Exposure control method of DR imaging system and DR imaging system

Abstract

An exposure control method of a DR imaging system and the DR imaging system, the method comprising: emitting an X-ray to a target portion of a target object, allowing the X-ray to pass through the target portion; receiving the X-rays passing through the target part, and generating digital radiography (DR) image data according to the X-rays passing through the target part; reading DR image data according to a preset time interval; determining an accumulated DR feature index of DR image data read from the beginning of exposure, the DR feature index being 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 data; and when the accumulated DR characteristic index reaches the target DR characteristic index, controlling the DR imaging system to stop exposure. The stop time of exposure is determined according to the DR characteristic index, and automatic exposure control can be realized by replacing an ionization chamber.

Description

Technical Field

[0001] The present invention relates to the field of medical image technology, and more particularly to an exposure control 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. As the exposure dose increases, the voltage value of the ionization chamber output signal will also increase. When the voltage value reaches the set reference voltage, the exposure ends. However, there are certain defects in automatic exposure control through an ionization chamber. The ionization chamber is located on the surface of the flat-panel detector and will also absorb X-rays, affecting the absorption of X-rays by the flat-panel detector. This will result in the need to increase the X-ray dose in the X-ray source and increase the X-ray dose received by the patient. In addition, the ionization chamber will cause artifact interference to the DR image, affecting the image quality. In addition, the ionization chamber needs to be connected to the high-voltage generator through a dedicated cable, which is complex to manufacture and costly. Moreover, as a fixed installation device, the ionization chamber cannot be used for shooting outside the bed. 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 an exposure control method for a DR imaging system, the method comprising:

[0006] emitting X-rays to a target part of a target object so that the X-rays pass through the target part;

[0007] receiving X-rays after passing through the target part, and generating digital X-ray photography DR image data according to the X-rays after passing through the target part;

[0008] Reading the DR image data at a preset time interval;

[0009] Determining an accumulated DR feature index of the DR image data read since the start of exposure, 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 data;

[0010] When the accumulated DR characteristic index reaches the target DR characteristic index, the DR imaging system is controlled to stop exposure.

[0011] In one embodiment, the method further comprises: generating a single or multiple DR images according to the DR image data read within a time range from starting exposure to stopping exposure.

[0012] In one embodiment, determining the accumulated DR feature index of the DR image data read since the start of exposure includes: acquiring the DR image data corresponding to each of the preset time intervals, and calculating the DR feature index of the DR image data corresponding to each of the preset time intervals; and accumulating the DR feature indexes of the DR image data corresponding to at least two of the preset time intervals to obtain the accumulated DR feature index.

[0013] In one embodiment, determining the accumulated DR feature index of the DR image data read since the start of exposure includes: accumulating the DR image data corresponding to at least two of the preset time intervals acquired since the start of exposure to obtain accumulated DR image data; determining the DR feature index of the accumulated DR image data to obtain the accumulated DR feature index.

[0014] In one embodiment, determining the accumulated DR feature index of the DR image data read since the start of exposure includes: determining the accumulated DR feature index of the DR image data as a whole, or determining the accumulated DR feature index of a target area in the DR image data.

[0015] In one embodiment, the method further includes: acquiring current imaging conditions; and determining the target DR characteristic index according to the imaging conditions.

[0016] In one embodiment, the imaging condition includes at least one of the following: exposure parameter, imaging part, imaging position, body shape of the target object, gender of the target object, and age of the target object.

[0017] In one embodiment, the method also includes: obtaining a calibration operation for the target DR feature index; based on the calibration operation, obtaining multiple frames of reference DR image data, and obtaining the DR feature index of each frame of the reference DR image data; performing statistics on the DR feature indexes of the multiple frames of reference DR image data to obtain statistical results, and updating the target DR feature index according to the statistical results.

[0018] In one embodiment, acquiring multiple frames of reference DR image data based on the calibration operation includes: acquiring historical DR image data collected within a preset time range as the reference DR image data.

[0019] In one embodiment, based on the calibration operation, multiple frames of reference DR image data are obtained, including: displaying historical DR image data collected within a preset time range; performing a selection operation on the historical DR image data, and based on the selection operation, determining the selected historical DR image data as the reference DR image data.

