CN106214171B - Automatic exposure control method and device - Google Patents

Automatic exposure control method and device Download PDF

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CN106214171B
CN106214171B CN201610807883.6A CN201610807883A CN106214171B CN 106214171 B CN106214171 B CN 106214171B CN 201610807883 A CN201610807883 A CN 201610807883A CN 106214171 B CN106214171 B CN 106214171B
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exposure
voltage
gray value
equivalent thickness
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CN106214171A (en
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赵明宇
李焕文
李海春
赵晓洲
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Neusoft Medical Systems Co Ltd
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Abstract

The application provides an automatic exposure control method and device, wherein the method comprises the following steps: acquiring a desired gray value corresponding to an exposure part of a detected object, the equivalent thickness of the exposure part and a preset tube voltage kv; acquiring exposure cut-off voltage REF corresponding to the expected gray value when kv and the equivalent thickness are obtained according to a cut-off voltage relation model, wherein the cut-off voltage relation model is used for representing the relation among the image gray value, the equivalent thickness, the tube voltage kv and the REF; and sending the REF to a high-voltage generator so that the high-voltage generator controls exposure according to the REF. The application realizes different gray scale requirements for different exposure parts of the object to be detected.

Description

Automatic exposure control method and device
Technical Field
The present application relates to medical equipment technologies, and in particular, to an automatic exposure control method and apparatus.
Background
Digital Radiography (DR) equipment is widely used due to its advantages of small radiation dose, high image quality, and the like, and in order to obtain an ideal image quality, it is necessary to set appropriate exposure parameters for DR equipment. In the traditional mode, a doctor can determine the value of the exposure parameter of the equipment according to the characteristics of the patient such as the height and the weight of the patient and by combining the subjective experience of the doctor, but the mode of manually setting the exposure parameter completely depends on the subjective experience of the doctor, and the image gray scale and the image quality cannot meet the actual requirements due to the lack of scientific theoretical basis. In order to overcome the above-mentioned drawbacks of manual Exposure Control, AEC (Automatic Exposure Control) technology is currently applied to a DR apparatus to achieve the purpose of Automatic Exposure by controlling an Exposure deadline during Exposure.
The ionization chamber control method is one of the AEC parameter adjustment methods, and the principle of the method is that a high-voltage generator controls a bulb tube to emit rays, and the rays are received by a detector after irradiating a detected body to generate an exposure image; an ionization chamber is arranged between the detected body and the detector, the ionization chamber can sense the dose of rays penetrating through the detected body, the dose of the rays is converted into an electric signal and is fed back to the high-voltage generator, and the high-voltage generator stops outputting the high voltage when the electric signal is determined to reach a preset exposure cut-off voltage AEC REF (REF for short), namely, the exposure is stopped. It can be seen that the high voltage generator can automatically control when to stop the exposure based on REF, which is an important parameter for controlling the exposure dose, by adjusting which the magnitude of the exposure dose can be controlled, thereby affecting the gray value of the exposed image. In the related art, the corresponding REF may be set according to a desired gray level of an image to be generated, but the same desired gray level is generally used for different subjects, for example, different parts of human bodies of different body types, and different gray level requirements for image diagnosis of different parts cannot be satisfied.
Disclosure of Invention
In view of the above, the present application provides an automatic exposure control method and apparatus to achieve different gray scale requirements for different exposed portions of a subject.
Specifically, the method is realized through the following technical scheme:
in a first aspect, an automatic exposure control method is provided, the method including:
acquiring a desired gray value corresponding to an exposure part of a detected object, the equivalent thickness of the exposure part and a preset tube voltage kv;
acquiring exposure cut-off voltage REF corresponding to the expected gray value when kv and the equivalent thickness are obtained according to a cut-off voltage relation model, wherein the cut-off voltage relation model is used for representing the relation among the image gray value, the equivalent thickness, the tube voltage kv and the REF;
and sending the REF to a high-voltage generator so that the high-voltage generator controls exposure according to the REF.
