Prediction method before exposure of DR imaging system and DR imaging system

Abstract

The invention discloses a pre-exposure prediction method of a DR imaging system and the DR imaging system. The method comprises the following steps: acquiring a current set exposure parameter of the DR imaging system; a pre-established corresponding relation between exposure parameters and a DR feature index is obtained, and the DR feature index is determined according to at least one image feature of a gray scale entropy feature, a texture feature, a noise feature, a gradient feature and a divergence feature extracted from the DR image; determining a DR characteristic index prediction value corresponding to the set exposure parameter according to the set exposure parameter and the corresponding relation; and outputting the DR characteristic index prediction value. Before exposure, the DR characteristic index prediction value is obtained according to the corresponding relation between the exposure parameter and the DR characteristic index, so that a user can be helped to find the possible abnormity of the current set exposure parameter before exposure, and the user is guided to adjust the exposure parameter in time before exposure.

Description

Technical Field

[0001] The present invention relates to the field of medical image technology, and more specifically to a pre-exposure prediction method of a DR imaging system and a DR imaging system. Background Art

[0002] Digital Radiography (DR) images are a common type of medical digital images and are widely used in physical examinations and routine medical imaging diagnosis. With the development of digital X-ray detectors and digital image processing systems, DR images can often present diagnostic image effects within a wide range of exposure doses. However, this process often relies on the experience of the operator to grasp the final image effect, and the accuracy of the judgment is easily affected by various factors such as experience differences, subjective differences, and post-processing.

[0003] At present, in the process of DR imaging, doctors often use ionization chamber automatic exposure control technology to control the exposure cutoff time to achieve the purpose of automatic exposure, and obtain DR images that meet the requirements while reducing the difference in subjective experience. Automatic exposure technology converts X-rays passing through the human body into voltage signals through an ionization chamber, and automatically stops exposure when the voltage signal reaches a preset value. The preset value is set based on the grayscale value of the DR image after exposure, which makes it difficult to accurately and real-time reflect the exposure dose of the current DR image, and it is even more impossible to detect abnormalities before exposure. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] An embodiment of the present invention provides a pre-exposure prediction method for a DR imaging system, the method comprising:

[0006] Acquire the current set exposure parameters of the DR imaging system;

[0007] Acquiring a correspondence between a pre-established exposure parameter and a DR feature index, wherein the DR feature index is determined based on at least one image feature of a grayscale entropy feature, a texture feature, a noise feature, a gradient feature, and a divergence feature extracted from the DR image;

[0008] Determining a DR characteristic index prediction value corresponding to the set exposure parameter according to the set exposure parameter and the corresponding relationship;

[0009] Output the DR characteristic index prediction value.

[0010] In one embodiment, the method further includes: obtaining a standard value of a DR characteristic index; comparing the predicted value of the DR characteristic index with the standard value of the DR characteristic index, and outputting prompt information according to the comparison result.

[0011] In one embodiment, the DR characteristic index standard value includes a DR characteristic index upper limit value and / or a DR characteristic index lower limit value; the outputting prompt information according to the comparison result includes: when the DR characteristic index prediction value is higher than the DR characteristic index upper limit value, outputting a first prompt information, the first prompt information including prompt information for lowering the set exposure parameters; when the DR characteristic index prediction value is lower than the DR characteristic index lower limit value, outputting a second prompt information, the second prompt information including prompt information for increasing the set exposure parameters.

[0012] In one embodiment, the set exposure parameters include at least one of tube current, tube voltage and exposure time; the first prompt information includes prompt information for lowering the tube current, the tube voltage and / or the exposure time; the second prompt information includes prompt information for increasing the tube current, the tube voltage and / or the exposure time.

[0013] In one embodiment, the method further includes: acquiring imaging conditions related to the target object to be imaged; and acquiring the DR feature index standard value includes: acquiring the DR feature index standard value corresponding to the imaging conditions related to the target object.

[0014] In one embodiment, the method also includes: performing a calibration operation on the DR feature index standard value; based on the calibration operation, obtaining multiple frames of reference DR images, and obtaining the DR feature index of each frame of the reference DR image; performing statistics on the DR feature index of the multiple frames of reference DR images to obtain statistical results, and updating the DR feature index standard value according to the statistical results.

[0015] In one embodiment, the DR characteristic index standard value includes a DR characteristic index upper limit value and / or a DR characteristic index lower limit value, and updating the DR characteristic index standard value according to the statistical result includes: increasing a preset threshold value or increasing a preset percentage on the basis of the statistical result to obtain the DR characteristic index upper limit value, and / or, reducing a preset threshold value or reducing a preset percentage on the basis of the statistical result to obtain the DR characteristic index lower limit value.

[0016] In one embodiment, the acquiring of multiple frames of reference DR images includes: acquiring historical DR images collected within a preset time range as the reference DR images.

[0017] In one embodiment, the acquiring of multiple frames of reference DR images includes: displaying historical DR images acquired within a preset time range; and selecting the historical DR images to determine the selected historical DR images as the reference DR images.

[0018] In one embodiment, the method further includes: acquiring imaging conditions related to the target object to be imaged; and acquiring the correspondence between the pre-established exposure parameters and the DR characteristic index includes: acquiring a correspondence that matches the imaging conditions related to the target object.

[0019] In one embodiment, the correspondence between the exposure parameters and the DR characteristic index can be pre-established in the following manner: controlling the DR imaging system to perform exposure under different exposure parameters to obtain DR images corresponding to different exposure parameters; extracting the DR characteristic index of the DR images corresponding to the different exposure parameters; associating the exposure parameters and DR characteristic index corresponding to each DR image to obtain the correspondence between the exposure parameters and the DR characteristic index.

[0020] In one embodiment, determining the DR characteristic index prediction value corresponding to the set exposure parameter based on the set exposure parameter and the corresponding relationship includes: searching for the DR characteristic index corresponding to the set exposure parameter from the pre-stored corresponding relationship to obtain the DR characteristic index prediction value.

[0021] In one embodiment, the DR imaging system performs exposure under different imaging conditions, and the corresponding relationship includes a plurality of corresponding relationships respectively matching the different imaging conditions.

[0022] In one embodiment, the correspondence between the exposure parameters and the DR characteristic index can be pre-established in the following manner: obtaining the exposure parameters corresponding to multiple frames of DR images, and extracting the DR characteristic index of each frame of DR image; performing model training based on the exposure parameters and the DR characteristic index to obtain a model that characterizes the correspondence between the exposure parameters and the DR characteristic index.

