CN117911306A - Method, apparatus, storage medium and program product for determining exposure parameters - Google Patents
Method, apparatus, storage medium and program product for determining exposure parameters Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, a storage medium and a program product for determining exposure parameters by X-ray imaging. The method comprises the following steps: determining an X-ray imaging protocol and a thickness value of an imaging target; inputting the thickness value into an exposure parameter prediction model corresponding to the X-ray imaging protocol, wherein the exposure parameter prediction model is trained based on first training data, and the first training data comprises an exposure parameter historical value in a historical exposure operation based on the X-ray imaging protocol and a historical thickness value in the historical exposure operation; an exposure parameter prediction value determined based on the thickness value is received from the exposure parameter prediction model. According to the embodiment of the invention, the exposure parameters are automatically determined according to the thickness value of the imaging target, so that the exposure accuracy is improved, and the manual difficulty is reduced. The embodiment of the invention can determine the exposure parameters in a general mode irrelevant to the adjustment operation or a special mode relevant to the adjustment operation, and also realizes automatic determination of the thickness value.
Description
Technical Field
The present invention relates to the field of medical imaging technology, and in particular, to a method, an apparatus storage medium, and a program product for determining exposure parameters by X-ray imaging.
Background
X-rays are electromagnetic radiation having wavelengths between ultraviolet and gamma rays. X-rays have penetrability and have different penetrability to substances with different densities. In medicine, human organs and bones are generally projected with X-rays to form medical images. X-ray imaging systems typically include an X-ray generation assembly, a chest-Wall-Stand (BWS) assembly, a table assembly, a cassette assembly containing a flat panel detector, and a remotely located control host, among others. The X-ray generating assembly emits X-rays transmitted through the irradiation imaging target by using high voltage provided by the high voltage generator, and forms medical image information of the imaging target on the flat panel detector. The flat panel detector transmits the medical image information to the control host. The imaging subject may stand near the chest frame assembly or lie on the couch assembly to receive X-ray images of the skull, chest, abdomen, joints, etc., respectively.
In X-ray imaging, X-ray exposure parameters (e.g., bulb voltage, bulb current, exposure time, etc.) have a large impact on X-ray image quality. Currently, the X-ray exposure parameters are set by themselves mainly according to personal experiences of technicians, and the defects of difficult implementation are caused.
Disclosure of Invention
The embodiment of the invention provides a method, a device storage medium and a program product for determining exposure parameters by X-ray imaging.
The technical scheme of the embodiment of the invention comprises the following steps:
a method of determining exposure parameters in X-ray imaging, comprising:
Determining an X-ray imaging protocol and a thickness value of an imaging target;
Inputting the thickness value into an exposure parameter prediction model corresponding to the X-ray imaging protocol, wherein the exposure parameter prediction model is trained based on first training data comprising an exposure parameter historical value in a historical exposure operation based on the X-ray imaging protocol and a historical thickness value in the historical exposure operation;
An exposure parameter prediction value determined based on the thickness value is received from the exposure parameter prediction model.
Therefore, the embodiment of the invention automatically determines the exposure parameters according to the thickness value of the imaging target, improves the exposure accuracy and reduces the manual difficulty. In addition, in the process of automatically determining the exposure parameters, the relevance between the thickness value of the imaging target and the exposure parameters is considered, so that the parameter accuracy is further improved.
In an exemplary embodiment, further comprising:
establishing a first artificial neural network model;
Inputting the first training data into the first artificial neural network model;
Training the first artificial neural network model into the exposure parameter prediction model by using the first training data.
Therefore, the implementation difficulty is also reduced by introducing the artificial neural network into the exposure parameter prediction process.
In an exemplary embodiment, further comprising:
inputting the exposure parameter predicted value into an exposure parameter adjustment model corresponding to the exposure parameter predicted model, wherein the exposure parameter adjustment model is trained based on second training data, the second training data comprising an exposure parameter history predicted value historically output by the exposure parameter predicted model and an exposure parameter history adjustment value corresponding to the exposure parameter history predicted value;
receiving an exposure parameter adjustment value determined based on the exposure parameter prediction value from the exposure parameter adjustment model;
an exposure operation is performed on the imaging target based on the exposure parameter adjustment value to generate an X-ray image.
Therefore, the embodiment of the invention adjusts the predicted value of the exposure parameter through the historical adjustment value of the exposure parameter, introduces the adjustment preference habit, realizes the determination of the exposure parameter in a mode related to the adjustment operation, and is suitable for the customization situation.
In an exemplary embodiment, further comprising:
Establishing a second artificial neural network model;
Inputting the second training data into the second artificial neural network model;
and training the second artificial neural network model into the exposure parameter adjustment model by using the second training data.
Therefore, the implementation difficulty is also reduced by introducing the artificial neural network into the exposure parameter adjustment process.
In an exemplary embodiment, the first training data is derived from log data of a plurality of X-ray imaging units;
The second training data is derived from log data of the same X-ray imaging unit or the second training data is derived from log data of the same X-ray imaging operation subject in the same X-ray imaging unit.