[0020] Another aspect of an embodiment of the present invention provides an exposure control method for a DR imaging system, the method comprising:

[0021] emitting X-rays to a target part of a target object so that the X-rays pass through the target part;

[0022] receiving X-rays after passing through the target part, and generating digital X-ray photography DR image data according to the X-rays after passing through the target part;

[0023] Reading the DR image data at a preset time interval, and determining a DR feature index of at least one frame of the DR image data, 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 data;

[0024] Determining a predicted exposure stop condition according to a DR feature index and a target DR feature index of the at least one frame of DR image data;

[0025] When the predicted exposure stop condition is met, the DR imaging system is controlled to stop exposure.

[0026] In one embodiment, the predicted exposure stop condition includes a predicted exposure time, and when the predicted exposure stop condition is met, controlling the DR imaging system to stop exposure includes: stopping exposure when the exposure time reaches the predicted exposure time;

[0027] Alternatively, the predicted exposure stop condition includes a predicted target voltage value, and when the predicted exposure stop condition is met, controlling the DR imaging system to stop exposure includes: stopping exposure when the voltage value reaches the target voltage value;

[0028] Alternatively, the predicted exposure stop condition includes a predicted target current value, and when the predicted exposure stop condition is met, controlling the DR imaging system to stop exposure includes: stopping exposure when the current value reaches the predicted target current value.

[0029] In one embodiment, when the predicted exposure stop condition is the predicted exposure time, determining the predicted exposure stop condition based on the DR feature index and the target DR feature index of the at least one frame of DR image data includes: determining the number of frames of the DR image data required to complete the exposure based on the DR feature index and the target DR feature index of the DR image data corresponding to at least one of the preset time intervals; determining the predicted exposure time based on the number of frames of the DR image data required to complete the exposure and the at least one preset time interval.

[0030] In one embodiment, the reading of the DR image data at a preset time interval and determining the DR feature index of at least one frame of the DR image data includes: reading a frame of DR image data at a current moment and determining the DR feature index of the frame of DR image data; or, accumulatively reading multiple frames of the DR image data at the preset time interval, and determining the accumulated DR feature index based on the accumulated read multiple frames of DR image data.

[0031] A third aspect of an embodiment of the present invention provides an exposure control method for a DR imaging system, the method comprising:

[0032] emitting X-rays to a target part of a target object so that the X-rays pass through the target part;

[0033] receiving X-rays after passing through the target part, and generating digital X-ray photography DR image data according to the X-rays after passing through the target part;

[0034] determining a DR feature index of the DR image data, wherein the DR feature index is determined based on an image feature extracted from the DR image data;

[0035] When the DR characteristic index reaches a target DR characteristic index, the DR imaging system is controlled to stop exposure.

[0036] In one embodiment, determining the DR feature index of the DR image data includes: determining the DR feature index of one frame of the DR image data; or determining the accumulated DR feature index of multiple frames of the DR image data.

[0037] In one embodiment, the method further comprises: calibrating the target DR characteristic index based on a calibration operation performed on the target DR characteristic index.

[0038] A fourth aspect of an embodiment of the present invention provides a DR imaging system, the DR imaging system comprising: an X-ray source, a flat panel detector, a processor and a controller;

[0039] 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;

[0040] The flat panel detector is used to receive the X-rays after passing through the target part, and generate DR image data according to the X-rays after passing through the target part;

[0041] The processor is used to determine a DR feature index of the DR image data, wherein the DR feature index is determined based on an image feature extracted from the DR image data;

[0042] The controller is used to control the DR imaging system to stop exposure when the DR characteristic index reaches a target DR characteristic index.

[0043] The exposure control method and the DR imaging system of the embodiment of the present invention can determine the exposure stop time in real time according to the accumulated DR characteristic index, and can replace the ionization chamber to realize automatic exposure control. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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.

[0045] Figure 1 A schematic flow chart showing an exposure control method of a DR imaging system according to an embodiment of the present invention;

[0046] Figure 2 A schematic flow chart showing an exposure control method of a DR imaging system according to another embodiment of the present invention;

[0047] Figure 3A schematic flow chart showing an exposure control method of a DR imaging system according to yet another embodiment of the present invention;

[0048] Figure 4 A schematic block diagram of a DR imaging system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] Next, first refer to Figure 1 The exposure control method 100 of the DR imaging system according to one embodiment of the present invention is described. Figure 1As shown, the exposure control method 100 of the DR imaging system may include the following steps:

[0055] In step S110, X-rays are emitted to a target part of a target object so that the X-rays pass through the target part;

[0056] In step S120, receiving the X-ray after passing through the target part, and generating digital X-ray photography DR image data according to the X-ray after passing through the target part;

[0057] In step S130, the DR image data is read at a preset time interval;

[0058] In step S140, an accumulated DR feature index of the DR image data read since the start of exposure is determined, 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 data;

[0059] In step S150, when the accumulated DR characteristic index reaches the target DR characteristic index, the DR imaging system is controlled to stop exposure.