[0023] In one embodiment, determining the DR characteristic index prediction value corresponding to the set exposure parameter based on the set exposure parameter and the corresponding relationship includes: inputting the set exposure parameter into the model to obtain the DR characteristic index prediction value output by the model.

[0024] In one embodiment, the multiple frames of DR images include DR images obtained under different imaging conditions, and the model includes multiple models corresponding to the different imaging conditions.

[0025] In one embodiment, the imaging condition includes at least one of the following: imaging site, imaging position, body shape of the target object, age of the target object, and gender of the target object.

[0026] In one embodiment, extracting the grayscale entropy feature from the DR image includes: obtaining a first probability statistical distribution of each grayscale source in the DR image; determining a first probability statistical distribution of non-redundant grayscale sources from the first probability statistical distribution of the grayscale sources according to the size of the first probability statistical distribution; obtaining a second probability statistical distribution of each non-redundant grayscale source according to the ratio of the first probability statistical distribution of each non-redundant grayscale source to the sum of the first probability statistical distributions of all non-redundant grayscale sources; and obtaining the grayscale entropy feature by calculating the entropy of the second probability statistical distribution of the non-redundant grayscale sources.

[0027] In one embodiment, extracting the texture feature from the DR image includes: obtaining a texture feature description matrix based on the grayscale value of each pixel in the DR image, the texture feature description matrix describing the distance between different pixel points in the DR image and the change in grayscale value in the direction between different pixel points; extracting at least one two-dimensional component of the texture feature description matrix as the texture feature.

[0028] In one embodiment, extracting the noise feature from the DR image includes: filtering the DR image to obtain a high-frequency image; 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 feature.

[0029] In one embodiment, extracting the gradient feature from the DR image includes: determining the regional distribution of different tissues in the DR image; and obtaining the clarity of the boundary of the regional distribution of different tissues as the gradient feature.

[0030] 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 transition strength and / or trend consistency of the boundaries of the regional distribution of different tissues as the divergence feature.

[0031] On the other hand, an embodiment of the present invention provides a pre-exposure prediction method for a DR imaging system, the method comprising: displaying multiple alternative body positions on a display interface; determining a body position to be imaged from the multiple alternative body positions based on a user's selection operation; determining exposure parameters corresponding to the body position to be imaged; determining a DR characteristic index prediction value based on the exposure parameters and a pre-established correspondence between the exposure parameters and the DR characteristic index; and displaying the DR characteristic index prediction value on the display interface.

[0032] In one embodiment, determining the exposure parameter corresponding to the body position to be imaged includes: determining a preset exposure parameter corresponding to the body position to be imaged; or determining the exposure parameter based on an adjustment operation of a user.

[0033] In one embodiment, the method further includes: obtaining a standard value of a DR characteristic index; comparing the predicted value of the DR characteristic index with the standard value of the DR characteristic index, and outputting prompt information according to the comparison result.

[0034] In one embodiment, the method further includes: determining imaging conditions related to the target object to be imaged based on a user's selection operation; and obtaining the DR feature index standard value includes: obtaining the DR feature index standard value corresponding to the imaging conditions related to the target object.

[0035] In one embodiment, the imaging condition includes at least one of the following: the body shape of the target object, the age of the target object, and the gender of the target object.

[0036] On the other hand, an embodiment of the present invention provides a DR imaging system, which includes: an X-ray source, a flat-panel detector, a processor and an output device; the X-ray source is used to emit X-rays to a target part of a target object, so that the X-rays pass through the target part; the flat-panel detector is used to receive the X-rays after passing through the target part; the processor is used to process the X-rays after passing through the target part to obtain a digital X-ray photography DR image, and the processor is also used to obtain a DR characteristic index prediction value according to the pre-exposure prediction method of the DR imaging system as described above; the output device is used to output the DR characteristic index prediction value.

[0037] The pre-exposure prediction method of the DR imaging system and the DR imaging system of the embodiment of the present invention obtain the DR characteristic index prediction value according to the correspondence between the exposure parameters and the DR characteristic index before exposure, which can help the user discover possible abnormalities in the current set exposure parameters before exposure and guide the user to adjust the exposure parameters in time before exposure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other purposes, features and advantages of the present invention will become more apparent by describing the embodiments of the present invention in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0039] Figure 1A schematic flow chart showing a pre-exposure prediction method of a DR imaging system according to an embodiment of the present invention;

[0040] Figure 2 A schematic flow chart showing a pre-exposure prediction method of a DR imaging system according to another embodiment of the present invention;

[0041] Figure 3 A schematic block diagram of a DR imaging system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

[0043] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0044] It should be understood that the present invention can be implemented in different forms and should not be interpreted as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the disclosure thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0045] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present invention. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "consisting of" and / or "comprising", 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.

[0046] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0047] Next, first refer to Figure 1 The following describes a pre-exposure prediction method 100 for a DR imaging system according to an embodiment of the present invention. Figure 1 As shown, the pre-exposure prediction method 100 of the DR imaging system may include the following steps:

[0048] In step S110, the current exposure parameters of the DR imaging system are obtained;

[0049] In step S120, a correspondence between a pre-established exposure parameter and a DR feature index is obtained, wherein the DR feature index is determined based on at least one image feature of a grayscale entropy feature, a texture feature, a noise feature, a gradient feature, and a divergence feature extracted from the DR image;

[0050] In step S130, according to the set exposure parameter and the corresponding relationship, a DR characteristic index prediction value corresponding to the set exposure parameter is determined;

[0051] In step S140, the DR feature index prediction value is output.

[0052] The pre-exposure prediction method 100 of the DR imaging system of an embodiment of the present invention obtains a DR characteristic index prediction value based on the correspondence between the exposure parameters and the DR characteristic index before exposure, which can help the user discover possible abnormalities in the current set exposure parameters before exposure and guide the user to adjust the exposure parameters in time before exposure.

[0053] Specifically, first, the current set exposure parameters of the DR imaging system are obtained. The current set exposure parameters are the exposure parameters currently set for DR imaging. If exposure is performed with the set exposure parameters, a DR image corresponding to the set exposure parameters can be obtained. In the past, DR imaging systems usually evaluated the obtained DR images after DR imaging, and it was impossible to predict the imaging effect that can be obtained by exposure with the current set exposure parameters before imaging. However, the embodiment of the present invention predicts the imaging effect corresponding to the set exposure parameters before imaging, so that the user can adjust the set exposure parameters in time, reduce the X-ray dose, and reduce the number of reshoots.