In a second aspect, there is provided an automatic exposure control apparatus, the apparatus comprising:
the device comprises a parameter acquisition module, a parameter acquisition module and a parameter analysis module, wherein the parameter acquisition module is used for acquiring an expected gray value corresponding to an exposure part of a detected object, the equivalent thickness of the exposure part and a preset tube voltage kv;
the cut-off voltage determining module is used for acquiring the exposure cut-off voltage REF corresponding to the expected gray value when the kv and the equivalent thickness are detected according to a cut-off voltage relation model, and the cut-off voltage relation model is used for representing the relation among the image gray value, the equivalent thickness, the tube voltage kv and the REF;
and the data sending module is used for sending the REF to the high-voltage generator so that the high-voltage generator controls exposure according to the REF.
According to the automatic exposure control method and the automatic exposure control device, the expected gray values corresponding to different parts of the object to be detected are adopted to meet different image diagnosis requirements of different parts, and the relation between the REF and a plurality of variables is quantized by establishing a multivariable relation model related to the REF, so that the REF determined according to the relation model is more accurate, and different gray requirements of different exposed parts of the object to be detected are met.
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FIG. 1 is a schematic diagram of an AEC system shown in an exemplary embodiment of the present application;
FIG. 2 is a graph of REF versus GV illustrating an exemplary embodiment of the present application;
FIG. 3 is a flow chart illustrating a REF determination according to an exemplary embodiment of the present application;
FIG. 4 is a flow chart illustrating an automatic exposure control according to an exemplary embodiment of the present application;
FIG. 5 is a block diagram illustrating an application of a raster scanning system according to an exemplary embodiment of the present application;
FIG. 6 is a flow chart illustrating an automatic exposure control according to an exemplary embodiment of the present application;
FIG. 7 is a graph of a relationship under one mA condition as shown in an exemplary embodiment of the present application;
fig. 8 is a schematic structural diagram of an automatic exposure control apparatus according to an exemplary embodiment of the present application;
fig. 9 is a schematic structural diagram of an automatic exposure control apparatus according to an exemplary embodiment of the present application;
fig. 10 is a schematic diagram of a hardware configuration of an apparatus for controlling automatic exposure according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
AEC is a technology capable of automatically adjusting exposure parameters of DR equipment, and can overcome the defect of low precision of manual exposure control in the traditional mode. Fig. 1 illustrates a schematic diagram of an AEC system in a DR apparatus that can be applied to an ionization chamber control method (which is one of AEC control methods, and AEC control techniques, as well as other methods, such as a double exposure method).
As shown in fig. 1, the controller 11 may send a control parameter (e.g., REF) to the high voltage generator 12, and the high voltage generator 12 controls the emission of the exposure radiation (e.g., x-ray) of the bulb 13 according to the control parameter. After passing through the subject 14, the exposure radiation is received by the detector 15 to generate an exposure image. Wherein, an ionization chamber 16 is arranged between the object 14 and the detector 15, and the ionization chamber 16 can sense the dose of the rays transmitted through the object 14 and convert the dose of the rays into an electric signal to be fed back to the high voltage generator 12. When the high-voltage generator determines that the electric signal reaches a preset exposure cut-off voltage AEC REF (may be referred to as REF for short), the high-voltage generator stops outputting the high voltage, namely, stops the exposure. Therefore, the REF is an important parameter for controlling the exposure stop of the high voltage generator, and the exposure dose can be controlled through the REF, so as to achieve the purpose of controlling the gray value of the exposed image.
The automatic exposure control method provided in the embodiment of the present application may be applied to the architecture shown in fig. 1, and will be used to describe how the controller 11 determines REF, and after determining REF, may issue the parameter to the high voltage generator 12, and the high voltage generator 12 may stop exposure when the electrical signal fed back by the ionization chamber 16 reaches REF. In the embodiment of the present application, according to the image diagnosis requirements of different parts of the subject, the expected gray values corresponding to the different parts may be set according to empirical values, for example, a relationship table of the expected gray values corresponding to each part of the subject may be established in advance for table lookup in subsequent practical applications, for example, when in practical diagnosis, after a doctor selects an exposure part of a patient, the cut-off voltage relationship model may automatically calculate the corresponding REF value by calling the expected gray value according to the relationship table and set the REF value to the high voltage generator, and the purpose of the expected gray value is further achieved by controlling the exposure cut-off time. The automatic exposure control method of the embodiment of the present application will be used to describe how to achieve the above-mentioned desired gray scale value to meet different image gray scale requirements for different parts of the object. In the ionization chamber control method of the present example, the control of the image gray scale is realized by REF control, and therefore, the automatic exposure control method of the present example explains how to determine the REF used by the high voltage generator to control the exposure, so as to achieve the purpose of realizing the desired gray scale.