It can be seen that the first training data is derived from a plurality of X-ray imaging units, and the trained exposure parameter prediction model is a widely applicable general model. The second training data are derived from the same X-ray imaging unit or the same X-ray imaging operation main body, and the trained exposure parameter adjustment model is suitable for the same X-ray imaging unit or the same X-ray imaging operation main body and belongs to the custom model.
In an exemplary embodiment, the determining the thickness value of the imaging target includes:
Determining a distance of an X-ray source from a surface point of the imaging target based on a three-dimensional image containing the imaging target;
determining a thickness value of the imaging target at a surface point of the imaging target based on a distance between the X-ray source and an imaging surface, a distance between a contact plate and a detector, and a distance between the X-ray source and the surface point of the imaging target;
determining a thickness value of the imaging target based on the thickness value of the imaging target at the surface point;
wherein the thickness value of the imaging target comprises: a minimum thickness value of the imaging target or an average thickness value of the imaging target.
Therefore, the embodiment of the invention also realizes automatic determination of the thickness value of the imaging target, and omits the complicated workload of manually measuring the thickness value of the imaging target.
An apparatus for determining exposure parameters in X-ray imaging, comprising:
The determining module is used for determining the thickness value of the X-ray imaging protocol and the imaging target;
A first input module for inputting the thickness value into an exposure parameter prediction model corresponding to the X-ray imaging protocol, wherein the exposure parameter prediction model is trained based on first training data, the first training data including an exposure parameter historical value in a historical exposure operation based on the X-ray imaging protocol and a historical thickness value in the historical exposure operation;
and the first receiving module is used for receiving the exposure parameter predicted value determined based on the thickness value from the exposure parameter predicted model.
Therefore, the embodiment of the invention automatically determines the exposure parameters according to the thickness value of the imaging target, improves the exposure accuracy and reduces the manual difficulty. In addition, in the process of automatically determining the exposure parameters, the relevance between the thickness value of the imaging target and the exposure parameters is considered, so that the parameter accuracy is further improved.
In an exemplary embodiment, further comprising:
The first training module is used for establishing a first artificial neural network model; inputting the first training data into the first artificial neural network model; training the first artificial neural network model into the exposure parameter prediction model by using the first training data.
Therefore, the implementation difficulty is also reduced by introducing the artificial neural network into the exposure parameter prediction process.
In an exemplary embodiment, further comprising:
A second input module for inputting the exposure parameter predicted value into an exposure parameter adjustment model corresponding to the exposure parameter predicted model, wherein the exposure parameter adjustment model is trained based on second training data, the second training data comprising an exposure parameter history predicted value historically output by the exposure parameter predicted model and an exposure parameter history adjustment value corresponding to the exposure parameter history predicted value;
A second receiving module for receiving an exposure parameter adjustment value determined based on the exposure parameter prediction value from the exposure parameter adjustment model;
And the exposure module is used for executing exposure operation on the imaging target based on the exposure parameter adjustment value so as to generate an X-ray image.
Therefore, the embodiment of the invention adjusts the predicted value of the exposure parameter through the historical adjustment value of the exposure parameter, introduces the adjustment preference habit, realizes the determination of the exposure parameter in a mode related to the adjustment operation, and is suitable for the customization situation.
In an exemplary embodiment, further comprising:
The second training module is used for establishing a second artificial neural network model; inputting the second training data into the second artificial neural network model; and training the second artificial neural network model into the exposure parameter adjustment model by using the second training data.
Therefore, the implementation difficulty is also reduced by introducing the artificial neural network into the exposure parameter adjustment process.
In an exemplary embodiment, the determining module is configured to determine a distance of an X-ray source from a surface point of the imaging target based on a three-dimensional image including the imaging target; determining a thickness value of the imaging target at a surface point of the imaging target based on a distance between the X-ray source and an imaging surface, a distance between a contact plate and a detector, and a distance between the X-ray source and the surface point of the imaging target; determining a thickness value of the imaging target based on the thickness value of the imaging target at the surface point; wherein the thickness value of the imaging target comprises: a minimum thickness value of the imaging target or an average thickness value of the imaging target.
It can be seen that the first training data is derived from a plurality of X-ray imaging units, and the trained exposure parameter prediction model is a widely applicable general model. The second training data are derived from the same X-ray imaging unit or the same X-ray imaging operation main body, and the trained exposure parameter adjustment model is suitable for the same X-ray imaging unit or the same X-ray imaging operation main body and belongs to the custom model.
An apparatus for determining exposure parameters in X-ray imaging comprising a processor and a memory;
the memory has stored therein an application executable by the processor for causing the processor to perform the method of determining exposure parameters in X-ray imaging as described in any of the above.
A computer readable storage medium having stored therein computer readable instructions which when executed by a processor implement a method of determining exposure parameters in X-ray imaging as described in any of the above.
A computer program product comprising a computer program which, when executed by a processor, implements a method of determining exposure parameters in X-ray imaging as defined in any one of the above.
Drawings
Fig. 1 is a flow chart of a method of determining exposure parameters in X-ray imaging according to an embodiment of the invention.
Fig. 2 is an exemplary process diagram for determining thickness values of an imaging target in X-ray imaging according to an embodiment of the present invention.
FIG. 3 is an exemplary process diagram for determining exposure parameters in X-ray imaging in accordance with an embodiment of the present invention.