[0054] Exemplarily, setting the exposure parameters includes at least one of tube current, tube voltage and exposure time. Tube voltage and tube current are the main factors affecting the exposure dose. Specifically, the X-ray source of the DR imaging system mainly includes a high-voltage generator and a tube. The high-voltage generator provides a tube voltage to the tube, so that the electrons generated by the filament in the tube are accelerated under the action of the tube voltage, bombarding the target material to generate X-rays, and the electrons received by the target material form a tube current. Generally speaking, the tube voltage controls the energy and quality of the X-ray beam generated by the tube, which in turn determines the penetration ability of the X-rays. The strength of the penetration ability determines the amount of radiation irradiated on the flat-panel detector, thereby determining the contrast of the DR image. The tube current controls the number of X-ray beams, and the number of X-rays determines the upper limit of the amount of X-rays that may be irradiated on the detector during DR imaging, thereby determining the density of the DR image. Too high tube voltage and tube current will result in too many X-rays penetrating the human body, increasing image noise; too low tube voltage and tube current will result in too few X-rays penetrating the human body, and the image details cannot be clearly displayed.

[0055] The exposure time is the duration of the exposure process. The exposure process begins when the high-voltage generator generates high voltage to drive the tube to produce X-rays. During this process, the flat-panel detector receives the X-rays and converts them into electrical signals. The exposure time can determine the dose of X-rays.

[0056] In some embodiments, the exposure parameters may also include other exposure-related parameters such as the photographic distance and the irradiation field. The photographic distance is the distance between the X-ray source and the detector, which can affect the size of the irradiation field. The irradiation field is the exposure area of ​​the body surface irradiated by the X-rays, and the size of the irradiation field is closely related to the radiation dose of the X-rays and the quality of the DR image.

[0057] In addition to setting the exposure parameters, it is also necessary to obtain the pre-established correspondence between the exposure parameters and the DR characteristic index. The correspondence is used to reflect the DR characteristic index of the DR image obtained under different exposure parameters. Based on the correspondence, the DR characteristic index of the DR image obtained by the current set exposure parameters of the DR imaging system can be predicted.

[0058] The DR feature index of the embodiment of the present invention is determined based on at least one image feature of the grayscale entropy feature, texture feature, noise feature, gradient feature and divergence feature extracted from the DR image, which can reflect the basic feature information content of the DR image and can reflect the comprehensive level between the radiation dose of the X-ray and the image quality of the DR image. The DR feature index can also be called a DR feature index, a DR objectification index, or any other suitable name. Among them, the grayscale entropy feature, texture feature, noise feature, gradient feature and divergence feature respectively reflect the grayscale information, texture information, noise information, gradient information and divergence information of the DR image. The multi-dimensional image features are conducive to obtaining a more accurate DR feature index, which can more comprehensively and accurately reflect the imaging effect of the DR image compared with conventional evaluation indicators such as grayscale indicators.

[0059] In one embodiment, the grayscale entropy feature is the information content obtained by statistically screening out redundant grayscale information sources in the DR image. According to the transmission characteristics of X-ray quanta converted into digital signals by the detector of the DR system, each independent grayscale in the DR image can be regarded as a source, and the DR imaging process can be regarded as a process of transmitting information through grayscale information sources. Among all the grayscales, there are some grayscales that do not have grayscale values, so they are regarded as redundant grayscale information sources. Since the extraction process of the grayscale entropy feature of the embodiment of the present invention screens out redundant grayscale information sources, more effective image information can be extracted.

[0060] For example, the grayscale entropy feature can be extracted from the DR image in the following manner:

[0061] First, the first probability statistical distribution pH(i) of each grayscale 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 source is determined from the first probability statistical distribution pH(i) of the grayscale 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 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 source, that is:

[0062]

[0063] After obtaining the first probability statistical distribution pNH(i) of the non-redundant grayscale information source, the second probability statistical distribution pH'(i) of each non-redundant grayscale information source is obtained according to the ratio of the first probability statistical distribution pNH(i) of each non-redundant grayscale information source to the sum of the first probability statistical distributions ∑pNH(i) of all non-redundant grayscale information sources, that is:

[0064] pH'(i)=pNH(i) / ∑pNH(i) Formula (2)

[0065] Finally, entropy is calculated based on the second probability statistical distribution pH'(i) of the non-redundant grayscale information source to obtain the grayscale entropy feature H(Image). The method of calculating entropy 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 is expressed as follows:

[0066]

[0067] Thus, the grayscale entropy feature can be obtained after filtering out the redundant grayscale signal sources.

[0068] Texture features represent the spatial variation relationship of different grayscale values ​​on the DR image, which can reflect the abstract features of the law of changes in human tissue. For example, a statistical method can be used to extract the texture features of the DR image, which obtains the statistical characteristics of the texture area based on the grayscale attributes of the pixel and its neighborhood. The following mainly describes the specific details of the statistical method for extracting texture features, but the method for extracting texture features can also include geometric methods, model methods or other suitable texture feature extraction methods.

[0069] Exemplarily, when using statistical methods to extract texture features from a DR image, 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.

[0070] In one embodiment, 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. Assuming that the DR image Image = f(x, y), its texture feature description matrix P(i, j) is:

[0071] P(i,j)=#{(x1,y1),(x2,y2)∈M*N|f(x1,y1)=i,f(x2,y2)=j} Formula (4)

[0072] 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 according to the sub-pixel grayscale values.

[0073] 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, where the texture feature reflects the key characteristics of the texture feature description matrix. Exemplarily, the texture feature includes at least one of the following:

[0074] The first texture feature P1 is used to count 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;

[0075] 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 grayscale of the image in different directions.

[0076] The third texture feature P3 is used to count 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.

[0077] 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.

[0078] 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.

[0079] 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 the noise feature, that is, the noise feature reflects the fluctuation degree of X-ray quanta in the DR image.

[0080] In one embodiment, extracting noise features from a DR image includes the following steps: filtering the DR image to obtain a high-frequency image, wherein 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 statistically analyzing the distribution of noise values ​​in the noise distribution image to obtain the noise features.

[0081] As an implementation, filtering the DR image to obtain a high-frequency image includes: performing low-frequency filtering on the DR image I to filter out the low-frequency components therein to obtain a low-frequency image I1; and obtaining the high-frequency image I2 according to the difference between the low-frequency image and the DR image. In other implementations, the DR image I may also be directly subjected to high-frequency filtering to obtain the high-frequency image I2.

