-frequency image I2 can be obtained by calculating the difference between the low-frequency image I1 and the image I, i.e., I2 = I1 - I. The noise distribution image I3 can be obtained by extracting the effective information from the high-frequency image I2. In one embodiment, the noise distribution image I3 is an image formed by calculating the root mean square of the neighborhood of each pixel in the high-frequency image. The root mean square calculation method includes using the L1 norm or other approximate or equivalent measures, such as:

[0063]

[0064] Among them, I3(i,j) is the value of the noise distribution image I3 at the pixel point (i,j), and I2(l,k) is the value of the high frequency I2 at the pixel point (l,k).

[0065] After obtaining the noise distribution image I3, the noise value distribution thereof is statistically analyzed to obtain the acoustic feature. For example, the noise value interval with the highest noise value distribution probability in the noise distribution image can be determined, and the noise value of the noise value interval with the highest noise value distribution probability is used as the noise feature.

[0066] Specifically, first, the pixel values ​​of the noise distribution image I3 are divided into M intervals, with the pixel value interval of each interval being d. The histogram vector is initialized to h with a length of M. Each component h(i) of the initialized histogram represents the number of pixel values ​​in the i-th interval. For the pixel point (i, j) in the noise distribution image I3, the interval corresponding to the pixel value of this point is R[I3(i, j)]. After traversing each pixel point of the noise distribution image I3 and counting h(R(I3(i, j))), the maximum value of its main peak is max(h), corresponding to the interval R0 = arg max(h). Then, the noise distribution probability R0*d is calculated as the noise feature.

[0067] In some embodiments, image features also include gradient features. Due to the varying absorption coefficients of X-rays after penetrating different tissues in the human body, different tissue regions are distributed regionally in the DR image. Different degrees of boundaries exist between different tissue regions, and the clarity of these boundaries can be quantified by extracting gradient features. Specifically, gradient features are used to characterize the clarity of boundaries between different tissue regions in the DR image. In one embodiment, extracting gradient features from a DR image includes: determining the regional distribution of different tissues in the DR image; and obtaining the clarity of the boundaries between the different tissue regional distributions as gradient features.

[0068] For example, the gradient feature can be calculated as follows:

[0069]

[0070] Among them, Grad(x,y) is used to represent the gradient size at the coordinate (x,y), and Image(x,y) is used to represent the pixel value at the coordinate (x,y).

[0071] In some embodiments, the image features also include divergence features. In DR images, the boundaries between different tissues are not strict boundaries, but rather have a certain degree of transition. The strength and / or trend consistency of this transition can be quantified by extracting the divergence feature. That is, the divergence feature is used to characterize the strength and / or trend consistency of the transition between the boundaries of different tissues in the DR image. In one embodiment, extracting the divergence feature from the DR image includes: determining the regional distribution of different tissues in the DR image; and obtaining the strength and / or trend consistency of the transition between the boundaries of the different tissue regional distributions as the divergence feature.

[0072] For example, the divergence feature can be calculated as follows:

[0073]

[0074] Among them, Diver(x,y) is used to represent the divergence size at the coordinate (x,y), Image(x,y) is used to represent the pixel value at the coordinate (x,y), and actan represents the inverse tangent function.

[0075] In some embodiments, in addition to at least one of the above image features, other image features can be extracted from the DR image for obtaining the DR feature index, such as grayscale gradient, grayscale mean, variance, pixel value information, signal-to-noise ratio, contrast-to-noise ratio, etc. The specific features can be determined according to actual conditions and are not specifically limited here.

[0076] In step S130, device parameter features are acquired based on device parameters of the DR imaging device, and / or object parameter features are acquired based on object parameters of the target object. The device parameter features and object parameter features may be collectively referred to as parameter features.

[0077] The device parameter characteristics can be obtained based on device parameters that may affect the image quality of DR images or the X-ray radiation dose received by the target object. The device parameter characteristics can be obtained based on parameter settings of the DR imaging device or based on parameter detection values. Once the device parameters are obtained, they can be calculated based on specific calculation rules to obtain the device parameter characteristics.