In order to make the parameter determination more accurate, the exposure control method provided in the embodiment of the present application includes two parts, namely "AEC calibration" and "AEC practical application", wherein in the "AEC calibration" stage, a parameter relationship model required to be used in the exposure control method is mainly established through sampling data, for example, the model may include a quantization relationship among multiple variables, such as exposure parameters, REF, image gray scale of an exposure image, and a conversion path between REF and related variables is established through the quantization relationship; the exposure parameters, REF and corresponding image gray variable relation selected by the model mainly depend on data collected during AEC calibration, and other kV conditions and corresponding variable relation curve relations are obtained through an interpolation algorithm. In the "AEC practical application" phase, some model input quantities related to the REF can be obtained according to the model, and finally the corresponding REF is determined according to the model. The "AEC calibration" and "AEC practical application" processes will be described separately as follows.
AEC calibration procedure
The calibration process is mainly used to establish the parameter relationship used in the subsequent application. In this example, a phantom is used in the calibration process, and the phantom is exposed under different exposure parameters to obtain sampling data.
Firstly, under the condition of certain same equivalent thickness and kv, exposure is carried out by converting REF to obtain an image gray value corresponding to the REF. For example, assuming that kv-1 is adopted, the equivalent thickness of the phantom is Pet-1, and under the condition (kv-1, Pet-1), the exposure is controlled by using a REF-1 value, the image gray value of the obtained exposure image is GV-1, i.e. a sampling point (REF-1, GV-1) is obtained; then, the exposure is controlled by changing the REF value to REF-2, the image gray value of the obtained exposure image is GV-2, and another sampling point (REF-2, GV-2) is obtained. In the same way, a plurality of sampling points can be obtained.
The sampling can obtain a plurality of REFs and corresponding sampling point data of GV under a certain condition (kv-1, Pet-1), for example, (REF-1, GV-1), (REF-2, GV-2), and a relationship curve of REF and image gray value under the same kv and Pet conditions can be fitted through the sampling point data. Similarly, keeping the kv condition unchanged, transforming the equivalent thickness to Pet-2, obtaining a plurality of REFs under the condition (kv-1, Pet-2) and corresponding GV sampling point data in the same way, and fitting a relation curve between REFs and image gray values under the condition (kv-1, Pet-2).
As can be seen in conjunction with fig. 2, fig. 2 illustrates different relationships of REF and GV at different equivalent thicknesses under kv-1 conditions. As shown in FIG. 2, when the equivalent thickness is Pet-1, the relationship between REF and GV is in accordance with the relationship curve 21, and when the equivalent thickness is Pet-2, the relationship between REF and GV is in accordance with the relationship curve 22, and REF and GV are approximately linear. For the middle thickness of the non-sampling point between Pet-1 and Pet-2, the relation curve of REF and GV at the middle thickness can be calculated by an interpolation algorithm. That is, fig. 2 illustrates the relationship curves of only two equivalent thicknesses, and actually, fig. 2 may include a plurality of consecutive (REF, GV) relationship curves of equivalent thicknesses.
Next, kv conditions may be changed, kv-1 may be changed to kv-2, exposure may be performed by changing REF under the conditions (kv-2, Pet-1) to obtain an image gradation value corresponding to the REF, data of a plurality of sampling points under the conditions, for example, (REF-3, GV-3), (REF-4, GV-4), may be acquired, and a relationship curve between REF and the image gradation value under the conditions (kv-2, Pet-1) may be fitted from the data of the sampling points. Similarly, different equivalent thicknesses, such as (kv-2, Pet-2), (kv-2, Pet-3), etc., can be converted under kv-2 conditions, and exposure is performed by converting REF under each condition as well, resulting in a plurality of (REF, GV) sampling point data. After fitting the data, a relationship curve similar to fig. 2 can be obtained, except that the relationship curve is a (REF, GV) relationship curve corresponding to different equivalent thicknesses Pet under kv-2. For the intermediate tube voltage of non-sampling points between different kV, for example, the intermediate kV condition between kV-1 and kV-2, the relationship curve of REF and GV under the intermediate kV condition can also be calculated approximately by interpolation algorithm, and at the same time, the correction must be performed with reference to the specific kV characteristic.