[0082] Exemplarily, 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:

[0083]

[0084] The high-frequency image I2 can be obtained by calculating the difference between the low-frequency image I1 and the image I, that is, I2=I1-I. The noise distribution image I3 can be obtained by extracting the effective information in 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 point in the high-frequency image. The method for calculating the root mean square includes using the L1 norm or other approximate or equivalent measures, for example:

[0085]

[0086] 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).

[0087] After acquiring 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.

[0088] Specifically, first, the pixel values ​​of the noise distribution image I3 are divided into M intervals, the pixel value interval of each interval is d, the initialization histogram vector is h, the length is M, and each component h(i) of the initialization 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))), after obtaining h, 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.

[0089] In some embodiments, the image features also include gradient features. Due to the different absorption coefficients of X-rays after penetrating different tissues of the human body, regional distributions of different tissues will be formed in the DR image, and there will be different degrees of boundaries between different tissue regions. The clarity of these boundaries can be quantified by extracting gradient features, that is, gradient features are used to characterize the clarity of boundaries between different tissues 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; obtaining the clarity of the boundaries of different tissue regional distributions as gradient features.

[0090] Exemplarily, the gradient feature can be calculated as follows:

[0091]

[0092] Among them, Grad(x,y) is used to represent the gradient size at the coordinates (x,y), and Image(x,y) is used to represent the pixel value at the coordinates (x,y).

[0093] In some embodiments, the image features also include divergence features. In DR images, the boundaries of different tissues are not strict boundaries, but there is a certain degree of transition. The strength and / or trend consistency of this transition can be quantified by extracting the divergence features, that is, the divergence features are used to characterize the strength and / or trend consistency of the transition of the boundaries of different tissues in the DR image. In one embodiment, extracting the divergence features from the DR image includes: determining the regional distribution of different tissues in the DR image; obtaining the strength and / or trend consistency of the transition of the boundaries of the regional distribution of different tissues as the divergence features.

[0094] For example, the divergence feature can be calculated as follows:

[0095]

[0096] Among them, Diver(x,y) is used to represent the divergence size at the coordinates (x,y), Image(x,y) is used to represent the pixel value at the coordinates (x,y), and actan represents the inverse tangent function.

[0097] In some embodiments, in addition to at least one of the above image features, other image features can also 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.

[0098] After extracting the above-mentioned image features from the DR image, the image features can be input into a pre-trained target model, and the DR feature index output by the target model can be obtained. In the model training stage, first, a DR image set is obtained, and the DR image set includes multiple sample DR images, that is, DR images used as training samples. An image feature set can be obtained based on the DR image set, and the image feature set includes sample image features extracted from multiple sample DR images. Exemplarily, for each sample DR image in the DR image set, at least one sample image feature of grayscale entropy features, texture features, noise features, gradient features, and divergence features is extracted respectively, and multiple sample image features extracted from multiple sample DR images in the DR image set together constitute an image feature set, and each sample DR image in the DR image set corresponds to at least one sample image feature in the image feature set.

[0099] A scoring set can also be obtained according to the DR image set, and the scoring set includes the scores obtained by evaluating multiple sample DR images. Exemplarily, if the sample DR images in the DR image set are DR original images, the multiple sample DR images in the DR image set can be image processed separately to obtain multiple DR diagnostic images, that is, visual images used by doctors for diagnosis, and the multiple DR diagnostic images constitute a DR diagnostic image set, and the above-mentioned scoring set is obtained according to the DR diagnostic image set, and the scoring set includes the scores obtained by evaluating multiple DR diagnostic images. In some embodiments, clinical experts can evaluate the DR diagnostic images in the DR diagnostic image set according to the quality of the DR diagnostic images and give expert scores. The higher the score of the DR diagnostic image, the more basic feature information content is contained in the corresponding DR original image; conversely, the lower the score of the DR diagnostic image, the less basic feature information content is contained in the corresponding DR original image. The expert scores of the DR diagnostic images constitute the expert scoring set. Optionally, if the DR images in the DR image set are visual images used by doctors for diagnosis, the scoring set can be directly obtained according to the DR image set.

[0100] Afterwards, the initial model is trained using the acquired image 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 a neural network, a support vector machine, a linear discriminant analysis, etc. The training method includes but is not limited to model training methods such as linear regression and gradient descent. Exemplarily, 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.

[0101] In one embodiment, when the image feature set, the parameter feature set and the score set are used for classification and regression training, the classification and regression training formula used is:

[0102]

[0103] Among them, x is the feature vector, {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:

[0104]

[0105] in, is the input feature, w and s are the scaling and translation parameter vectors respectively, and the kernel function k can be or

[0106] It should be noted that the embodiments of the present invention do not limit the linear transfer function, kernel function or parameters used in the regression training method.

[0107] After obtaining the trained target model in the model training stage, in the actual application process, at least one image feature among the grayscale entropy feature, texture feature, noise feature, gradient feature and divergence feature extracted from the DR image is input into the trained network model to obtain the DR feature index. Optionally, the image features can also be comprehensively calculated according to a preset function to obtain the DR feature index. The DR feature index can characterize the basic feature information content of the DR image, and can also characterize the comprehensive level between the radiation dose of X-rays received by the target object and the image quality of the DR image. The comprehensive level refers to the quality of the image of the DR image, and the comprehensive level of the radiation dose of the X-rays received. If the user thinks that the image quality is good and meets the requirements, the DR feature index can be lowered to achieve the effect of reducing the radiation dose of the X-rays received; similarly, if the image quality is considered to be poor, the DR feature index can be increased to achieve the effect of improving the image quality.

[0108] In an embodiment of the present invention, the DR characteristic index of the DR image that can be obtained by setting the exposure parameters is predicted according to the corresponding relationship between the exposure parameters and the DR characteristic index before actual exposure. In an embodiment, the corresponding relationship between the exposure parameters and the DR characteristic index can be pre-established in the following manner: controlling the DR imaging system to perform exposure under different exposure parameters to obtain DR images corresponding to different exposure parameters; extracting the DR characteristic index of the DR images corresponding to different exposure parameters according to the above method; associating the exposure parameters and the DR characteristic index corresponding to each DR image to obtain the corresponding relationship between the exposure parameters and the DR characteristic index.