[0078] In one embodiment, device parameter characteristics include tube voltage and / or tube current. Tube voltage and tube current are the primary factors affecting exposure dose. Specifically, the X-ray generator of a DR imaging device primarily comprises a high-voltage generator and a tube. The high-voltage generator supplies tube voltage to the tube, accelerating electrons generated by the tube's filament under the action of the tube voltage. These electrons strike the target, generating X-rays. The electrons received by the target form the tube current. Generally speaking, the tube voltage controls the energy and quality of the X-ray beam generated by the tube, thereby determining the penetrating power of the X-rays. This penetrating power determines the amount of X-rays that impinge on the detector, thereby determining the contrast of the DR image. The tube current controls the amount of X-rays in the X-ray beam, which determines the upper limit of the amount of X-rays that can impinge on the detector during DR imaging and, therefore, the density of the DR image. Excessively high tube voltage and current will result in excessive X-rays penetrating the body, increasing image noise. Excessively low tube voltage and current will result in insufficient X-rays penetrating the body, hindering the display of clear image details. The tube voltage parameter characteristics and tube current parameter characteristics can reflect whether the tube voltage and tube current are appropriate, and are ultimately reflected in the DR characteristic index.

[0079] When acquiring the tube voltage parameter characteristics, the tube voltage parameter characteristics may be acquired based on the tube voltage setting value of the console of the DR imaging device, or based on the tube voltage received value of the high-voltage generator of the DR imaging device. Similarly, when acquiring the tube current parameter characteristics, the tube current parameter characteristics may be acquired based on the tube current setting value of the console of the DR imaging device, or based on the tube current received value of the high-voltage generator of the DR imaging device.

[0080] Device parameter characteristics may also include exposure time parameter characteristics. Specifically, the exposure process begins when a high-voltage generator generates high voltage to drive the tube to produce X-rays. During this process, a detector receives the X-rays and converts them into electrical signals. The exposure time determines whether the X-ray dose meets the required standards. For example, the exposure time parameter characteristics can be obtained based on the exposure time setting value of the DR imaging device's console, the exposure time parameter characteristics can be obtained based on the exposure time received value of the DR imaging device's high-voltage generator, or the exposure time parameter characteristics can be obtained based on the feedback value of the DR imaging device's automatic exposure control device.

[0081] Device parameter characteristics may also include imaging position and / or body shape parameter characteristics. Different imaging positions / body shapes require different tube voltages, tube currents, or exposure parameters. For example, the imaging position and / or body shape parameter characteristics can be obtained based on the body position and / or body shape settings on the DR imaging device's console, or based on visual images captured by the DR imaging device's camera.

[0082] Device parameter characteristics may also include photographic distance parameter characteristics. The photographic distance generally refers to the source image distance (SID), which is the distance between the X-ray generator and the detector. The SID can affect the size of the irradiation field. For example, the photographic distance parameter characteristics can be obtained based on the photographic distance setting value of the DR imaging device's console, or based on the photographic distance measured by a first measuring device of the DR imaging device, where the first measuring device may be an additional measuring device of the beam limiter of the DR imaging device.

[0083] Device parameter characteristics may also include irradiation field parameter characteristics. The irradiation field is the area of ​​X-ray exposure to the body surface. The size of the irradiation field is closely related to the X-ray radiation dose and the quality of the DR image. The irradiation field is primarily determined by the beam limiter and the projection distance. The beam limiter is an optical device that controls the irradiation field of the X-rays emitted by the tube by focusing. While meeting the requirements of X-ray imaging, it reduces the X-ray projection range and avoids unnecessary radiation dose. Generally speaking, while covering the imaging area, the irradiation field should be minimized to improve DR image quality and reduce the radiation dose of the target object. For example, the irradiation field parameter characteristics can be obtained based on the irradiation field setting value of the DR imaging device's console. Alternatively, since the irradiation field depends on the beam limiter and the imaging distance, the irradiation field parameter characteristics can also be obtained based on the imaging distance parameter characteristics and the opening size reported by the DR imaging device's beam limiter, combined with pre-set inference rules.