Table 1 below illustrates the correspondence between the four corresponding parameter values (kv, Pet, REF, GV) (for example, the graph shown in fig. 2 actually corresponds to the four parameters, except that the curve shows the correspondence between (REF, GV) in (kv, Pet)), and the correspondence between the parameters, that is, the cutoff voltage relationship model, may obtain the corresponding REF according to three of the parameters (kv, Pet, GV). Note that table 1 illustrates a partial correspondence relationship, and an arbitrary correspondence relationship (kv, Pet, REF, GV) can be obtained by interpolation. It can also be seen from table 1 that, under different kv and Pet, the same REF is used to obtain different image gray values GV.
Partial correspondence data in the model of Table 1
Figure BDA0001111073940000061
Figure BDA0001111073940000071
After obtaining the cutoff voltage relationship model, the corresponding relationship between the four parameters (kv, Pet, REF, GV) is obtained, and the relationship can be represented by a relationship curve similar to the example of fig. 2. In practical application, as long as kv and the equivalent thickness Pet used for exposure are determined, REF corresponding to the desired gray value GV can be obtained by combining the relationship curve according to the desired gray value of the exposure portion. How this cutoff voltage relationship model is applied will be described below.
Practical application process of AEC
This application process is a process of performing a formal scan to create an image of a subject, taking the subject as a patient as an example, and fig. 3 illustrates a process of determining REF by applying the model created above when exposing the patient, including:
in step 301, a desired gradation value corresponding to an exposure portion of a subject, an equivalent thickness of the exposure portion, and a preset tube voltage (i.e., bulb voltage) kv are acquired.
For example, a doctor can select a desired gradation value corresponding to an exposure site of a patient to be treated, and the controller can calculate an equivalent thickness (Pet) of the exposure site by an equivalent thickness algorithm. The doctor can also set the exposure parameters used for the exposure, such as tube voltage kv and milliampere-seconds mAs.
In step 302, an exposure cut-off voltage REF corresponding to the desired gray value at kv and equivalent thickness is obtained according to a cut-off voltage relationship model, which is used to represent a relationship between an image gray value, an equivalent thickness, a tube voltage kv, and REF.
In this step, REF may be obtained according to a relationship model established in the AEC calibration process, and the three parameters obtained in step 301, i.e., the desired gray value, the equivalent thickness, and kv, are used as inputs of the model, so that the output REF of the model may be obtained.
In step 303, the REF is sent to a high voltage generator, so that the high voltage generator controls exposure according to the REF.
In the automatic exposure control method of the embodiment, different parts of the object adopt corresponding different expected gray levels to meet different image diagnosis requirements of the different parts, and the relationship between the REF and a plurality of variables is quantified by establishing a multivariate relational model related to the REF, so that the REF determined according to the relational model is more accurate, and the method realizes different gray levels requirements of the different exposed parts of the object.
In one example, there may be a deviation from the equivalent thickness estimated from the subject exposure region determined by the doctor, which may lead to an error in the determination of REF, and therefore, this example may acquire the actual thickness of the exposure region of the subject and correct REF determined according to the cutoff voltage relationship model accordingly.
As shown in fig. 4, the flow of the automatic exposure control in one example includes a step of correcting REF according to the actual thickness of the exposure portion. As shown in fig. 4, may include:
in step 401, a desired gradation value corresponding to an exposure portion of a subject, an equivalent thickness of the exposure portion, and a preset tube voltage kv are acquired.
In step 402, the actual thickness of the exposed part measured by the raster scanning system is acquired.
In this example, the two sides of the chest film frame and the bed body may be pre-installed with a raster scanning system, as shown in fig. 5, which is a schematic structural diagram of the adopted raster scanning system, the raster scanning system is composed of a transmitter and a receiver, and light emitted by the transmitter is directly incident on the receiver to form the raster scanning system.