[0109] Exemplarily, a human body model can be used when establishing a corresponding relationship, and the human body model is exposed under different exposure parameters to obtain DR images corresponding to different exposure parameters. Taking the exposure parameters including tube current, tube voltage and exposure time as an example, the tube voltage and tube current values ​​are set to KV1 and mA1 respectively at the initial stage, and the human body model is exposed according to different exposure parameters to obtain DR images corresponding to different exposure parameters, and the DR characteristic index of the DR image is extracted according to the above method to obtain the corresponding relationship between the exposure time and the DR characteristic index when the tube voltage and tube current are (KV1, mA1). Next, keep the tube voltage KV1 unchanged, change the tube current to mA2, and expose again under different exposure parameters to obtain the corresponding relationship between the exposure time and the DR characteristic index when the tube voltage and tube current are (KV1, mA2). Change the tube current and repeat the above process to obtain the corresponding relationship between the exposure time and the DR characteristic index under the same tube voltage and different tube current conditions. Then keep the tube current unchanged, change the tube voltage and repeat the above process to obtain the corresponding relationship between the exposure time and the DR characteristic index under the same tube current and different tube voltage conditions. Similarly, by keeping the exposure time unchanged and changing the tube current to repeat the above process, the corresponding relationship between the tube voltage and the DR characteristic index under the same exposure time and different tube current conditions can be obtained.

[0110] After obtaining the above data by exposing under different exposure parameters, the exposure parameters and DR characteristic index corresponding to each DR image can be associated and stored in the memory of the DR imaging system, or in the cloud server, and the data storage form includes but is not limited to a corresponding relationship table. In practical applications, the DR characteristic index corresponding to the set exposure parameter can be found from the pre-stored corresponding relationship to obtain the DR characteristic index prediction value. For example, the DR characteristic index corresponding to the currently set tube current, tube voltage and exposure time can be found in the corresponding relationship table as the DR characteristic index prediction value.

[0111] In another embodiment, the corresponding relationship between the exposure parameters and the DR characteristic index can be pre-established by model training, and the set exposure parameters are input into the model to obtain the predicted value of the DR characteristic index output by the model. Wherein, the corresponding relationship between the exposure parameters and the DR characteristic index is established by model training, including: obtaining the exposure parameters corresponding to multiple frames of DR images, and extracting the DR characteristic index of each frame of DR image; performing model training according to the exposure parameters and the DR characteristic index, and obtaining a model that characterizes the corresponding relationship between the exposure parameters and the DR characteristic index. Wherein, the method described above can be used to control the DR imaging system to perform exposure under different exposure parameters to obtain DR images corresponding to different exposure parameters, and extract the DR characteristic index of the DR images corresponding to different exposure parameters, and use the exposure parameters and the corresponding DR characteristic index to perform model training. Alternatively, the historical DR images stored in the DR imaging system can be obtained, the exposure parameters of the historical DR images can be obtained, and the DR characteristic index of the historical DR images can be extracted to perform model training. During the training process, the exposure parameters are used as input data and the DR feature index is used as the target value. Through continuous iteration, after the exposure parameters are input into the model, the data output by the model continues to approach the actual DR feature index. When the error is less than the set value, the training is completed.

[0112] Furthermore, due to the differences in attenuation coefficients and thicknesses between human tissues, when X-rays pass through different tissues of the human body, the degree of attenuation is different. When establishing the correspondence between the exposure parameters and the DR characteristic index, the current imaging conditions may also be considered, such as the imaging site, the imaging position, the body shape of the target object, the age of the target object, the gender of the target object, etc. When obtaining the correspondence between the exposure parameters and the DR characteristic index, the imaging conditions related to the target object to be imaged may be obtained, and then the corresponding relationship matching the imaging conditions related to the target object may be obtained, so that the predicted DR characteristic index prediction value is more accurate.

[0113] For example, when the DR imaging system has a camera or other measuring device, imaging conditions such as imaging part, imaging position, and body shape of the target object can be obtained based on the visualized image obtained by the measuring device of the DR imaging system. Alternatively, the imaging conditions can also be obtained based on user input.

[0114] By way of example, when the exposure parameter and the DR characteristic index are associated to obtain a corresponding relationship between the exposure parameter and the DR characteristic index, the DR imaging system can be controlled to perform exposure under different imaging conditions to obtain a plurality of corresponding relationships that match the different imaging conditions. For example, human models of different body shapes can be exposed to obtain corresponding relationships that match the different body shapes, or different parts of the human model can be exposed to obtain corresponding relationships that match different imaging parts, etc.

[0115] When the corresponding relationship between the exposure parameters and the DR characteristic index is obtained by the model training method, DR images obtained under different imaging conditions can be obtained, and multiple models corresponding to different imaging conditions can be trained. For example, a human model with a slightly fat body is exposed to obtain exposure parameters and DR characteristic indexes for model training, or DR images of people with a slightly fat body are obtained, the exposure parameters used during imaging are obtained, and the DR characteristic index of the DR image is extracted for model training to obtain a corresponding relationship model that matches the slightly fat body. In actual application, if the target object to be imaged is slightly fat, the corresponding relationship model can be selected to obtain the DR characteristic index prediction value to improve the accuracy of the prediction.

[0116] Through the correspondence between the above-mentioned exposure parameters and the DR characteristic index, the DR characteristic index prediction value corresponding to the current set exposure parameters can be obtained. The DR characteristic index prediction value can reflect the basic characteristic information content of the DR image obtained according to the current exposure parameters, as well as the comprehensive level between the image quality of the DR image and the exposure dose of the X-ray, so that the user can pre-evaluate the imaging effect that can be obtained by the current exposure parameters before the actual exposure to determine whether the exposure parameters need to be adjusted. The DR characteristic index prediction value can be directly displayed on the display interface of the DR imaging device, and when the exposure parameters change, such as when the user adjusts the exposure parameters, the DR characteristic index prediction value displayed on the display interface is updated in real time.

[0117] Furthermore, in order to facilitate prompting the user of the meaning of the DR characteristic index prediction value, the DR characteristic index standard value can also be obtained, the DR characteristic index prediction value is compared with the DR characteristic index standard value, and prompt information is output according to the comparison result, so that users who lack relevant experience can also clearly understand whether the current DR characteristic index prediction value is appropriate and whether the set exposure parameters need to be adjusted. For example, when the DR characteristic index prediction value obviously deviates from the DR characteristic index standard value, a warning message is given.