[0060] The exposure control method 100 of the DR imaging system of the embodiment of the present invention reads DR image data at preset time intervals, and determines the exposure stop time in real time according to the accumulated DR characteristic index of multiple DR image data. It can replace the ionization chamber to realize automatic exposure control, avoid the need to increase the X-ray dose of the X-ray source due to the ionization chamber absorbing X-rays, and avoid the ionization chamber from generating artifacts in the DR image.

[0061] Specifically, during the DR imaging process, the processor of the DR imaging system controls the X-ray source to emit X-rays to the target part of the target object, so that the X-rays pass through the target part, which may be a tissue part to be examined in the human body or other animal body, such as the head, abdomen, etc. At the same time, the processor controls the flat panel detector of the DR imaging system to receive the X-rays after passing through the target part, and generates DR image data according to the X-rays after passing through the target part.

[0062] In this process, after the X-rays emitted by the X-ray source pass through the target part, they are attenuated to varying degrees and are received by the flat-panel detector. Exemplarily, the upper layer of the flat-panel 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 detection unit array of the flat-panel detector. Each detection unit corresponds to a pixel of the DR image. A digital signal, i.e., DR original image data, can be obtained by amplifying and processing the electrical signal and performing analog-to-digital conversion. The DR original image data is a grayscale image that has not been post-processed by contrast adjustment, brightness adjustment, etc. Furthermore, the DR original image data can also be subjected to a certain transformation relationship, or post-processing such as image contrast and image brightness adjustment to obtain processed DR image data, i.e., the DR image data of the embodiment of the present invention can be a DR original image or DR image data after any post-processing of the DR original image data, which is not specifically limited here.

[0063] Usually, when automatic exposure control is performed, the DR image data read during the exposure time is obtained, and a complete frame of DR image is generated. However, in the embodiment of the present invention, the exposure time corresponding to a complete frame of DR image is divided into multiple preset time intervals, and the DR image data is read in real time at a high speed according to the preset time intervals. When the exposure is stopped, a single or multiple complete DR images are generated according to the DR image data read during multiple preset time intervals within the time range from the start of exposure to the stop of exposure. The preset time interval is much shorter than the exposure time corresponding to a normal frame of DR image, and the lengths of the preset time intervals are equal or unequal. For the convenience of description, the DR image data read at each preset time interval is referred to as a DR image data segment below.

[0064] In the process of reading DR image data at high speed according to preset time intervals, the accumulated DR feature index of the DR image data read since the start of exposure is determined, and when the accumulated DR feature index reaches the target DR feature index, the DR imaging system is controlled to stop exposure.

[0065] In one example, when accumulating DR feature indices of multiple DR image data segments, after obtaining the DR image data segment corresponding to each preset time interval, the DR feature index of each DR image data segment can be calculated, and then the DR feature indices of at least two DR image data segments can be accumulated to obtain an accumulated DR feature index.

[0066] For example, after the exposure switch is turned on and exposure begins, the DR image data segment D1 is read at the first preset time interval t1, and its DR feature index IFI1 is calculated. The DR image data segment D2 is read at the second preset time interval t2, and its DR feature index IFI2 is calculated. By analogy, the DR image data segment Dn is read at the nth preset time interval tn, and its DR feature index is IFIn, and IFI1+IFI2+…+IFIn=IFI target , IFI target is the target DR feature index. At this time, it can be determined that the DR image data segments read from the n preset time intervals from t1 to tn can constitute a complete DR image, and the DR feature index of the DR image is close to the target DR feature index, that is, the imaging quality of the DR image meets expectations. Therefore, the exposure can be stopped at this time, and a complete DR image is generated according to the DR image data read at n preset time intervals.