Fig. 4 is an exemplary schematic diagram of a human-machine interaction process for X-ray imaging in accordance with an embodiment of the present invention.
Fig. 5 is a block diagram of an apparatus for determining exposure parameters in X-ray imaging according to an embodiment of the present invention.
Fig. 6 is a block diagram of an apparatus for determining exposure parameters in X-ray imaging having a memory-processor architecture according to an embodiment of the invention.
Wherein, the reference numerals are as follows:
Detailed Description
In order to make the technical scheme and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description is intended to illustrate the invention and is not intended to limit the scope of the invention.
For simplicity and clarity of description, the following description sets forth aspects of the invention by describing several exemplary embodiments. Numerous details in the embodiments are provided solely to aid in the understanding of the invention. It will be apparent, however, that the embodiments of the invention may be practiced without limitation to these specific details. Some embodiments are not described in detail in order to avoid unnecessarily obscuring aspects of the present invention, but rather only to present a framework. Hereinafter, "comprising" means "including but not limited to", "according to … …" means "according to at least … …, but not limited to only … …". The term "a" or "an" is used herein to refer to a number of components, either one or more, or at least one, unless otherwise specified.
In view of the defect that the X-ray exposure parameters are set by the personnel experience of technicians in the prior art, the embodiment of the invention considers the relevance between the thickness value of the imaging target and the exposure parameters (generally, the larger the thickness value of the imaging target is, the larger the exposure parameters are), and the exposure parameters are automatically determined according to the thickness value of the imaging target, so that the exposure accuracy is improved, and the manual difficulty is reduced.
Fig. 1 is a flow chart of a method of determining exposure parameters in X-ray imaging according to an embodiment of the invention. Preferably, the method of FIG. 1 may be performed by a controller. The controller may be implemented as a control host integrated into the X-ray imaging system, or as a control unit separate from the control host.
As shown in fig. 1, the method 100 includes:
step 101: an X-ray imaging protocol and a thickness value of an imaged object are determined.
The imaging target is a target that needs to be subjected to X-ray imaging. The imaging target may be a living or inanimate object, and the specific characteristics of the imaging target are not limited by the embodiments of the present invention. An X-ray imaging protocol is a specific protocol employed in performing X-ray imaging of an imaging subject, such as the tissue organ protocol (OGP). The X-ray imaging protocol may be determined based on a user selection operation in the human-machine interaction interface.
In an alternative embodiment, the thickness value of the imaging target may be measured manually by a staff member using a ruler. Preferably, in consideration of the defects of manually measuring the imaging target thickness value by using the ruler, the embodiment of the invention also provides a technical scheme for automatically determining the imaging target thickness value, and the imaging target thickness value is automatically determined based on the three-dimensional image of the imaging object, so that the complicated workload of manually measuring the thickness value is saved.
The imaging target can be photographed by the photographing assembly to obtain a three-dimensional image of the imaging target, and the three-dimensional image of the imaging target can be obtained from a storage medium (such as a cloud or a local database), wherein the three-dimensional image is obtained by photographing a person to be tested by the photographing assembly. After the imaging assembly acquires the three-dimensional image of the imaging target, the three-dimensional image can be sent to a controller executing the flow of fig. 1 via a wired interface or a wireless interface. Preferably, the wired interface comprises at least one of: universal serial bus interfaces, controller area network interfaces, serial ports, and the like; the wireless interface includes at least one of: infrared interfaces, near field communication interfaces, bluetooth interfaces, zigbee interfaces, wireless broadband interfaces, and the like.
In an exemplary embodiment, the specific step of determining the thickness value of the imaging target includes: step (1): determining a distance (Source to Object Surface Distance, SOSD) of the X-ray source from a surface point of the imaging target based on the three-dimensional image containing the imaging target; step (2): determining a thickness value of the imaging target at a surface point based on a distance (source to IMAGE DISTANCE, SID) between the X-ray source and the imaging surface, a distance (Table to Detector Distance, TDD) between the contact plate and the detector, and a distance of the X-ray source from the surface point of the imaging target; step (3): determining a thickness value of the imaging target based on the thickness value of the imaging target at the surface point; wherein the thickness values of the imaging target include: a minimum thickness value of the imaging target or an average thickness value of the imaging target, etc.
Here, the contact plate is a plate contacted by a subject in X-ray applications. The contact plate can isolate the subject from the imaging surface. The contact plate generally has the following meaning:
(1) When the X-ray imaging system works in the examination bed mode, the contact plate is the bed plate of the examination bed.
(2) When the X-ray imaging system works in a chest stand mode under the full spine imaging protocol, the contact plate is a supporting plate for assisting a person to be tested to stand.
(3) When the X-ray imaging system is operated in a chest stand mode under a non-full spine imaging protocol (such as a chest imaging protocol, a knee imaging protocol), the contact plate is the panel of the cassette assembly into which the flat panel detector is inserted.
(4) When the X-ray imaging system works in a free exposure mode (namely, a to-be-detected person directly contacts the flat panel detector), the contact plate is a panel contacted by the flat panel detector and the to-be-detected person.