[0084] A grid can eliminate scattered rays and improve the clarity of DR images. Therefore, in some embodiments, the device parameter characteristics may also include grid parameter characteristics derived from grid parameters. Specifically, the grid parameter characteristics may be derived based on the grid settings or grid parameter feedback values ​​on the DR imaging device's console. Grid parameters include grid thickness, bar width, gap width, grid ratio, and the like.

[0085] In addition to device parameter characteristics, parameter characteristics can also include object parameter characteristics. Because human tissues vary in attenuation coefficient and thickness, X-rays attenuate differently when passing through different tissues, resulting in different images after imaging processing. Therefore, corresponding object parameter characteristics can be derived based on the target object's parameters related to tissue attenuation coefficient or thickness.

[0086] Exemplarily, the object parameter characteristics include height parameter characteristics and / or weight parameter characteristics. When the DR imaging device has a camera device, the height parameter characteristics and / or weight parameter characteristics can be obtained based on the visual image obtained by the camera device of the DR imaging device. Specifically, image recognition can be performed on the visual image to obtain the height parameter and / or weight parameter, and then the height parameter characteristics and / or weight parameter characteristics can be obtained. Alternatively, the height parameter characteristics and / or weight parameter characteristics can also be obtained based on the input body position and / or body shape setting value. In addition, the height parameter characteristics and / or weight parameter characteristics can also be obtained based on the height parameter and / or weight parameter measured by the second measuring device of the DR imaging device. Specifically, the second measuring device includes a height measuring device for measuring the height parameter to obtain the height parameter characteristics; or a weight measuring device for measuring the weight parameter to obtain the weight parameter characteristics.

[0087] Exemplarily, the object parameter features may further include age parameter features and / or gender parameter features. The age parameter features are determined based on age parameters, and the gender parameter features are determined based on gender parameters. The age parameter features and / or gender parameter features may be acquired based on a visual image acquired by a camera device of a DR imaging device. Specifically, image recognition may be performed on the visual image to acquire age parameters and / or gender parameters, thereby acquiring the age parameter features and / or gender parameter features. Alternatively, the age and / or gender parameter features may be acquired based on input body position and / or body shape setting values. Alternatively, the age parameter features and / or gender parameter features may also be acquired based on age information and / or gender information input through an input device of the DR imaging device.

[0088] Reference Figure 2 , the step of extracting image features and the step of obtaining parameter features can influence or optimize each other based on pre-set rules. That is to say, the extraction of image features can be adjusted according to the device parameter features and / or object parameter features, and the acquisition of device parameter features and / or object parameter features can also be adjusted according to the image features. For example, the statistical scale or direction of the texture features in the image features can be adjusted to different degrees according to the shooting posture and / or body shape parameter features in the device parameter features. Specifically, for those with larger postures / body shapes, it is considered that the texture is rich, and the statistical scale or direction of the texture features can be increased. For the mutual adjustment between other parameters, they can also be adjusted according to the correlation between the device parameters / object parameters and the image features, referring to the above method, which will not be described in detail here. Through the mutual influence and optimization of image features and parameter features, the image features and parameter features can better characterize the image information and parameter information, and thus the DR feature index can better reflect the feature information content of the DR image.

[0089] In step S140, a DR feature index reflecting the feature information content of the DR image is determined based on the image features, and the device parameter features and / or the object parameter features.

[0090] In one embodiment, referring to Figure 2 , image features and device parameter features and / or object parameter features can be input into the target model, and the DR feature index output by the target model can be obtained. The target model can be a trained model or an untrained model. The target model can be trained offline, and its training method mainly includes:

[0091] First, a DR image set is obtained, where the DR image set includes multiple sample DR images, i.e., DR images serving as training samples. An image feature set can be obtained based on the DR image set, where the image feature set includes sample image features extracted from the multiple sample DR images. Exemplarily, for each sample DR image in the DR image set, at least one sample image feature among grayscale entropy features, texture features, noise features, gradient features, and divergence features is extracted respectively. The multiple sample image features extracted from the multiple sample DR images in the DR image set together constitute an image feature set. Each sample DR image in the DR image set corresponds to at least one sample image feature in the image feature set. The method for extracting sample image features from sample DR images can refer to the method for extracting image features from DR images in step S120.