[0118] In some embodiments, the DR characteristic index standard value includes a DR characteristic index upper limit value and / or a DR characteristic index lower limit value. When the DR characteristic index prediction value is higher than the DR characteristic index upper limit value, it indicates that the X-ray radiation dose is too large. At this time, the first prompt information can be output, and the first prompt information includes prompt information for reducing the set exposure parameters, so as to avoid adverse effects on the human body. Generally speaking, the DR characteristic index upper limit value is used to characterize the maximum radiation dose of X-rays that can be borne by different target objects or different parts of the same target object; when the DR characteristic index prediction value is lower than the DR characteristic index lower limit value, it indicates that the image quality of the DR image is too poor, and the second prompt information is output, and the second prompt information includes prompt information for increasing the set exposure parameters, so as to ensure that the DR image contains sufficient information for diagnosis. The above prompt information can be implemented in the form of text, color, sound or light, and the prompt information can only include prompt information for reducing or increasing the exposure parameters, and can also further include prompt information for reducing or increasing the amplitude. Generally speaking, the DR characteristic index lower limit value is used to characterize the minimum radiation dose of X-rays that can be borne by different target objects or different parts of the same target object. At this minimum radiation dose, the most basic shooting requirements of DR images also need to be met. The DR characteristic index upper limit value and the DR characteristic index lower limit value may fluctuate under certain conditions, for example, a hospital requires a uniform reduction in X-ray exposure dose.

[0119] As described above, the exposure parameters mainly include tube current, tube voltage, and exposure time, etc. Therefore, the first prompt information includes prompt information for reducing the tube current, tube voltage, and / or exposure time; the second prompt information includes prompt information for increasing the tube current, tube voltage, and / or exposure time. Exemplarily, the exposure parameter to be adjusted can be selected based on the corresponding relationship between the exposure parameter and the DR characteristic index, for example, the exposure parameter with the smallest adjustment range and the best adjustment effect can be selected.

[0120] Since different target objects or different parts of the same target object can bear different X-ray radiation doses, the corresponding DR characteristic index standard values ​​are also different. Therefore, the imaging conditions related to the target object to be imaged can also be obtained, so as to obtain the DR characteristic index standard value corresponding to the imaging conditions related to the target object. The imaging conditions include the imaging part, the imaging position, the body shape of the target object, the age of the target object, the gender of the target object, etc. That is, in some embodiments, the imaging conditions can be used to select the corresponding relationship between the exposure parameters and the DR characteristic index, and can also be used to obtain the DR characteristic index standard value.

[0121] Exemplarily, the DR characteristic index standard value can be obtained by statistically analyzing the DR images of a batch of patients, or by comprehensive consideration by experts based on the exposure assessment standard of the DR image and the DR characteristic index. The DR characteristic index standard value may fluctuate. For example, if a hospital requires a uniform reduction in the exposure dose of X-rays, the DR characteristic index standard value may decrease accordingly. Therefore, in some embodiments, the DR characteristic index standard value may be calibrated as needed to better meet clinical needs.

[0122] In one embodiment, calibrating the DR feature index standard value includes: performing a calibration operation on the DR feature index standard value; based on the calibration operation, obtaining multiple frames of reference DR images, and obtaining the DR feature index of each frame of the reference DR image; performing statistics on the DR feature index of the multiple frames of reference DR images to obtain statistical results, and updating the DR feature index standard value according to the statistical results. The calibration operation can be triggered by the user, and there can be many ways to trigger the calibration operation, such as touch control, button control, remote control control, gesture control or voice control. Alternatively, the calibration operation can also be automatically triggered, such as regularly calibrating the DR feature index standard value.

[0123] Exemplarily, the multiple reference DR images may be historical DR images acquired within a preset time range. For example, when performing a calibration operation, the historical DR images acquired by the DR imaging system within the past month are acquired, the DR feature index of each frame of the historical DR image is extracted, and the statistical values ​​such as the average or median of the DR feature index of the multiple frames of the historical DR image are calculated and set as the new DR feature index standard value.

[0124] Alternatively, since abnormal images may exist in historical DR images, in order to avoid the influence of abnormal images on the standard value of DR characteristic index, historical DR images collected within a preset time range can also be displayed, and in-hospital experts can observe the image quality of historical DR images, select historical DR images, and determine the selected historical DR images as reference DR images.

[0125] Based on the DR characteristic index prediction value provided to the user, if the user believes that the DR characteristic index prediction value corresponding to the current set exposure parameter meets expectations, or the comparison result of the DR characteristic index prediction value and the DR characteristic index standard value meets expectations, DR imaging can be performed according to the current set exposure parameter. Specifically, the DR imaging system is controlled to emit X-rays to the target tissue part of the target object, and the X-rays are controlled to pass through the target tissue part; the X-rays that pass through the target tissue part are received, and the X-rays that pass through the target tissue part are processed to obtain a digital X-ray photography DR image.

[0126] During the DR imaging process, the X-rays emitted by the X-ray source are attenuated to varying degrees after passing through the human body and are received by the detector. Exemplarily, the upper layer of the detector is an X-ray conversion medium, which has photoconductivity and can convert X-rays into electronic signals, generating positive and negative charges. These charges move along the electric field in the form of current under the action of the bias voltage and are collected by the detector array of the detector. Each detection unit corresponds to a pixel of the DR image. A digital signal, i.e., a DR original image, can be obtained by amplifying and processing the electrical signal and performing analog-to-digital conversion. The DR original image can be a grayscale image that has not been post-processed by contrast adjustment, brightness adjustment, etc. The processed image of the DR original image can be an image of the DR original image after a certain transformation relationship, or an image of post-processing such as image contrast and image brightness adjustment. The DR image involved in the embodiment of the present invention can be a DR original image or a processed DR image, which is not specifically limited here.

[0127] Based on the above description, the pre-exposure prediction method 100 of the DR imaging system according to an embodiment of the present invention obtains a DR characteristic index prediction value according to the correspondence between the exposure parameters and the DR characteristic index before exposure, which can help the user discover possible abnormalities in the current set exposure parameters before exposure and guide the user to adjust the exposure parameters in time before exposure.

[0128] Another aspect of the present invention provides a pre-exposure prediction method for a DR imaging system. Figure 2 As shown, the pre-exposure prediction method 200 of the DR imaging system includes the following steps:

[0129] In step S210, a plurality of candidate body positions are displayed on a display interface;

[0130] In step S220, a body position to be imaged is determined from the plurality of candidate body positions based on a selection operation of the user;

[0131] In step S230, the exposure parameters corresponding to the body position to be imaged are determined;

[0132] In step S240, a DR characteristic index prediction value is determined according to the exposure parameter and a pre-established correspondence relationship between the exposure parameter and the DR characteristic index;

[0133] In step S250, the DR characteristic index prediction value is displayed on the display interface.