The distance between the X-ray source and the imaging plane and the distance between the contact plate and the detector both correspond to the X-ray imaging protocol. The distance between the X-ray source and the imaging plane, and the distance between the contact plate and the detector are known values when the X-ray imaging protocol is determined.
Fig. 2 is an exemplary process diagram for determining thickness values of an imaging target in X-ray imaging according to an embodiment of the present invention. In fig. 2, an exemplary process for determining thickness values of an imaging target is described using full spine imaging as an example.
The subject 75 stands at the support plate 76. For example, the left or right shoulder is a certain surface point C of the full spine imaging. The distance between the X-ray source in X-ray generation assembly 77 (typically located in an X-ray tube) and the imaging plane of the flat panel detector in cassette assembly 78 is SID; the distance between the support plate 76 and the flat panel detector in the cassette assembly 78 is TDD; the distance TOD between the spinal reference line 79 and the support plate 76 is the thickness value of the imaging target.
The X-ray source is located at a distance SOSDc from the surface point C, wherein: SOSDc = R x Ip + T. Wherein: r is a rotation matrix between an imaging component coordinate system and an X-ray tube coordinate system for imaging the subject 75 to form a three-dimensional picture; ip is a transformation matrix between the coordinate system of the camera component and the coordinate system of the X-ray tube; t is the depth value of the C point (T can be determined based on the three-dimensional image).
Then, a thickness value TODc at surface point C may be determined based on SOSDc, wherein: TODc = SID-TDD-SOSDc.
Similarly, thickness values at each surface point in the full spine imaging may be calculated. In one embodiment, the minimum value is selected from the thickness values at all the surface points as the thickness value of the imaging target in step 101. In one embodiment, an average thickness value is calculated based on the thickness values at all the surface points as the thickness value of the imaging target in step 101.
While the above exemplary descriptions of typical examples of thickness values of an imaging target, those skilled in the art will recognize that such descriptions are merely exemplary and are not intended to limit the scope of embodiments of the present invention.
Step 102: the thickness value is input into an exposure parameter prediction model corresponding to the X-ray imaging protocol, wherein the exposure parameter prediction model is trained based on first training data, and the first training data comprises exposure parameter historical values in historical exposure operation based on the X-ray imaging protocol and historical thickness values in the historical exposure operation.
It can be seen that the correlation between the thickness value of the imaging target and the exposure parameter is included in the exposure parameter prediction model trained by using the exposure parameter history value in the history exposure operation and the history thickness value in the history exposure operation. Thus, the exposure parameter prediction model can improve the exposure parameter accuracy. The exposure parameters may include: at least one of bulb voltage, bulb current, exposure time, exposure dose, product of bulb current and exposure time, and exposure density.
In an exemplary embodiment, the method 100 further comprises: establishing a first artificial neural network model; inputting the first training data into a first artificial neural network model; the first artificial neural network model is trained as an exposure parameter prediction model using the first training data. Therefore, the implementation difficulty is also reduced by introducing the artificial neural network into the exposure parameter prediction process.
Step 103: an exposure parameter prediction value determined based on the thickness value is received from the exposure parameter prediction model.
Here, the exposure parameter prediction model predicts an exposure parameter value based on the input thickness value, that is, an exposure parameter predicted value. An exposure operation may be performed on the imaging target based on the exposure parameter predictors to generate an X-ray image.
Therefore, the embodiment of the invention outputs the exposure parameter predicted value through the exposure parameter predicted model, and the exposure parameter predicted value can be directly used for executing the exposure operation on the imaging target, thereby realizing the determination of the exposure parameter in a general mode irrelevant to the adjustment operation and improving the exposure efficiency.
The automatic adjustment of the predicted exposure parameters may be further based on the preferences of the technician, considering that the technician will typically adjust the automatically determined predicted exposure parameters according to personal habits.
In an exemplary embodiment, the method 100 further comprises: inputting the exposure parameter predicted value into an exposure parameter adjustment model corresponding to the exposure parameter predicted model, wherein the exposure parameter adjustment model is trained based on second training data, and the second training data comprises exposure parameter historical predicted values which are output by the exposure parameter predicted model in history and exposure parameter historical adjustment values corresponding to the exposure parameter historical predicted values; receiving an exposure parameter adjustment value determined based on the exposure parameter prediction value from the exposure parameter adjustment model; an exposure operation is performed on the imaging target based on the exposure parameter adjustment value to generate an X-ray image. Here, the meaning of the exposure parameter history prediction value is: the exposure parameter prediction model historically outputs a predicted value for the exposure parameter; the meaning of the exposure parameter history adjustment value is: historically (typically, manually by a technician) adjusting the exposure parameter historical predicted values to actual values of the exposure parameters; the meaning of the exposure parameter adjustment value is: and adjusting the actual value of the exposure parameter after the exposure parameter predicted value input into the exposure parameter adjustment model. Therefore, the exposure parameter adjustment value output by the exposure parameter adjustment model can be directly applied to the exposure operation as the actual value of the adjusted exposure parameter.