[0092] According to the corresponding device parameters and / or object parameters of each sample DR image, a parameter feature set can also be obtained, and the parameter feature set includes device parameter features and / or object parameter features. Exemplarily, for each sample DR image in the DR image set, device parameter features and / or object parameter features are respectively obtained, and multiple sample parameter features obtained from multiple sample DR images in the DR image set together constitute the parameter feature set. Each sample DR image in the DR image set corresponds to at least one device parameter feature and / or object parameter feature in the parameter feature set. The method for obtaining device parameter features and / or object parameter features from sample DR images can refer to the method for obtaining device parameter features and / or object parameter features from DR images in step S130.

[0093] A scoring set can also be obtained based on the DR image set. The scoring set includes scores obtained by evaluating multiple sample DR images. For example, if the sample DR images in the DR image set are original DR images, the multiple sample DR images in the DR image set can be individually processed to obtain multiple DR diagnostic images, i.e., visual images used by doctors for diagnosis. The multiple DR diagnostic images can then form a DR diagnostic image set. The aforementioned scoring set is then obtained based on the DR diagnostic image set. The scoring set includes scores obtained by evaluating the multiple DR diagnostic images. In some embodiments, clinical experts can evaluate the DR diagnostic images in the DR diagnostic image set based on their quality and assign expert scores. A higher DR diagnostic image score indicates a greater amount of basic feature information contained in the corresponding original DR image; conversely, a lower DR diagnostic image score indicates a lower amount of basic feature information contained in the corresponding original DR image. The expert scores of the DR diagnostic images constitute the expert scoring set. Alternatively, if the DR images in the DR image set are visual images used by doctors for diagnosis, the scoring set can be directly derived from the DR image set.

[0094] Afterwards, the initial model is trained using the acquired image feature set, parameter feature set, and score set as a training sample set to obtain a target model. The target model may be a traditional machine learning model or a deep learning model, including but not limited to neural networks, support vector machines, linear discriminant analysis, etc.; the training methods include but are not limited to model training methods such as linear regression and gradient descent. For example, an optimal mapping function from image features to scores can be learned so that the error between the DR feature index obtained by the image feature mapping and the actual calibrated expert score is minimized. By executing this optimal mapping function on the image features acquired in step S120 and the parameter features acquired in step S130, a prediction result of the DR feature index closest to the expert score can be obtained.

[0095] In one embodiment, when performing classification and regression training using the image feature set, the parameter feature set, and the score set, the classification and regression training formula used is:

[0096]

[0097] Among them, x is the eigenvector, {x i} i＝1,...,m is the support vector, α i is the weighting coefficient, b is the bias, k is the kernel function, and each value of the eigenvector x needs to be obtained through the linear transfer function:

[0098]

[0099] in, is the input feature, w and s are scaling and translation parameter vectors respectively, and the kernel function k can be or

[0100] It should be noted that the embodiment of the present invention does not limit the linear transfer function, kernel function or parameters used in the regression training method.

[0101] After obtaining a trained target model during the model training phase, in actual application, at least one image feature from among the grayscale entropy, texture, noise, gradient, and divergence features extracted in step S120, as well as at least one parameter feature from among the device parameter and object parameter features obtained in step S130, is input into the trained network model. This yields a DR feature index representing the comprehensive relationship between the X-ray radiation dose received by the target object and the image quality of the DR image. This DR feature index can be displayed on a display device or output in other ways. It should be understood that the comprehensive level herein refers to the comprehensive level of the image quality of the DR image and the X-ray radiation dose received. If the user believes the image quality is good and meets their needs, they can try to lower the DR feature index to reduce the X-ray radiation dose received. Similarly, if the image quality is poor, they can increase the DR feature index to improve image quality. Factors influencing the DR feature index include the image features, device parameter features, and / or object parameter features described herein, and the DR feature index is output through the training model.