[0134] The pre-exposure prediction method 200 of the DR imaging system of the embodiment of the present invention first determines the body position to be imaged and its corresponding exposure parameters before exposure, and predicts the DR characteristic index prediction value corresponding to the exposure parameters, which can help the user discover possible abnormalities in the current exposure parameters before exposure and guide the user to adjust the exposure parameters in time before exposure.

[0135] In one example, determining the exposure parameters corresponding to the body position to be imaged includes: determining the exposure parameters corresponding to the body position to be imaged. When the user selects the body position to be imaged, the pre-set exposure parameters corresponding to the body position to be imaged are automatically retrieved. In another example, the exposure parameters may also be determined based on the user's adjustment operation, and the user may adjust the exposure parameters based on the pre-set exposure parameters or manually re-enter the exposure parameters.

[0136] Furthermore, after obtaining the DR characteristic index prediction value, the DR characteristic index standard value can also be obtained, the DR characteristic index prediction value can be compared with the DR characteristic index standard value, and prompt information can be output based on the comparison result. The prompt information can include text prompts, voice prompts, color prompts, and light prompts. The prompt information can include prompt information on whether the DR characteristic index prediction value deviates too much from the DR characteristic index standard value; further, the prompt information can also include prompt information to guide the user to lower or increase the exposure parameters.

[0137] Furthermore, the imaging conditions related to the target object to be imaged can also be determined based on the user's selection operation, and then the DR characteristic index standard value corresponding to the imaging conditions related to the target object can be obtained. Exemplarily, the imaging conditions include the body shape of the target object, the age of the target object, the gender of the target object, etc.

[0138] The pre-exposure prediction method 200 of the DR imaging system of the embodiment of the present invention has many identical or similar details to the pre-exposure prediction method 100 of the DR imaging system described above, which can be specifically referred to the relevant description above and will not be elaborated here.

[0139] Another embodiment of the present invention provides a DR imaging system, see Figure 3 The DR imaging system 300 includes: an X-ray source 310, a flat panel detector 320, a processor 330 and an output device 340, wherein the processor 330 is communicatively connected with the X-ray source 310, the flat panel detector 320 and the output device 340; the X-ray source 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 flat panel 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 output device 340 is used to output the DR image.

[0140] Exemplarily, the X-ray source 310 includes a tube for emitting X-rays; the flat panel detector 320 includes an X-ray conversion medium located in the upper layer and a detection unit array located in the lower layer. The X-ray conversion medium converts X-rays into electronic signals, generates positive and negative charges, and these charges move along the electric field in the form of current under the action of the bias voltage, are collected by the detection unit array, and converted into electrical signals. The flat panel detector 320 includes but is not limited to amorphous silicon, amorphous selenium, CCD, etc. When using the DR imaging system 300 for image acquisition, the position of the X-ray source 310 and the position of the flat panel detector 320 need to be fixed in a specified manner, so that the X-rays emitted by the X-ray source 310 can irradiate the flat panel detector 320 after passing through the target tissue, so that the flat panel detector 320 can collect the X-rays after passing through the target tissue site to obtain a DR image.

[0141] The processor 330 is used to process the X-ray 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 steps of the pre-exposure prediction method 100 or the pre-exposure prediction method 200 of the above-mentioned DR imaging system to obtain a DR characteristic index prediction value; the output device 340 is used to output the DR characteristic index prediction value. The specific details of the pre-exposure prediction method 100 and the pre-exposure prediction method 200 of the DR imaging system can be referred to above. The DR imaging system 300 according to an embodiment of the present invention obtains a DR characteristic index prediction value based on the correspondence between the exposure parameters and the DR characteristic index before exposure, which can help the user discover possible abnormalities in the current set exposure parameters before exposure, and guide the user to adjust the exposure parameters in time before exposure.

[0142] In addition, according to an embodiment of the present invention, a computer storage medium is also provided, on which program instructions are stored, and when the program instructions are executed by a computer or a processor, the corresponding steps of the pre-exposure prediction method of the DR imaging system of any embodiment of the present invention are executed. 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, an application required by at least one function; the data storage area may store data created during the execution of 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 arranged relative to the processor.

[0143] Exemplarily, the computer storage medium may include, for example, 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.

[0144] 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 a processor, it is used to execute the corresponding steps of the pre-exposure prediction method of the DR imaging system of the embodiment of the present invention.

[0145] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as required by the appended claims.

[0146] 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 to be beyond the scope of the present invention.

[0147] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0148] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention 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.

[0149] Similarly, it should be understood that in order to streamline the present invention and help understand one or more of the various inventive aspects, in the description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present invention should not be interpreted as reflecting the following intention: the claimed invention requires more features than the features explicitly stated in each claim. More specifically, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0150] It will be understood by those skilled in the art that, except for mutually exclusive features, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this specification may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0151] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0152] The various component embodiments of the present invention may 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) may be used in practice to implement some or all of the functions of some modules in the article analysis device according to an embodiment of the present invention. The present invention may also be implemented as a device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0153] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0154] The above is only a specific embodiment of the present invention or an explanation of a specific embodiment. The protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A pre-exposure prediction method for a DR imaging system, characterized in that: The method comprises: Acquire the current set exposure parameters of the DR imaging system; Acquiring a correspondence between a pre-established exposure parameter and a DR feature index, wherein the DR feature index is determined based on at least one image feature of a grayscale entropy feature, a texture feature, a noise feature, a gradient feature, and a divergence feature extracted from the DR image; Determining a DR characteristic index prediction value corresponding to the set exposure parameter according to the set exposure parameter and the corresponding relationship; Output the DR characteristic index prediction value.

2. The method according to claim 1, characterized in that The method further comprises: Obtain the standard value of DR characteristic index; The predicted value of the DR characteristic index is compared with the standard value of the DR characteristic index, and prompt information is output according to the comparison result.

3. The method according to claim 2, characterized in that The DR characteristic index standard value includes a DR characteristic index upper limit value and / or a DR characteristic index lower limit value; The outputting of prompt information according to the comparison result includes: When the DR characteristic index prediction value is higher than the DR characteristic index upper limit value, outputting first prompt information, wherein the first prompt information includes prompt information for reducing the set exposure parameter; When the predicted value of the DR characteristic index is lower than the lower limit value of the DR characteristic index, second prompt information is output, and the second prompt information includes prompt information for increasing the set exposure parameter.