Therefore, the trained exposure parameter adjustment model which is output by the exposure parameter prediction model and is provided with the exposure parameter history prediction value and the exposure parameter history adjustment value (usually manually adjusted by a technician) corresponding to the exposure parameter history prediction value comprises the correlation between the exposure parameter history prediction value and the exposure parameter history adjustment value, so that the adjustment preference of the technician is reflected, and the accuracy of the exposure parameter can be further improved.
Therefore, the embodiment of the invention outputs the exposure parameter adjustment value through the exposure parameter prediction model and the exposure parameter adjustment model, and the exposure parameter adjustment value can be directly used for executing exposure operation on the imaging target, and also shows the adjustment preference of a technician, thereby realizing the determination of the exposure parameter in a special mode related to the adjustment operation and improving the accuracy of the exposure parameter.
In an exemplary embodiment, the method 100 further comprises: establishing a second artificial neural network model; inputting the second training data into a second artificial neural network model; training the second artificial neural network model into an exposure parameter adjustment model by using the second training data. Therefore, the implementation difficulty is also reduced by introducing the artificial neural network into the exposure parameter adjustment process.
The first training data and the second training data are described in detail below. The X-ray imaging unit may be a specific medical facility, such as a hospital or physical examination facility. The X-ray imaging operation subject may be an individual, such as a technician, who operates the X-ray imaging system within the X-ray imaging unit.
In the first case: the first training data is derived from log data of a plurality of X-ray imaging units, and the second training data is derived from log data of the same X-ray imaging unit.
In the first case, the exposure parameter prediction model trained based on the first training data is a general model widely applicable to each X-ray imaging unit; the exposure parameter adjustment model trained based on the second training data is a custom model that is adapted for the same X-ray imaging unit.
Examples: the first training data is derived from log data of hospital a, hospital B, and hospital C. The first training data specifically comprises: (1) Exposure parameter history values in the history exposure operation of the hospital a, and history thickness values in the history exposure operation of the hospital a; (2) Exposure parameter history values in the history exposure operation of hospital B, and history thickness values in the history exposure operation of hospital B; (3) Exposure parameter history values in the history exposure operation of hospital C, and history thickness values in the history exposure operation of hospital C. The exposure parameter prediction model trained based on the first training data is a general model widely applicable to various hospitals (e.g., hospitals a to C that provide the first training data, and hospital D that do not provide the first training data, etc.).
After the exposure parameter prediction model is issued to each hospital, each hospital can determine the exposure parameter prediction value in each X-ray imaging operation by using the exposure parameter prediction model.
In addition, the second training data of the hospital A can be utilized to adjust the exposure parameter prediction model issued to the hospital A. The second training data of hospital a specifically includes: the hospital a uses the exposure parameter history predicted value and the exposure parameter history adjustment value corresponding to the exposure parameter history predicted value that the exposure parameter prediction model historically output (after the technician of the hospital a observes the exposure parameter history predicted value, manually adjusts the exposure parameter history predicted value to the exposure parameter history adjustment value). The exposure parameter adjustment model trained based on the second training data of hospital a is a custom model suitable for hospital a. The output of the exposure parameter prediction model issued to the hospital A is connected to the input of the customized model applicable to the hospital A, and the exposure parameter prediction model issued to the hospital A and the customized model of the hospital A can be connected, so that an adjusted exposure parameter prediction model applicable to the hospital A is formed.
Similarly, the second training data of hospital C may be used to adjust the exposure parameter prediction model issued to hospital C. The second training data of hospital C includes: the hospital C uses the exposure parameter history predicted value and the exposure parameter history adjustment value corresponding to the exposure parameter history predicted value that the exposure parameter prediction model historically output (after the technician of the hospital C observes the exposure parameter history predicted value, the exposure parameter history predicted value is manually adjusted to the exposure parameter history adjustment value). The exposure parameter adjustment model trained based on the second training data of hospital C is a custom model suitable for hospital C. The output of the exposure parameter prediction model issued to the hospital C is connected to the input of the customized model applicable to the hospital C, and the exposure parameter prediction model issued to the hospital C and the customized model of the hospital C can be connected, so that an adjusted exposure parameter prediction model applicable to the hospital C is formed.
In the second case: the first training data is derived from log data of a plurality of X-ray imaging units, and the second training data is derived from log data of the same X-ray imaging operation subject in the same X-ray imaging unit.
In the second case, the exposure parameter prediction model trained based on the first training data is a generic model widely applicable to each X-ray imaging unit; the exposure parameter adjustment model trained based on the second training data is a custom model applicable to the same X-ray imaging operation subject in the same X-ray imaging unit.
Examples: the first training data is derived from log data of hospital a, hospital B, and hospital C. The first training data specifically comprises: (1) Exposure parameter history values in the history exposure operation of the hospital a, and history thickness values in the history exposure operation of the hospital a; (2) Exposure parameter history values in the history exposure operation of hospital B, and history thickness values in the history exposure operation of hospital B; (3) Exposure parameter history values in the history exposure operation of hospital C, and history thickness values in the history exposure operation of hospital C. The exposure parameter prediction model trained based on the first training data is a general model widely applicable to various hospitals (e.g., hospitals a to C that provide the first training data, and hospital D that do not provide the first training data, etc.).
After the exposure parameter prediction model is issued to each hospital, each hospital can determine an exposure parameter prediction value in the X-ray imaging operation of each hospital by using the exposure parameter prediction model.