[0067] Another way to calculate the accumulated DR feature index is: first accumulate at least two DR image data segments acquired since the start of exposure to obtain accumulated DR image data, and then determine the DR feature index of the accumulated DR image data to obtain the accumulated DR feature index. For example, the DR image data segment D1 is read at the first preset time interval t1, and its DR feature index IFI1 is calculated. The DR image data segment D2 is read at the second preset time interval t2, D1 and D2 are accumulated to obtain the accumulated DR image data segment D1+D2, and the DR feature index IFI2 of D1+D2 is calculated. By analogy, if the DR image data segment Dn is read at the nth preset time interval tn, the DR feature index of the accumulated DR image data segment D1+D2+…+Dn is IFIn, and IFIn=IFI target , the exposure can be stopped at this time, and a complete DR image is generated according to the DR image data read at n preset time intervals.

[0068] Exemplarily, when calculating the DR feature index and the accumulated DR feature index, the accumulated DR feature index of the entire DR image data may be determined, or the accumulated DR feature index of the target area in the DR image data may be determined to reduce the amount of calculation.

[0069] Among them, the DR feature index is determined based on at least one image feature among the grayscale entropy feature, texture feature, noise feature, gradient feature and divergence feature extracted from the DR image data, 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.

[0070] 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.

[0071] For example, the grayscale entropy feature can be extracted from the DR image in the following manner:

[0072] 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:

[0073]

[0074] 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:

[0075] pH'(i)=pNH(i) / ∑pNH(i) Formula (2)

[0076] 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:

[0077]

[0078] Thus, the grayscale entropy feature can be obtained after filtering out the redundant grayscale signal sources.

[0079] 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.

[0080] 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.

[0081] 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:

[0082] P(i,j)=#{(x1,y1),(x2,y2)∈M*N|f(x1,y1)=i,f(x2,y2)=j} Formula (4)

[0083] 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.

[0084] 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:

[0085] 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;

[0086] 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.

[0087] 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.

[0088] The fourth texture feature P4 is used to measure the distribution of values ​​in the texture feature description matrix and the local texture variation of the DR image. The larger the P4 value is, the stronger the regularity of the texture is.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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:

[0094]

[0095] 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:

[0096]

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Exemplarily, the gradient feature can be calculated as follows:

[0102]

[0103] 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).

[0104] 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.

[0105] For example, the divergence feature can be calculated as follows:

[0106]

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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:

[0113]

[0114] 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:

[0115]

[0116] in, is the input feature, w and s are the scaling and translation parameter vectors respectively, and the kernel function k can be or

[0117] 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.

[0118] 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.

[0119] After the accumulated DR feature index is obtained, it is compared with the target DR feature index. If the accumulated DR feature index reaches the target DR feature index, the exposure is stopped. Since the accumulated DR feature index corresponds to the accumulated DR image data, a DR image can be generated according to the accumulated DR image data after the exposure is stopped, so that the DR feature index of the DR image is close to the target DR feature index.

[0120] The target DR characteristic index may vary depending on the actual clinical situation. In some embodiments, the current imaging conditions may also be obtained, and the target DR characteristic index may be determined based on the current imaging conditions to improve the accuracy of the target DR characteristic index. Exemplarily, the current DR imaging conditions include at least one of the following: exposure parameters, imaging site, imaging position, body shape of the target object, gender of the target object, and age of the target object.

[0121] Exemplarily, when the DR imaging system has a camera device or other measuring device, imaging conditions such as imaging part, imaging position, and body shape of the target object can be obtained according to the visualized image obtained by the measuring device of the DR imaging system. Alternatively, the imaging conditions can also be obtained according to user input. Exemplarily, the exposure parameter includes at least one of a tube current and a tube voltage.

[0122] As clinical needs vary, the target DR characteristic index is not static. For example, if the hospital requires a reduction in the X-ray radiation dose, the target DR characteristic index will also be reduced. In order to make the target DR characteristic index meet clinical needs, the embodiment of the present invention performs a calibration operation on the current target DR characteristic index according to actual needs. The calibration operation can be manually 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 target DR characteristic index.

[0123] Next, the current target DR feature index is calibrated in response to the calibration operation, and the calibrated target DR feature index is obtained. In one embodiment, the current target DR feature index is calibrated, including: acquiring multiple frames of reference DR images; calibrating the current target DR feature index according to the multiple frames of reference DR images. The multiple frames of reference DR images can be DR images that meet certain requirements, or they can be multiple frames of DR images acquired randomly. The target DR feature index can be calibrated according to the statistical results of the DR feature index of the multiple frames of reference DR images, and the statistical results include statistical values ​​such as mean, mean square error, and median. Calibrating the current target DR feature index according to the statistical results can be to directly use the statistical results such as the mean as the calibrated target DR feature index, or to adjust the current target DR feature index based on the statistical results, such as weighted summing the statistical results and the current target DR feature index.