[0102] In some embodiments, after obtaining the DR characteristic index, prompt information for guiding adjustment of the X-ray emission dose may be output based on the DR characteristic index. Since the DR characteristic index represents the combined level between the X-ray radiation dose received by the target object and the image quality of the DR image, it is possible to determine whether the X-ray radiation dose received by the target object is appropriate based on the DR characteristic index. If it is determined that the X-ray radiation dose received by the target object is not appropriate, prompt information for guiding adjustment of the X-ray emission dose is output, directing the user to increase or decrease the X-ray emission dose.

[0103] In some embodiments, a prompt message indicating whether the DR characteristic index meets the requirements can be output based on the relationship between the value of the DR characteristic index and a preset threshold. Specifically, the value of the DR characteristic index can be compared with the preset threshold. When the value of the DR characteristic index does not fall within the range of the preset threshold, it is determined that the DR characteristic index does not meet the requirements, and a prompt message is output in the form of text, graphics, voice, etc., prompting the user to adjust the imaging parameters in a timely manner.

[0104] In some embodiments, after obtaining the DR feature index, the image quality of the DR image can be evaluated based on the DR feature index, and the evaluation results can be output. For example, to more intuitively reflect the image quality of the DR image, classification or grading can be performed based on the DR feature index to obtain a qualitative quality assessment result or a semi-quantitative quality grading result, indicating to the user the quality of the DR image.

[0105] Based on the above description, the analysis method of DR imaging according to the embodiment of the present application obtains a DR feature index that can objectively reflect the comprehensive level between the radiation dose of X-rays received by the target object and the image quality of the DR image based on the image characteristics of the DR image and the equipment parameter characteristics and / or object parameter characteristics, thereby providing the operator with an objective judgment basis.

[0106] Another embodiment of the present invention provides a DR imaging device, see Figure 3 The DR imaging device 300 includes: an X-ray generator 310, a detector 320, a processor 330 and a display 340, wherein the processor 330 is communicatively connected to the X-ray generator 310, the detector 320 and the display 340; the X-ray generator 310 is used to generate X-rays, emit X-rays to a target tissue part of a target object, and control the X-rays to pass through the target tissue part; the detector 320 is used to receive the X-rays after passing through the target tissue part, the processor 330 is used to process the X-rays after passing through the target tissue part to obtain a digital X-ray photography DR image, and the display 340 is used to display the DR image.

[0107] Exemplarily, the X-ray generator 310 includes a tube for emitting X-rays; the detector 320 includes a flat-panel detector, specifically comprising an X-ray conversion medium located on an upper layer and a detection unit array located on a lower layer. The X-ray conversion medium converts X-rays into electronic signals, generating positive and negative charges. These charges, under the action of a bias voltage, migrate along an electric field in the form of currents, are collected by the detection unit array, and converted into electrical signals. The detector 320 includes, but is not limited to, amorphous silicon, amorphous selenium, and a CCD. When using the DR imaging device 300 for image acquisition, the positions of the X-ray generator 310 and the detector 320 must be fixed in a specified manner so that the X-rays emitted by the X-ray generator 310, after passing through the target tissue, can strike the detector 320. This allows the detector 320 to capture the X-rays after they have passed through the target tissue to produce a DR image.

[0108] The processor 330 is used to process the X-rays after passing through the target tissue site to obtain a digital X-ray photography DR image. The processor 330 is also used to execute the above-mentioned DR imaging analysis method 100 to obtain a DR characteristic index that reflects the comprehensive level between the radiation dose of the X-rays received by the target object and the image quality of the DR image; the display 340 is used to display the DR characteristic index. The specific details of the DR imaging analysis method 100 can be found above. According to the embodiment of the present application, the DR imaging device 300 obtains a DR characteristic index that can objectively reflect the comprehensive level between the radiation dose of the X-rays received by the target object and the image quality of the DR image based on the image characteristics of the DR image and the device parameter characteristics and / or the object parameter characteristics, thereby providing an objective judgment basis for the operator.