In addition, the exposure parameter prediction model issued to hospital a may be adjusted using the second training data of the technician in hospital a. The second training data for technician Zhang three includes: the technician uses the exposure parameter prediction model to historically output an exposure parameter history prediction value, and the technician Zhang Sanzhen executes an adjusted exposure parameter history adjustment value on the exposure parameter history prediction value (the technician manually adjusts the exposure parameter history prediction value to an exposure parameter history adjustment value after viewing the exposure parameter history prediction value). The exposure parameter adjustment model trained based on the second training data of the third technician is a custom model suitable for the third technician. The output of the exposure parameter prediction model issued to the hospital a is connected to the input of the custom model applicable to the technician's third, and the exposure parameter prediction model issued to the hospital a and the custom model applicable to the technician's third can be connected to form an adjusted exposure parameter prediction model applicable to the technician's third in the hospital a.
Similarly, the exposure parameter prediction model issued to hospital C may be adjusted using the second training data of technician Li IV in hospital C. The second training data for technician Li IV includes: the technician side uses the exposure parameter prediction model to historically output an exposure parameter history prediction value, and the technician side executes an exposure parameter history adjustment value adjusted for the exposure parameter history prediction value (the technician side manually adjusts the exposure parameter history prediction value to an exposure parameter history adjustment value after observing the exposure parameter history prediction value). The exposure parameter adjustment model trained based on the second training data of the fourth technician is a custom model suitable for the fourth technician. The output of the exposure parameter prediction model issued to the hospital C is connected to the input of the custom model adapted to the fourth technician, and the exposure parameter prediction model issued to the hospital C and the custom model of the fourth technician may be connected to form an adjusted exposure parameter prediction model adapted to the fourth technician.
FIG. 3 is an exemplary process diagram for determining exposure parameters in X-ray imaging in accordance with an embodiment of the present invention.
The technicians 10 at the respective hospitals perform the imaging protocol selection process 11 through the man-machine interaction interface on the control host to select the imaging protocol, respectively. The control host exhibits initial values (typically preset) of exposure parameters corresponding to the imaging protocol. The technician 10 performs the exposure parameter adjustment process 12 to manually adjust the initial value of the exposure parameter, and performs the exposure operation using the manually adjusted exposure parameter. The control host determines a thickness value of the imaging target in the exposure operation. The control host generates log data 13 containing exposure parameters and thickness values in the exposure operation. The control host sends the log data 13 to a database 15 in the cloud 14.
The cloud 14 extracts, as the first training data, exposure parameter values in exposure operations of the respective hospitals and thickness values in the exposure operations from the database 15 as history data. A first model training process 16 is performed on the first artificial neural network model based on the first training data to train a generic exposure parameter prediction model 19 that is widely applicable to various hospitals.
The cloud 14 delivers the general exposure parameter prediction model 19 to each hospital, and each hospital can automatically predict the exposure parameters by using the exposure parameter prediction model 19. The control host of each hospital further transmits log data in the process of using the exposure parameter prediction model 19 to the database 15.
The cloud 14 further extracts, from the database 15, the exposure parameter history prediction value predicted by the specific hospital by using the exposure parameter prediction model 19 and the exposure parameter history adjustment value corresponding to the exposure parameter history prediction value, as second training data. A second model training process 17 is performed on the second artificial neural network model based on the second training data to obtain an exposure parameter adjustment model for the particular hospital. The output of the exposure parameter prediction model 19 issued to a specific hospital is connected to the input of the exposure parameter adjustment model of the specific hospital, so that the adjusted exposure parameter prediction model 18 suitable for the specific hospital can be formed. The exposure parameter prediction model 18 is a model specific to the particular hospital. Similarly, individual dedicated models may be generated separately for each hospital.
Each hospital technician may then select one from the generic exposure parameter prediction model 19 or the exposure parameter prediction model 18 applicable to the home hospital to perform the exposure operation. In a specific exposure operation, the technician 10 needs to perform the imaging protocol selection process 21 and the exposure process 22 based on the exposure parameters determined by the selected model.
Fig. 4 is an exemplary schematic diagram of a human-machine interaction process for X-ray imaging in accordance with an embodiment of the present invention. Training data may be conveniently collected based on the human-machine interaction process of fig. 4. In fig. 4, the human-computer interaction 30 includes a subject information display area 31, an imaging protocol display area 35, an exposure button 38, and an exposure parameter display area 40. In the tester information display area 31, a tester identification 32, a tester gender 33, and a tester age 34 are displayed.
In the imaging protocol presentation area 35, a neck left side imaging protocol 36 and a neck right side imaging protocol 37 are exemplarily presented for a user to select a specific imaging protocol.
In the exposure parameter display area 40, specific values of the bulb voltage 41, the bulb current 42, the exposure time 43, the exposure dose 44, and the exposure density 45 are displayed. Moreover, technician 10 may adjust the specific values of bulb voltage 41, bulb current 42, exposure time 43, exposure dose 44, and exposure density 45.
When the technician finishes the adjustment, the exposure button 38 is triggered, and the exposure can be performed according to the adjusted exposure parameters.