[0124] Exemplarily, the multi-frame reference DR image may be a multi-frame historical DR image acquired within a preset time range. In order to reduce the deviation caused by different equipment or application scenarios, the multi-frame reference DR image may be a multi-frame historical DR image acquired by the current DR imaging system within a preset time range, or may be a multi-frame historical DR image acquired by the department or medical institution where the current DR imaging system is located within a preset time range. The preset time range may be set based on user operation, for example, the user may select the time period of the hospital's dose reduction as the preset time range; alternatively, the default preset time range may be used.

[0125] When calibrating the target DR feature index based on multiple frames of historical DR images, an automatic calibration method can be used. For example, when the calibration operation of the target DR feature index is triggered, the DR imaging system automatically obtains multiple frames of historical DR images collected within a preset time range, automatically counts the DR feature index of the multiple frames of historical DR images, and updates the target DR feature index based on the statistical results. Alternatively, the historical DR images can be automatically screened, and the historical DR images that meet the preset requirements are selected to calibrate the target DR feature index.

[0126] Exemplarily, the reference DR image can also be manually selected by the user. Specifically, multiple frames of historical DR images acquired within a preset time range are displayed, and a selection operation is performed on the historical DR image, and the selected historical DR image is determined as the reference DR image. For example, multiple frames of historical DR images can be displayed frame by frame or tiled in batches in the calibration interface, and in-hospital experts can observe the image quality of the historical DR images and select several frames of historical DR images that they recognize for calibration of the target DR feature index.

[0127] Exemplarily, after the DR characteristic index of the reference DR image is counted and the calibrated DR characteristic index is obtained according to the statistical result, the calibrated target DR characteristic index can also be adjusted based on the user operation. Specifically, the in-hospital expert can adjust the calibrated target DR characteristic index according to the exposure evaluation standard, imaging conditions, experience, etc.

[0128] Based on the above description, the exposure control method 100 of the DR imaging system according to the embodiment of the present invention determines the exposure stop time in real time according to the accumulated DR characteristic index, and can replace the ionization chamber to realize automatic exposure control.

[0129] Another aspect of the present invention provides an exposure control method for a DR imaging system. Figure 2 As shown, the exposure control method 200 of the DR imaging system includes the following steps:

[0130] In step S210, X-rays are emitted to a target part of a target object so that the X-rays pass through the target part;

[0131] In step S220, receiving the X-ray after passing through the target part, and generating digital X-ray photography DR image data according to the X-ray after passing through the target part;

[0132] In step S230, the DR image data is read at a preset time interval, and a DR feature index of at least one frame of the DR image data is determined, 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 data;

[0133] In step S240, a predicted exposure stop condition is determined according to the DR feature index of the at least one frame of DR image data and the target DR feature index;

[0134] In step S250, when the predicted exposure stop condition is met, the DR imaging system is controlled to stop exposure.

[0135] The difference between the exposure control method 200 of the DR imaging system of the embodiment of the present invention and the exposure control method 100 of the DR imaging system described above is that, in the exposure control method 100 of the DR imaging system, multiple DR image data segments are read at high speed and the accumulated DR feature indexes of the multiple DR image data segments are determined in real time, and the exposure is immediately stopped when the accumulated DR feature indexes of the multiple DR image data segments reach the target DR feature index, while in the exposure control method 200 of the DR imaging system, there is no need to calculate the accumulated DR feature index of all DR image data, but the predicted exposure stop condition is determined based on the DR feature index of at least one frame of DR image data, and the exposure is stopped when the predicted exposure stop condition is met, thereby reducing the number of times required to calculate the DR feature index and further reducing the amount of calculation.

[0136] In one embodiment, the predicted exposure stop condition includes a predicted exposure time, and the predicted exposure stop condition is satisfied when the actual exposure time reaches the predicted exposure time. Specifically, the number of frames of DR image data required to complete the exposure is determined based on the DR feature index of the DR image data corresponding to at least one preset time interval and the target DR feature index; the predicted exposure time is determined based on the number of frames of DR image data required to complete the exposure and at least one preset time interval.