After the patient enters the raster scanning system, the doctor can start the thickness recognition function on the control machine, and the transmitter and the receiver of the raster scanning system are automatically erected (normally in a folded state). After the doctor confirms that the patient is normally positioned, the doctor can start the raster starting program and the scanning function through a confirmation key of the application software of the control machine. When the scanning is finished, the device can automatically feed the detected thickness value of the actual part of the patient back to the control machine. In addition, after the exposure is finished, a doctor can click the control machine application software to finish confirmation, and then the raster scanning system can be automatically retracted to finish the measurement.
In step 403, a thickness correction factor is obtained according to the equivalent thickness and the actual thickness.
In this step, the controller may convert the measured thickness value fed back by the raster scanning system into a PMMA (polymethyl methacrylate) equivalent thickness value, and compare the measured thickness value with the previously estimated PMMA equivalent thickness value to obtain the thickness correction factor α.
The thickness correction factor will be used for REF correction in subsequent steps, or the calculation step for obtaining the thickness correction factor may be performed later.
In step 404, an exposure cut-off voltage REF corresponding to the desired gray value at kv and the equivalent thickness is obtained according to a cut-off voltage relationship model.
Referring to the example of fig. 2, the (REF, GV) to be selected is determined in advance according to the estimated equivalent thickness of the exposure portion, and the REF corresponding to GV is determined according to the expected gray value GV of the final image.
In step 405, the REF value is adjusted according to the thickness correction factor to obtain a new cutoff voltage REF'.
For example, the REF value may be corrected based on the thickness correction factor α in the following manner, derived as follows:
suppose the estimated equivalent thickness is l1Converting the measured thickness value fed back by the raster scanning system into PMMA equivalent thickness of l2The thickness correction factor
Figure BDA0001111073940000091
Let an equivalent thickness of l1AEC exposure of human tissue at a patient site dose I0The dose of the ray reaching the flat plate through the human body is A, namely the dose of the ray is attenuated to A after the ray passes through the patient, the attenuation coefficient of human tissue is mu, the thickness correction factor is α, and according to the attenuation principle of the ray, the formula is as follows:
Figure BDA0001111073940000092
and (3) obtaining:
Figure BDA0001111073940000093
equivalent thickness
Figure BDA0001111073940000094
For the same reason, for an equivalent thickness of l2Subjecting human tissue to AEC exposure, having
Figure BDA0001111073940000095
Wherein, Io' adjusted value for patient entrance dose, i.e. patient entrance dose needs to be changed to IoCan satisfy the equivalent thickness variation of l2The same ideal gray value requirement is realized. And (3) obtaining:
Figure BDA0001111073940000096
equivalent thickness
Figure BDA0001111073940000097
Thickness correction factor
Figure BDA0001111073940000098
Figure BDA0001111073940000099
And (3) obtaining:
Figure BDA00011110739400000910
and then have
Figure BDA00011110739400000911
Corresponding to the equivalent thickness l1The exposure reference voltage is REF, REF' corresponds to an equivalent thickness of l2The cut-off voltage corresponding to the human tissue AEC during shooting,
Figure BDA0001111073940000101
Figure BDA0001111073940000102
the abbreviation is:
Figure BDA0001111073940000103
i.e. at an equivalent thickness of l2When the human tissue AEC is shot, the REF value needs to be corrected to REF' to realize the purpose of ideal gray scale.
In step 406, a new cutoff voltage REF 'is issued to the high voltage generator such that the high voltage generator controls exposure according to REF'.
The automatic exposure control method of the embodiment not only realizes different gray scale requirements on different exposure parts of the object to be detected, but also corrects the equivalent thickness through the raster scanning system, so that REF determination is more accurate, and the realization of the gray scale of the image can be more accurately controlled.
In another example, after the REF is determined according to the cut-off voltage relationship model, the image gray scale obtained by controlling exposure according to the REF may also have a deviation, which is not an expected gray scale value, and at this time, the REF may be adjusted and corrected according to the gray scale of the actual exposure image, so that the REF is more accurate. Fig. 6 illustrates an example of REF correction according to gray scale, which may be added to the flowchart of fig. 4 by steps 407 and 408.
In step 407, an image gradation value of an exposure image obtained by exposing the exposure portion is acquired.
In step 408, the exposure cutoff voltage REF stored in the high voltage generator is corrected as the cutoff reference voltage under the same condition next time according to the image gradation value and the desired gradation value of the exposed portion in the image and the cutoff voltage relationship model.