[0109] Reference Figure 4 An embodiment of the present invention further provides an electronic device 400, which can be used to implement the DR imaging analysis method 100 described above, wherein the electronic device 400 can be a DR imaging device, such as a flat-panel detector or an automatic exposure controller or a mobile DR device or a fixed DR device, etc., or it can be other computers or terminal devices, such as mobile phones, computers, PDAs, etc., which are not specifically limited here.

[0110] The electronic device 400 includes a memory 410 and a processor 420. The memory 410 stores a computer program run by the processor 420. When the computer program is run by the processor, it executes the steps of the DR imaging analysis method 100. The specific details can be found above and will not be repeated here.

[0111] The processor 420 may be implemented using software, hardware, firmware, or any combination thereof. It may utilize circuits, single or multiple application-specific integrated circuits, single or multiple general-purpose integrated circuits, single or multiple microprocessors, single or multiple programmable logic devices, or any combination of the aforementioned circuits and / or devices, or other suitable circuits or devices. The processor 420 may control other components in the electronic device 400 to perform desired functions. The memory 410 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory and / or cache memory. Non-volatile memory may include, for example, read-only memory, a hard disk, or flash memory. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 420 may execute the program instructions to implement the DR image analysis method of the present invention and / or various other desired functions. The computer-readable storage medium may also store various applications and data, such as data used and / or generated by the applications.

[0112] In addition, according to an embodiment of the present invention, a computer storage medium is also provided, on which program instructions are stored. When the program instructions are executed by a computer or processor, the computer storage medium is used to execute the corresponding steps of the DR imaging analysis method of any embodiment of the present invention. In some embodiments, the computer storage medium is a non-volatile computer-readable storage medium, which may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created during the execution of the program instructions, etc. In addition, the non-volatile computer-readable storage medium may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the non-volatile computer-readable storage medium may optionally include a memory remotely located relative to the processor.

[0113] For example, the computer storage medium may include a hard disk of a personal computer, a storage component of a tablet computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, a memory card of a smart phone, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0114] In addition, according to an embodiment of the present invention, a computer program is also provided, which can be stored in a cloud or local storage medium. When the computer program is executed by a computer or processor, it is used to perform the corresponding steps of the DR imaging analysis method of the embodiment of the present invention.

[0115] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0116] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0118] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0119] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this approach of the present application should not be interpreted as reflecting the intention that the application claimed for protection requires more features than those explicitly recited in each claim. More precisely, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with fewer features than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present application.

[0120] It will be understood by those skilled in the art that, except where mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus disclosed herein may be combined in any combination. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature providing the same, equivalent, or similar purpose.

[0121] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0122] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules in the article analysis device according to the embodiment of the present application. The application can also be implemented as a device program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0123] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0124] The above description is merely a specific embodiment or illustration of a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A DR imaging analysis method, characterized in that: The method comprises: Acquire a digital X-ray DR image of the target object by a DR imaging device, wherein the DR image includes at least one of an original DR image and an image obtained by processing the original DR image; Extracting image features from the acquired DR image, wherein the image features include at least one of grayscale entropy features, texture features, noise features, gradient features, and divergence features; Acquiring device parameter characteristics according to device parameters of the DR imaging device, and / or acquiring object parameter characteristics according to object parameters of the target object, wherein the device parameters include at least one of the following: tube voltage parameter characteristics, tube current parameter characteristics, shooting position and / or body shape parameter characteristics, exposure time parameter characteristics, shooting distance parameter characteristics, irradiation field parameter characteristics, and grid parameter characteristics; A DR feature index is determined based on the image features, the device parameter features and / or the object parameter features. The DR feature index is used to characterize the comprehensive level between the X-ray radiation dose received by the target object and the image quality of the DR image.