Fig. 5 is a block diagram of an apparatus for determining exposure parameters in X-ray imaging according to an embodiment of the present invention. As shown in fig. 5, an apparatus 500 for determining exposure parameters in X-ray imaging includes:
A determining module 501 for determining a thickness value of an imaging object and an X-ray imaging protocol;
A first input module 502, configured to input a thickness value into an exposure parameter prediction model corresponding to an X-ray imaging protocol, where the exposure parameter prediction model is trained based on first training data, and the first training data includes an exposure parameter historical value in a historical exposure operation based on the X-ray imaging protocol and a historical thickness value in the historical exposure operation;
A first receiving module 503 is configured to receive an exposure parameter prediction value determined based on the thickness value from the exposure parameter prediction model.
In an exemplary embodiment, further comprising:
A first training module 504, configured to establish a first artificial neural network model; inputting the first training data into a first artificial neural network model; the first artificial neural network model is trained as an exposure parameter prediction model using the first training data.
In an exemplary embodiment, the method further includes a second input module 505 for inputting the exposure parameter predicted value into an exposure parameter adjustment model corresponding to the exposure parameter predicted model, wherein the exposure parameter adjustment model is trained based on second training data, the second training data including an exposure parameter history predicted value historically output by the exposure parameter predicted model and an exposure parameter history adjusted value corresponding to the exposure parameter history predicted value; a second receiving module 506, configured to receive, from the exposure parameter adjustment model, an exposure parameter adjustment value determined based on the exposure parameter prediction value; an exposure module 507 for performing an exposure operation on the imaging target based on the exposure parameter adjustment value to generate an X-ray image.
In an exemplary embodiment, a second training module 508 is further included for building a second artificial neural network model; inputting the second training data into a second artificial neural network model; training the second artificial neural network model into an exposure parameter adjustment model by using the second training data.
In an exemplary embodiment, a determination module 501 is configured to determine a distance of an X-ray source from a surface point of an imaging target based on a three-dimensional image containing the imaging target; determining a thickness value of the imaging target at a surface point based on a distance between the X-ray source and the imaging surface, a distance between the contact plate and the detector, and a distance between the X-ray source and the surface point of the imaging target; determining a thickness value of the imaging target based on the thickness value of the imaging target at the surface point; wherein the thickness values of the imaging target include: a minimum thickness value of the imaging target or an average thickness value of the imaging target.
Fig. 6 is a block diagram of an apparatus for determining exposure parameters in X-ray imaging having a memory-processor architecture according to an embodiment of the invention.
As shown in fig. 6, an apparatus 600 for determining exposure parameters in X-ray imaging comprises a processor 601, a memory 602, and a computer program stored on the memory 602 and executable on the processor 601, which when executed by the processor 601 implements a method for determining exposure parameters in X-ray imaging as described above. The memory 602 may be implemented as a variety of storage media such as an electrically erasable programmable read-only memory (EEPROM), a Flash memory (Flash memory), a programmable read-only memory (PROM), and the like. Processor 601 may be implemented to include one or more central processors or one or more field programmable gate arrays that integrate one or more central processor cores. In particular, the central processor or central processor core may be implemented as a CPU or MCU or DSP, etc.
It should be noted that not all the steps and modules in the above processes and the structure diagrams are necessary, and some steps or modules may be omitted according to actual needs. The execution sequence of the steps is not fixed and can be adjusted as required. The division of the modules is merely for convenience of description and the division of functions adopted in the embodiments, and in actual implementation, one module may be implemented by a plurality of modules, and functions of a plurality of modules may be implemented by the same module, and the modules may be located in the same device or different devices.
The hardware modules in the various embodiments may be implemented mechanically or electronically. For example, a hardware module may include specially designed permanent circuits or logic devices (e.g., special purpose processors such as FPGAs or ASICs) for performing certain operations. A hardware module may also include programmable logic devices or circuits (e.g., including a general purpose processor or other programmable processor) temporarily configured by software for performing particular operations. As regards implementation of the hardware modules in a mechanical manner, either by dedicated permanent circuits or by circuits that are temporarily configured (e.g. by software), this may be determined by cost and time considerations.
The present invention also provides a machine-readable storage medium storing instructions for causing a machine to perform a method as herein. Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium. Further, some or all of the actual operations may be performed by an operating system or the like operating on a computer based on instructions of the program code. The program code read out from the storage medium may also be written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion unit connected to the computer, and then, based on instructions of the program code, a CPU or the like mounted on the expansion board or the expansion unit may be caused to perform part or all of actual operations, thereby realizing the functions of any of the above embodiments. Storage medium implementations for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or cloud by a communications network.
The foregoing is merely a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (14)
1. A method (100) of determining exposure parameters in X-ray imaging, comprising:
Determining an X-ray imaging protocol and a thickness value of an imaging target (101);
Inputting the thickness value into an exposure parameter prediction model corresponding to the X-ray imaging protocol, wherein the exposure parameter prediction model is trained based on first training data comprising an exposure parameter historical value in a historical exposure operation based on the X-ray imaging protocol and a historical thickness value in the historical exposure operation (102);
An exposure parameter prediction value (103) determined based on the thickness value is received from the exposure parameter prediction model.