4. The method according to claim 3, characterized in that The set exposure parameters include at least one of tube current, tube voltage and exposure time; The first prompt information includes prompt information for reducing the tube current, the tube voltage and / or the exposure time; The second prompt information includes prompt information for increasing the tube current, the tube voltage and / or the exposure time.

5. The method according to claim 2, characterized in that The method further comprises: Acquiring imaging conditions related to a target object to be imaged; The obtaining of the DR characteristic index standard value includes: obtaining the DR characteristic index standard value corresponding to the imaging condition related to the target object.

6. The method according to claim 2, characterized in that The method further comprises: Performing a calibration operation on the DR characteristic index standard value; Based on the calibration operation, multiple frames of reference DR images are acquired, and a DR feature index of each frame of the reference DR image is acquired; Statistics are performed on the DR feature indexes of the multiple frames of reference DR images to obtain statistical results, and the DR feature index standard value is updated according to the statistical results.

7. The method according to claim 6, characterized in that The DR characteristic index standard value includes a DR characteristic index upper limit value and / or a DR characteristic index lower limit value, and updating the DR characteristic index standard value according to the statistical result includes: On the basis of the statistical result, the preset threshold value is increased or the preset percentage is increased to obtain the upper limit value of the DR characteristic index, and / or, on the basis of the statistical result, the preset threshold value is reduced or the preset percentage is reduced to obtain the lower limit value of the DR characteristic index.

8. The method according to claim 6, characterized in that The acquiring of multiple frames of reference DR images comprises: A historical DR image collected within a preset time range is obtained as the reference DR image.

9. The method according to claim 6, characterized in that The acquiring of multiple frames of reference DR images comprises: Display historical DR images collected within a preset time range; A selection operation is performed on the historical DR image, and the selected historical DR image is determined as the reference DR image.

10. The method according to claim 1, characterized in that The method further comprises: Acquiring imaging conditions related to a target object to be imaged; The obtaining of the pre-established correspondence between the exposure parameter and the DR characteristic index includes: obtaining a correspondence that matches the imaging condition related to the target object.

11. The method according to claim 1, characterized in that The corresponding relationship between the exposure parameters and the DR characteristic index can be pre-established in the following manner: Controlling the DR imaging system to perform exposure under different exposure parameters to obtain DR images corresponding to the different exposure parameters; Extracting DR characteristic indexes of the DR images corresponding to the different exposure parameters; The exposure parameter corresponding to each DR image is associated with the DR characteristic index to obtain a corresponding relationship between the exposure parameter and the DR characteristic index.

12. The method according to claim 11, characterized in that Determining the DR characteristic index prediction value corresponding to the set exposure parameter based on the set exposure parameter and the corresponding relationship includes: searching for the DR characteristic index corresponding to the set exposure parameter from the pre-stored corresponding relationship to obtain the DR characteristic index prediction value.

13. The method according to claim 11, characterized in that The DR imaging system performs exposure under different imaging conditions, and the corresponding relationship includes a plurality of corresponding relationships respectively matched with the different imaging conditions.

14. The method according to claim 1, wherein: The corresponding relationship between the exposure parameters and the DR characteristic index can be pre-established in the following manner: Obtaining exposure parameters corresponding to multiple frames of DR images, and extracting DR feature indexes of each frame of DR images; Model training is performed according to the exposure parameters and the DR characteristic index to obtain a model that characterizes the corresponding relationship between the exposure parameters and the DR characteristic index.

15. The method according to claim 14, characterized in that Determining the DR characteristic index prediction value corresponding to the set exposure parameter according to the set exposure parameter and the corresponding relationship includes: inputting the set exposure parameter into the model to obtain the DR characteristic index prediction value output by the model.

16. The method according to claim 14, characterized in that The multiple frames of DR images include DR images obtained under different imaging conditions, and the model includes a plurality of models corresponding to the different imaging conditions.

17. The method according to any one of claims 5, 10-16, characterized in that: The imaging conditions include at least one of the following: Imaging site, imaging position, target subject's body shape, target subject's age, target subject's gender.

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

19. The method according to claim 1, wherein: Extracting the texture features from the DR image 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 directions 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.

20. The method of claim 1, wherein: Extracting the noise feature from the DR image includes: Performing filtering on the DR image to obtain a high-frequency image; Calculating the root mean square of the 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.

21. The method according to claim 1, characterized in that Extracting the gradient feature from the DR image includes: Determining the regional distribution of different tissues in the DR image; The clarity of the boundaries of different tissue region distributions is obtained as the gradient feature.

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

23. A pre-exposure prediction method for a DR imaging system, characterized in that: The method comprises: Displaying multiple alternative body positions on a display interface; Determining a body position to be imaged from the plurality of candidate body positions based on a selection operation of a user; Determining exposure parameters corresponding to the body position to be imaged; Determining a predicted value of the DR characteristic index according to the exposure parameter and a pre-established correspondence relationship between the exposure parameter and the DR characteristic index; The DR characteristic index prediction value is displayed on the display interface.

24. The method according to claim 23, characterized in that The step of determining the exposure parameter corresponding to the body position to be imaged includes: Determining a preset exposure parameter corresponding to the body position to be imaged; Alternatively, the exposure parameter is determined based on an adjustment operation of a user.

25. The method of claim 23, wherein: The method further comprises: Obtain the standard value of DR characteristic index; The predicted value of the DR characteristic index is compared with the standard value of the DR characteristic index, and prompt information is output according to the comparison result.

26. The method of claim 25, wherein: The method further comprises: determining an imaging condition related to a target object to be imaged based on a selection operation of a user; The obtaining of the DR characteristic index standard value includes: obtaining the DR characteristic index standard value corresponding to the imaging condition related to the target object.

27. The method of claim 26, wherein: The imaging conditions include at least one of the following: The target object's body type, the target object's age, and the target object's gender.

28. A DR imaging system, characterized in that: The DR imaging system includes: an X-ray source, a flat panel detector, a processor and an output device; The X-ray source is used to emit X-rays to a target part of a target object so that the X-rays pass through the target part; The flat panel detector is used to receive X-rays after passing through the target part; The processor is used to process the X-ray after passing through the target part to obtain a digital X-ray photography DR image, and the processor is also used to obtain a DR characteristic index prediction value according to the pre-exposure prediction method of the DR imaging system according to any one of claims 1-27; The output device is used to output the DR characteristic index prediction value.

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