2. The method according to claim 1, wherein Acquiring the device parameter characteristics includes acquiring the tube voltage parameter characteristics, and acquiring the tube voltage parameter characteristics includes: Acquiring the tube voltage parameter characteristic according to a tube voltage setting value of a console of the DR imaging device, or acquiring the tube voltage parameter characteristic according to a tube voltage received value of a high voltage generator of the DR imaging device; and / or, Acquiring the device parameter characteristics includes acquiring the tube current parameter characteristics, and acquiring the tube current parameter characteristics includes: Acquiring the tube current parameter characteristics according to a tube current setting value of a console of the DR imaging device, or acquiring the tube current parameter characteristics according to a tube current received value of a high-voltage generator of the DR imaging device; and / or, Acquiring device parameter characteristics includes acquiring the shooting position and / or body shape parameter characteristics, and acquiring the shooting position and / or body shape parameter characteristics includes: Acquiring the shooting body position and / or body shape parameter characteristics according to the body position and / or body shape setting values ​​of the console of the DR imaging device, or acquiring the shooting body position and / or body shape parameter characteristics according to the visual image obtained by the camera device of the DR imaging device; and / or, Acquiring the device parameter characteristics includes acquiring the exposure time parameter characteristics, and acquiring the exposure time parameter characteristics includes: The exposure time parameter characteristic is obtained according to an exposure time setting value of a console of the DR imaging device, or according to an exposure time received value of a high-voltage generator of the DR imaging device, or according to a feedback value of an automatic exposure control device of the DR imaging device; and / or, Acquiring the device parameter characteristics includes acquiring the photographic distance parameter characteristics, and acquiring the photographic distance parameter characteristics includes: Acquiring the photographic distance parameter characteristic according to a photographic distance setting value of a console of the DR imaging device, or acquiring the photographic distance parameter characteristic according to a photographic distance measured by a first measuring device of the DR imaging device; and / or, Acquiring the device parameter characteristics includes acquiring the irradiation field parameter characteristics, and acquiring the irradiation field parameter characteristics includes: Acquiring the irradiation field parameter characteristics according to the irradiation field setting value of the console of the DR imaging device, or acquiring the irradiation field parameter characteristics according to the photographic distance parameter characteristics and the opening size fed back by the beam limiter of the DR imaging device; and / or, Acquiring the device parameter characteristics includes acquiring the grid parameter characteristics, and acquiring the grid parameter characteristics includes: The grid parameter characteristics are obtained according to the grid setting value of the control console of the DR imaging device, or the grid parameter characteristics are obtained according to the grid parameter feedback value.

3. The method according to claim 1, wherein The object parameter characteristics include at least one of the following: height parameter characteristics, weight parameter characteristics, age parameter characteristics and gender parameter characteristics.

4. The method according to claim 3, wherein Acquiring the height parameter characteristic and / or the weight parameter characteristic includes: Acquiring the height parameter characteristic and / or the weight parameter characteristic based on a visual image obtained by a camera device of the DR imaging device, or acquiring the height parameter characteristic and / or the weight parameter characteristic based on input body position and / or body shape setting values, or acquiring the height parameter characteristic and / or the weight parameter characteristic based on the height parameter and / or weight parameter measured by a second measuring device of the DR imaging device; and / or, Acquiring the age parameter feature and / or the gender parameter feature includes: The age parameter characteristics and / or the gender parameter characteristics are obtained based on the visual image obtained by the camera device of the DR imaging device, or the age parameter characteristics and / or the gender parameter characteristics are obtained based on the input body position and / or body shape setting values, or the age parameter characteristics and / or the gender parameter characteristics are obtained based on the age information and / or gender information input through the input device of the DR imaging device.

5. The method according to claim 1, wherein Extracting the image feature includes extracting the grayscale entropy feature, and extracting the grayscale entropy feature includes: Obtaining a first probability statistical distribution of each grayscale signal source in the DR image; determining a first probability statistical distribution of a non-redundant grayscale information source from the first probability statistical distribution of the grayscale information source according to the size of the first probability statistical distribution; Obtaining a second probability statistical distribution of each non-redundant grayscale signal source according to a ratio of the first probability statistical distribution of each non-redundant grayscale signal source to the sum of the first probability statistical distributions of all non-redundant grayscale signal sources; The entropy of the second probability statistical distribution of the non-redundant grayscale information source is calculated to obtain the grayscale entropy feature.