In the present step, when REF is corrected based on the gray scale deviation, the relationship curve of (REF, GV) may be used, for example, in the example shown in fig. 2, the relationship curve under a certain Pet is taken as an example, and it is assumed that the gray scale value GV is expected based on the relationship curvemThe corresponding REF is REFmBut according to REFmThe gray scale of the image obtained by exposure is GVnIs not desiredGV ofmThe compensation value of REF can be determined according to the inclination angle of the relation curve, and the compensation relation model can be
Figure BDA0001111073940000104
Wherein A is the inclination angle of the Pet2 relation curve (Pet2 is thicker human tissue equivalent thickness), which can be derived, and new reference voltage
Figure BDA0001111073940000111
In another example, the cut-off voltage relationship model in the embodiment of the present application is a relationship curve of GV and REF obtained under a certain fixed (kv, mA) condition, that is, if mA is sampled while remaining unchanged, in practical implementation, parameters set by a doctor may change, for example, the same kv and different mA are used, (for example, kv1 and mA1 when the model is established, and kv1 and mA2 are actually used), and such a current change may also cause a gray scale deviation, so in order to avoid the gray scale deviation caused by the current change, the present example may further correct REF, that is, adjust REF based on the REF determined by the (REF, GV) relationship curve.
In this example, a gray compensation model for representing a relationship curve between REF and an image gray value under a non-standard mA condition, which is mA corresponding to kv used in AEC calibration, may be established in advance.
For example, referring to fig. 7, in the model sampling, under the condition of not adding any PMMA load, the flat panel detector is blank-shot, and the REF exposure is changed to obtain GV under the conditions of mA1 and mA2, for example, the AEC REF voltage is set to 1V and 5V respectively, and after blank-shot exposure, the actual gray GV value is correspondingly obtained. As in the mA1 condition: (1V, GV1), (5V, GV2), and mA2 conditions: (1V, GV3), (5V, GV4), the above curve relationship can be fitted by an algorithm (blue is mA1 and red is mA 2).
When the gray scale compensation model is corrected according to the established gray scale compensation model, under the kv condition, the exposure is supposed to be performed according to (kv, mA1) according to the cut-off voltage relation model, and the cut-off voltage REF is adopted to obtain the expected gray scale value GV; however, when the doctor sets the exposure parameters, the parameters are set to (kv, mA2), and if the exposure is still controlled by REF, the gradation of the image actually obtained finally may be deviated. In this example, REF is adjusted according to the gray scale compensation model of fig. 7, and the REF obtained according to the cutoff voltage relationship model is corrected according to the acquired gray scale compensation model and the desired gray scale value.
Examples are as follows: and respectively acquiring an actual tube current (namely bulb tube current) mA2 adopted during exposure and a gray compensation model corresponding to a standard mA1 selected during AEC calibration, wherein the mA1 is mA corresponding to kv during AEC calibration. The compensation process is described in connection with the example of fig. 7:
the slope of the mA1 curve is: k is a radical of1＝(GV2-GV1)/(5-1)＝(GV2-GV1)/4........(1)
The mA1 curve corresponds to a gray rise angle α: α ═ α rc tan { (GV2-GV1)/4}. ·, (2)
In the same way, the mA2 curve gray scale rising angle β: β { (GV4-GV3)/4 { (3) is α rc tan { (GV4-GV3) }
Assuming that the mA2 condition is used, to achieve the same gray value GV, a compensated Δ AEC _ REF is needed:
ΔAEC_REF＝REF2-REF1......................(4)
REF2＝{1+4(GV-GV3)}/(GV4-GV3)....................(5)
REF1＝{1+4(GV-GV1)}/(GV2-GV1)......................(6)
and (3) subtracting the formula (5) from the formula (6) to obtain a compensation value delta AEC _ REF:
ΔAEC_REF＝{1+4(GV-GV3)}/(GV4-GV3)-{1+4(GV-GV1)}/(GV2-GV1)...(7)
the example establishes an accurate implementation relationship for corresponding gray scales under different mA conditions under the same kV condition, and adjusts REF through the relationship, so that the control of the image gray scale is further more accurate.