[0137] For example, assuming that the DR image data segment D1 is read at the first preset time interval t1, its DR feature index IFI1 is calculated, and the target DR feature index is IFI target , calculate IFI target=n*IFI1, then the number of frames of DR image data required to complete the exposure is determined to be n, and the predicted exposure time is n*t1. When the actual exposure time reaches n*t1, the exposure is stopped. Alternatively, in order to further improve the accuracy of the predicted exposure time, the DR image data segment D2 read at the second preset time interval t2 can also be obtained, its DR feature index IFI2 is calculated, and the average value IFImean of IFI1 and IFI2 is determined, IFI target =n*IFImean, then the number of frames of DR image data required to complete the exposure is determined to be n, and the predicted exposure time is n*t1.

[0138] Alternatively, the predicted exposure stop condition may also include a predicted target voltage value or a predicted target current value, and when the predicted exposure stop condition is met, the DR imaging system is controlled to stop exposure, including: stopping exposure when the actual voltage value reaches the target voltage value, or stopping exposure when the actual current value reaches the predicted target current value. The voltage value or current value is the voltage value or current value fed back by the flat panel detector.

[0139] Exemplarily, reading DR image data at a preset time interval and determining a DR feature index of at least one frame of DR image data includes: reading a frame of DR image data at a current moment and determining a DR feature index of a frame of DR image data at a current moment. Thereafter, determining a predicted exposure stop condition according to the DR feature index of the frame of DR image data at a current moment.

[0140] Alternatively, multiple frames of DR image data are accumulated and read at preset time intervals, and the accumulated DR feature index is determined based on the accumulated read multiple frames of DR image data. For example, when the DR image data D1 is read at the first preset time interval t1, its DR feature index IFI1 is calculated, and when the DR image data D2 is read at the second preset time interval t2, its DR feature index IFI2 is calculated, and the accumulated DR feature index IFI1+IFI2 of the first two preset time intervals is calculated. Alternatively, after the DR image data D2 is read at the second preset time interval t2, D1 and D2 are accumulated to obtain the accumulated DR image data D1+D2, and the DR feature index IFI2 of D1+D2 is calculated.

[0141] The exposure control method 200 of the DR imaging system of the embodiment of the present invention has many identical or similar details to the exposure control method 100 of the DR imaging system described above, which can be specifically referred to above and will not be described in detail here. The exposure control method 200 of the DR imaging system of the embodiment of the present invention reads DR image data at a preset time interval, predicts the exposure stop condition according to the DR characteristic index of the DR image data, can replace the ionization chamber to realize automatic exposure control, and can reduce the amount of calculation.

[0142] Another aspect of the present invention provides an exposure control method for a DR imaging system. Figure 3 As shown, the exposure control method 300 of the DR imaging system includes the following steps:

[0143] In step S310, X-rays are emitted to a target part of a target object, so that the X-rays pass through the target part;

[0144] In step S320, receiving the X-ray after passing through the target part, and generating digital X-ray photography DR image data according to the X-ray after passing through the target part;

[0145] In step S330, a DR feature index of the DR image data is determined, wherein the DR feature index is determined based on image features extracted from the DR image data;

[0146] In step S340, when the DR characteristic index reaches the target DR characteristic index, the DR imaging system is controlled to stop exposure.

[0147] Wherein, determining the DR feature index of DR image data includes: determining the DR feature index of one frame of DR image data; or determining the DR feature index accumulated from multiple frames of DR image data. When determining the DR feature index of one frame of DR image data, the one frame of image data may be DR image data read since the start of exposure, and the one frame of image data may be read from a cache. When determining the DR feature index accumulated from multiple frames of DR image data, multiple frames of DR image data may be read at preset time intervals, and the DR feature indexes of the multiple frames of DR image data may be accumulated. The DR feature index may be determined based on at least one image feature of grayscale entropy features, texture features, noise features, gradient features, and divergence features extracted from the DR image data, or may be determined based on other image features, and this embodiment does not impose any limitation on this.

[0148] In some embodiments, the target DR characteristic index can be determined according to the current DR imaging conditions, and the DR imaging conditions include at least one of the following: exposure parameters, imaging part, imaging position, body shape of the target object, gender of the target object, and age of the target object. Furthermore, a calibration instruction operation based on the target DR characteristic index can also be received to calibrate the target DR characteristic index.

[0149] The exposure control method 300 of the embodiment of the present invention can be used to generate a single-frame DR image, and can also be used to perform real-time DR imaging. When used for real-time DR imaging, after detecting that the DR feature index reaches the target DR feature index and stopping exposure, X-rays can be emitted to the target part of the target object again, so that the X-rays pass through the target part; the X-rays that pass through the target part are received, and DR image data is generated according to the X-rays that pass through the target part, and the DR feature index corresponding to the DR image data is obtained. When the DR feature index reaches the target DR feature index, the exposure is stopped and the next DR image is obtained.