6. The method according to claim 1, wherein Extracting the image features includes extracting the texture features, and extracting the texture features includes: Obtaining a texture feature description matrix according to the grayscale value of each pixel in the DR image, wherein the texture feature description matrix describes the distance between different pixels and the change of grayscale values ​​in the direction between different pixels in the DR image; At least one two-dimensional component of the texture feature description matrix is ​​extracted as the texture feature.

7. The method according to claim 1, wherein Extracting the image feature includes extracting the noise feature, and extracting the noise feature includes: performing filtering processing on the DR image to obtain a high-frequency image; Calculating the root mean square of a neighborhood of each pixel in the high-frequency image to obtain a noise distribution image; The noise value distribution in the noise distribution image is statistically analyzed to obtain the noise feature.

8. The method according to claim 1, characterized in that Extracting the image feature includes extracting the gradient feature, and extracting the gradient feature includes: determining the regional distribution of different tissues in the DR image; The clarity of the boundaries between different tissue regions is obtained as the gradient feature.

9. The method according to claim 1, characterized in that Extracting the image feature includes extracting the divergence feature, and extracting the divergence feature includes: determining the regional distribution of different tissues in the DR image; The transition strength and / or trend consistency of the boundaries of the distribution of different tissue regions are obtained as the divergence feature.

10. The method according to claim 1, wherein The method further includes: adjusting the extraction of the image features according to the device parameter features and / or the object parameter features, and / or adjusting the acquisition of the device parameter features and / or the object parameter features according to the image features.

11. The method according to claim 1, wherein Determining the DR feature index according to the image feature, the device parameter feature, and / or the object parameter feature includes: The image features and the device parameter features and / or the object parameter features are input into a target model, and the DR feature index output by the target model is obtained.

12. The method according to claim 11, wherein The training process of the target model includes: Acquire a DR image set, where the DR image set includes a plurality of sample DR images; Obtaining an image feature set according to the DR image set, wherein the image feature set includes a plurality of sample image features extracted from the plurality of sample DR images; Obtaining a parameter feature set, the parameter feature set comprising device parameter features obtained according to device parameters of a DR imaging device that generates the sample DR image, and / or object parameter features obtained according to object parameters of a target object in the sample DR image; obtaining a score set according to the DR image set, the score set including scores obtained by evaluating the plurality of sample DR images; The initial model is trained using the image feature set, the parameter feature set, and the score set as a training sample set to obtain the target model.

13. The method according to claim 1, wherein The method of obtaining a digital X-ray DR image of the target object by using a DR imaging device includes: controlling the DR imaging device to emit X-rays toward a target tissue portion of the target object, and controlling the X-rays to pass through the target tissue portion; receiving X-rays after passing through the target tissue site; The X-rays passing through the target tissue site are processed to obtain the digital X-ray photography DR image.

14. The method according to claim 13, wherein The method further comprises: Prompt information for guiding adjustment of the X-ray emission dose is output according to the DR characteristic index.

15. The method according to claim 1, wherein The method further comprises: The DR characteristic index is displayed, and prompt information indicating whether the DR characteristic index meets the requirements is output based on the relationship between the value of the DR characteristic index and a preset threshold.

16. The method according to claim 1, wherein The method further comprises: The image quality of the DR image is evaluated according to the DR feature index, and an evaluation result is output.

17. A DR imaging device, characterized in that: The DR imaging device includes: an X-ray generator, a detector, a processor and a display; The X-ray generator is used to generate X-rays, emit X-rays toward a target tissue site of a target object, and control the X-rays to pass through the target tissue site; The detector is used to receive X-rays after passing through the target tissue site; The processor is configured to process the X-rays after passing through the target tissue site to obtain a digital X-ray photography (DR) image, wherein the DR image includes at least one of an original DR image and an image obtained by processing the original DR image; The processor is further configured to obtain a DR characteristic index according to the DR imaging analysis method according to any one of claims 1 to 16, wherein the DR characteristic index is used to characterize a comprehensive level between the X-ray radiation dose received by the target object and the image quality of the DR image; The display is used to display the DR characteristic index.

Images