The exposure parameter adjusting method can be applied to AEC control executed by an ionization chamber control method, and a relational model between multiple variables related to REF and GV is established through sampling data in an AEC calibration process, so that the REF can be adjusted more accurately according to the quantization relation, and an expected image gray value can be realized more accurately; and the relationship model of this example includes the correspondence between arbitrary parameters determined by interpolation, and flexible and accurate REF control can be realized.
In order to implement the automatic exposure control method, the present application further provides an automatic exposure control apparatus, as shown in fig. 8, the apparatus may include: a parameter acquisition module 81, a cutoff voltage determination module 82, and a data transmission module 83.
A parameter obtaining module 81, configured to obtain a desired gray scale value corresponding to an exposure portion of a subject, an equivalent thickness of the exposure portion, and a preset tube voltage kv;
the cut-off voltage determining module 82 is configured to obtain an exposure cut-off voltage REF corresponding to the expected gray value when kv and the equivalent thickness are detected according to a cut-off voltage relationship model, where the cut-off voltage relationship model is used to represent a relationship between an image gray value, the equivalent thickness, and the tube voltage kv and REF;
and the data sending module 83 is configured to send the REF to the high-voltage generator, so that the high-voltage generator controls exposure according to the REF.
In one example, the cutoff voltage determination module 82 is further configured to: acquiring the actual thickness of the exposure part measured by a raster scanning system; converting the actual thickness into a polymethyl methacrylate (PMMA) equivalent thickness, and comparing the converted PMMA equivalent thickness with the estimated PMMA equivalent thickness of the exposure part to obtain a thickness correction factor; adjusting the REF value according to a thickness correction factor.
In one example, the cutoff voltage determination module 82 is further configured to: acquiring an image gray value of an exposure image obtained by exposing the exposure part; and correcting the exposure cut-off voltage REF according to the image gray value, the expected gray value and the cut-off voltage relation model.
In one example, as shown in fig. 9, the apparatus may further include: a model building module 84 for: during automatic exposure control AEC calibration, performing exposure by converting REF under the condition of the same equivalent thickness and kv to obtain an image gray value corresponding to the REF; fitting to obtain a relation curve of REF and image gray value under the equivalent thickness and kv condition; converting the equivalent thickness and kv conditions to respectively obtain the relationship curves of the REF and the image gray value under different equivalent thicknesses and kv conditions; and obtaining the cutoff voltage relation model among any image gray value, equivalent thickness, tube voltage kv and REF through an interpolation algorithm.
In one example, the cutoff voltage determination module 82 is further configured to: respectively obtaining a gray compensation model corresponding to an actual tube current mA adopted during exposure and a standard mA selected during AEC calibration, wherein the gray compensation model is used for representing a relation curve between REF and an image gray value under a non-standard mA condition, and the standard mA is mA correspondingly used by kv during AEC calibration; and correcting the REF obtained according to the cut-off voltage relation model according to the obtained gray compensation model and the expected gray value.
Referring to fig. 10, the present disclosure also provides an apparatus for controlling automatic exposure, corresponding to the above method. As shown in fig. 10, the apparatus may include a processor 1001 and a machine-readable storage medium 1002, wherein the processor 1001 and the machine-readable storage medium 1002 are typically connected to each other via an internal bus 1003. In other possible implementations, the apparatus may also include an external interface 1004 to enable communication with other devices or components. Further, the machine-readable storage medium 1002 has stored thereon a control logic 1005 for controlling auto exposure, and the logic module functionally divided by the control logic 1005 may be the structure of the auto exposure control apparatus shown in fig. 8 or 9.
In different examples, the machine-readable storage medium 1002 may be: a RAM (random access memory), a volatile memory, a non-volatile memory, a flash memory, a storage drive (e.g., a hard drive), a solid state drive, any type of storage disk (e.g., an optical disk, a dvd, etc.), or similar storage medium, or a combination thereof.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims (8)

1. An automatic exposure control method, characterized in that the method comprises:
acquiring a desired gray value corresponding to an exposure part of a detected object, the equivalent thickness of the exposure part and a preset tube voltage kv;
acquiring exposure cut-off voltage REF corresponding to the expected gray value when kv and the equivalent thickness are obtained according to a cut-off voltage relation model, wherein the cut-off voltage relation model is used for representing the relation among the image gray value, the equivalent thickness, the tube voltage kv and the REF;
sending the REF to a high-voltage generator so that the high-voltage generator controls exposure according to the REF;
the method further comprises the following steps:
during automatic exposure control AEC calibration, performing exposure by converting REF under the condition of the same equivalent thickness and kv to obtain an image gray value corresponding to the REF;
fitting to obtain a relation curve of REF and image gray value under the equivalent thickness and kv condition;
converting the equivalent thickness and kv conditions to respectively obtain the relationship curves of the REF and the image gray value under different equivalent thicknesses and kv conditions;
and obtaining the cutoff voltage relation model among any image gray value, equivalent thickness, tube voltage kv and REF through an interpolation algorithm.