[0150] The exposure control method 300 of the DR imaging system of the embodiment of the present invention has many identical or similar details to the exposure control method 100 of the DR imaging system described above, which can be specifically referred to above and will not be described in detail here. The exposure control method 300 of the DR imaging system of the embodiment of the present invention performs exposure control according to the DR characteristic index of the DR image data, stops exposure when the DR characteristic index reaches the target DR characteristic index, and can replace the ionization chamber to realize automatic exposure control.

[0151] Another embodiment of the present invention provides a DR imaging system, see Figure 4 The DR imaging system 400 includes: an X-ray source 410, a flat panel detector 420, a processor 430 and a controller 440, wherein the X-ray source 410 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 420 is used to receive the X-rays after passing through the target part, and generate DR image data according to the X-rays after passing through the target part; the processor 430 is used to determine a DR feature index of the DR image data, wherein the DR feature index is determined according to image features extracted from the DR image data; and the controller 440 is used to control the DR imaging system to stop exposure when the DR feature index reaches a target DR feature index.

[0152] Exemplarily, the X-ray source 410 includes a tube for emitting X-rays; the flat panel detector 420 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 420 includes but is not limited to amorphous silicon, amorphous selenium, CCD, etc. When using the DR imaging system 400 for image acquisition, the position of the X-ray source 410 and the position of the flat panel detector 420 need to be fixed in a specified manner, so that the X-rays emitted by the X-ray source 410 can irradiate the flat panel detector 420 after passing through the target tissue, so that the flat panel detector 420 can collect the X-rays after passing through the target tissue site to obtain DR image data.

[0153] The processor 430 is used to extract image features from the DR image data. For example, the image features include at least one of grayscale entropy features, texture features, noise features, gradient features, and divergence features; and determine a DR feature index according to the image features. The controller 440 is used to compare the DR feature index with a target DR feature index, and when the DR feature index reaches the target DR feature index, control the DR imaging system to stop exposure. The processor 430 and the controller 440 may be the same component or different components.

[0154] The DR imaging system 400 of the embodiment of the present invention determines the exposure stop time in real time according to the DR characteristic index extracted from the DR image data, and can replace the ionization chamber to realize automatic exposure control.

[0155] 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 exposure control 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.

[0156] 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.

[0157] 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 run by a computer or a processor, it is used to execute the corresponding steps of the exposure control method of the DR imaging system of the embodiment of the present invention.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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. An exposure control method for a DR imaging system, characterized in that: The method comprises: emitting X-rays to a target part of a target object so that the X-rays pass through the target part; receiving X-rays after passing through the target part, and generating digital X-ray photography DR image data according to the X-rays after passing through the target part; Reading the DR image data at a preset time interval; Determining an accumulated DR feature index of the DR image data read since the start of exposure, 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 data; When the accumulated DR characteristic index reaches the target DR characteristic index, the DR imaging system is controlled to stop exposure.

2. The method according to claim 1, characterized in that Also includes: A single or multiple DR images are generated based on the DR image data read within the time range from the start of exposure to the stop of exposure.

3. The method according to claim 1, characterized in that The step of determining the accumulated DR characteristic index of the DR image data read since the start of exposure comprises: Acquire the DR image data corresponding to each of the preset time intervals, and calculate the DR feature index of the DR image data corresponding to each of the preset time intervals; The DR feature indexes of the DR image data corresponding to at least two of the preset time intervals are accumulated to obtain the accumulated DR feature index.

4. The method according to claim 1, characterized in that The step of determining the accumulated DR characteristic index of the DR image data read since the start of exposure comprises: Accumulating the DR image data corresponding to at least two preset time intervals acquired since the start of exposure to obtain accumulated DR image data; A DR feature index of the accumulated DR image data is determined to obtain the accumulated DR feature index.

5. The method according to claim 1, characterized in that The step of determining the accumulated DR characteristic index of the DR image data read since the start of exposure comprises: determining an accumulated DR feature index of the entire DR image data, Alternatively, an accumulated DR feature index of the target area in the DR image data is determined.

6. The method according to claim 1, characterized in that Also includes: Get current imaging conditions; The target DR feature index is determined according to the current imaging condition.