2. The method of claim 1, further comprising:
acquiring the actual thickness of the exposure part measured by a raster scanning system;
converting the actual thickness into a polymethyl methacrylate (PMMA) equivalent thickness, and comparing the converted PMMA equivalent thickness with the estimated PMMA equivalent thickness of the exposure part to obtain a thickness correction factor;
adjusting the REF value according to the thickness correction factor.
3. The method of claim 1, further comprising:
acquiring an image gray value of an exposure image obtained by exposing the exposure part;
and correcting the exposure cut-off voltage REF according to the image gray value and the expected gray value of the exposure image and the cut-off voltage relation model.
4. The method of claim 1, further comprising:
respectively obtaining a gray compensation model corresponding to an actual tube current mA adopted during exposure and a standard mA selected during AEC calibration, wherein the gray compensation model is used for representing a relation curve between REF and an image gray value under a non-standard mA condition, and the standard mA is mA correspondingly used by kv during AEC calibration;
and correcting the REF obtained according to the cut-off voltage relation model according to the obtained gray compensation model and the expected gray value.
5. An automatic exposure control apparatus, characterized in that the apparatus comprises:
the device comprises a parameter acquisition module, a parameter acquisition module and a parameter analysis module, wherein the parameter acquisition module is used for acquiring an expected gray value corresponding to an exposure part of a detected object, the equivalent thickness of the exposure part and a preset tube voltage kv;
the cut-off voltage determining module is used for acquiring the exposure cut-off voltage REF corresponding to the expected gray value when the kv and the equivalent thickness are detected according to a cut-off voltage relation model, and the cut-off voltage relation model is used for representing the relation among the image gray value, the equivalent thickness, the tube voltage kv and the REF;
the data sending module is used for sending the REF to the high-voltage generator so that the high-voltage generator controls exposure according to the REF;
the device further comprises:
a model building module to: during automatic exposure control AEC calibration, performing exposure by converting REF under the condition of the same equivalent thickness and kv to obtain an image gray value corresponding to the REF; fitting to obtain a relation curve of REF and image gray value under the equivalent thickness and kv condition; converting the equivalent thickness and kv conditions to respectively obtain the relationship curves of the REF and the image gray value under different equivalent thicknesses and kv conditions; and obtaining the cutoff voltage relation model among any image gray value, equivalent thickness, tube voltage kv and REF through an interpolation algorithm.
6. The apparatus of claim 5,
the cutoff voltage determination module is further configured to: acquiring the actual thickness of the exposure part measured by a raster scanning system; converting the actual thickness into a polymethyl methacrylate (PMMA) equivalent thickness, and comparing the converted PMMA equivalent thickness with the estimated PMMA equivalent thickness of the exposure part to obtain a thickness correction factor; adjusting the REF value according to the thickness correction factor.
7. The apparatus of claim 5,
the cutoff voltage determination module is further configured to: acquiring an image gray value of an exposure image obtained by exposing the exposure part; and correcting the exposure cut-off voltage REF according to the image gray value and the expected gray value of the exposure image and the cut-off voltage relation model.
8. The apparatus of claim 5,
the cutoff voltage determination module is further configured to: respectively obtaining a gray compensation model corresponding to an actual tube current mA adopted during exposure and a standard mA selected during AEC calibration, wherein the gray compensation model is used for representing a relation curve between REF and an image gray value under a non-standard mA condition, and the standard mA is mA correspondingly used by kv during AEC calibration; and correcting the REF obtained according to the cut-off voltage relation model according to the obtained gray compensation model and the expected gray value.
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