7. The method according to claim 6, characterized in that The imaging conditions include at least one of the following: Exposure parameters, imaging part, imaging position, body shape of the target object, gender of the target object, and age of the target object.

8. The method according to claim 1 or 6, characterized in that: Also includes: Performing a calibration operation on the target DR characteristic index; Based on the calibration operation, multiple frames of reference DR image data are acquired, and a DR feature index of each frame of the reference DR image data is acquired; Statistics are performed on the DR feature indexes of the multiple frames of reference DR image data to obtain statistical results, and the target DR feature index is updated according to the statistical results.

9. The method according to claim 8, characterized in that The step of acquiring multiple frames of reference DR image data based on the calibration operation includes: The historical DR image data collected within a preset time range is acquired as the reference DR image data.

10. The method according to claim 8, characterized in that The step of acquiring multiple frames of reference DR image data based on the calibration operation includes: Display historical DR image data collected within a preset time range; A selection operation is performed on the historical DR image data, and based on the selection operation, the selected historical DR image data is determined as the reference DR image data.

11. An exposure control method for a DR imaging system, characterized in that: The method comprises: emitting X-rays to a target part of a target object so that the X-rays pass through the target part; receiving X-rays after passing through the target part, and generating digital X-ray photography DR image data according to the X-rays after passing through the target part; Reading the DR image data at a preset time interval, and determining a DR feature index of at least one frame of the DR image data, 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 data; Determining a predicted exposure stop condition according to a DR feature index and a target DR feature index of the at least one frame of DR image data; When the predicted exposure stop condition is met, the DR imaging system is controlled to stop exposure.

12. The method according to claim 11, characterized in that The predicted exposure stop condition includes a predicted exposure time, and when the predicted exposure stop condition is met, controlling the DR imaging system to stop exposure includes: stopping exposure when the exposure time reaches the predicted exposure time; Alternatively, the predicted exposure stop condition includes a predicted target voltage value, and when the predicted exposure stop condition is met, controlling the DR imaging system to stop exposure includes: stopping exposure when the voltage value reaches the target voltage value; Alternatively, the predicted exposure stop condition includes a predicted target current value, and when the predicted exposure stop condition is met, controlling the DR imaging system to stop exposure includes: stopping exposure when the current value reaches the predicted target current value.

13. The method according to claim 12, characterized in that When the predicted exposure stop condition is the predicted exposure time, determining the predicted exposure stop condition according to the DR feature index of the at least one frame of DR image data and the target DR feature index includes: determining the number of frames of the DR image data required to complete exposure according to the DR feature index of the DR image data corresponding to at least one of the preset time intervals and the target DR feature index; The predicted exposure time is determined according to the number of frames of the DR image data required to complete the exposure and the at least one preset time interval.

14. The method according to any one of claims 1 to 13, characterized in that: The step of reading the DR image data at a preset time interval and determining a DR feature index of at least one frame of the DR image data includes: Reading a frame of DR image data at the current moment, and determining a DR feature index of the frame of DR image data; or, According to the preset time interval, multiple frames of the DR image data are accumulated and read, and an accumulated DR feature index is determined according to the multiple frames of the DR image data accumulated and read.

15. An exposure control method for a DR imaging system, characterized in that: The method comprises: emitting X-rays to a target part of a target object so that the X-rays pass through the target part; receiving X-rays after passing through the target part, and generating digital X-ray photography DR image data according to the X-rays after passing through the target part; determining a DR feature index of the DR image data, wherein the DR feature index is determined based on an image feature extracted from the DR image data; When the DR characteristic index reaches a target DR characteristic index, the DR imaging system is controlled to stop exposure.

16. The method according to claim 15, characterized in that The determining of the DR feature index of the DR image data comprises: Determine a DR feature index of one frame of the DR image data; or determine the accumulated DR feature index of multiple frames of the DR image data.

17. The method according to claim 15 or 16, characterized in that Also includes: Based on the calibration operation of the target DR characteristic index, the target DR characteristic index is calibrated.

18. A DR imaging system, characterized in that: The DR imaging system includes: an X-ray source, a flat panel detector, a processor and a controller; 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, and generate DR image data according to the X-rays after passing through the target part; The processor is used to determine a DR feature index of the DR image data, wherein the DR feature index is determined based on an image feature extracted from the DR image data; The controller is used to control the DR imaging system to stop exposure when the DR characteristic index reaches a target DR characteristic index.

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