2. The method (100) of determining exposure parameters in X-ray imaging according to claim 1, further comprising:
establishing a first artificial neural network model;
Inputting the first training data into the first artificial neural network model;
Training the first artificial neural network model into the exposure parameter prediction model by using the first training data.
3. The method (100) of determining exposure parameters in X-ray imaging according to claim 1, further comprising:
inputting the exposure parameter predicted value into an exposure parameter adjustment model corresponding to the exposure parameter predicted model, wherein the exposure parameter adjustment model is trained based on second training data, the second training data comprising an exposure parameter history predicted value historically output by the exposure parameter predicted model and an exposure parameter history adjustment value corresponding to the exposure parameter history predicted value;
receiving an exposure parameter adjustment value determined based on the exposure parameter prediction value from the exposure parameter adjustment model;
an exposure operation is performed on the imaging target based on the exposure parameter adjustment value to generate an X-ray image.
4. A method (100) of determining exposure parameters in X-ray imaging according to claim 3, further comprising:
Establishing a second artificial neural network model;
Inputting the second training data into the second artificial neural network model;
and training the second artificial neural network model into the exposure parameter adjustment model by using the second training data.
5. Method (100) for determining exposure parameters in X-ray imaging according to claim 3 or 4, characterized in that,
The first training data is derived from log data of a plurality of X-ray imaging units;
The second training data is derived from log data of the same X-ray imaging unit or the second training data is derived from log data of the same X-ray imaging operation subject in the same X-ray imaging unit.
6. The method (100) of determining exposure parameters in X-ray imaging according to claim 1, wherein determining a thickness value (101) of an imaging target comprises:
Determining a distance of an X-ray source from a surface point of the imaging target based on a three-dimensional image containing the imaging target;
determining a thickness value of the imaging target at a surface point of the imaging target based on a distance between the X-ray source and an imaging surface, a distance between a contact plate and a detector, and a distance between the X-ray source and the surface point of the imaging target;
determining a thickness value of the imaging target based on the thickness value of the imaging target at the surface point;
wherein the thickness value of the imaging target comprises: a minimum thickness value of the imaging target or an average thickness value of the imaging target.
7. An apparatus (500) for determining exposure parameters in X-ray imaging, comprising:
A determination module (501) for determining a thickness value of an imaging object for an X-ray imaging protocol;
A first input module (502) for inputting the thickness value into an exposure parameter prediction model corresponding to the X-ray imaging protocol, wherein the exposure parameter prediction model is trained based on first training data comprising exposure parameter historical values in a historical exposure operation based on the X-ray imaging protocol and historical thickness values in the historical exposure operation;
A first receiving module (503) for receiving an exposure parameter prediction value determined based on the thickness value from the exposure parameter prediction model.
8. The apparatus (500) for determining exposure parameters in X-ray imaging according to claim 7, further comprising:
A first training module (504) for building a first artificial neural network model; inputting the first training data into the first artificial neural network model; training the first artificial neural network model into the exposure parameter prediction model by using the first training data.
9. The apparatus (500) for determining exposure parameters in X-ray imaging according to claim 7, further comprising:
A second input module (505) for inputting the exposure parameter predicted value into an exposure parameter adjustment model corresponding to the exposure parameter predicted model, wherein the exposure parameter adjustment model is trained based on second training data, the second training data comprising an exposure parameter history predicted value historically output by the exposure parameter predicted model and an exposure parameter history adjustment value corresponding to the exposure parameter history predicted value;
A second receiving module (506) for receiving an exposure parameter adjustment value determined based on the exposure parameter prediction value from the exposure parameter adjustment model;
an exposure module (507) for performing an exposure operation on the imaging target based on the exposure parameter adjustment value to generate an X-ray image.
10. The apparatus (500) for determining exposure parameters in X-ray imaging according to claim 9, further comprising:
A second training module (508) for building a second artificial neural network model; inputting the second training data into the second artificial neural network model; and training the second artificial neural network model into the exposure parameter adjustment model by using the second training data.
11. The apparatus (500) for determining exposure parameters in X-ray imaging according to any of claims 9-10, characterized in that,
-The determination module (501) for determining a distance of an X-ray source from a surface point of the imaging target based on a three-dimensional image comprising the imaging target; determining a thickness value of the imaging target at a surface point of the imaging target based on a distance between the X-ray source and an imaging surface, a distance between a contact plate and a detector, and a distance between the X-ray source and the surface point of the imaging target; determining a thickness value of the imaging target based on the thickness value of the imaging target at the surface point; wherein the thickness value of the imaging target comprises: a minimum thickness value of the imaging target or an average thickness value of the imaging target.
12. An apparatus (600) for determining exposure parameters in X-ray imaging, comprising a processor (601) and a memory (602);
The memory (602) has stored therein an application executable by the processor (601) for causing the processor to perform the method (100) of determining exposure parameters in X-ray imaging according to any one of claims 1-6.
13. A computer readable storage medium, having stored therein computer readable instructions which, when executed by a processor, implement a method (100) of determining exposure parameters in X-ray imaging according to any of claims 1-6.
14. A computer program product comprising a computer program which, when executed by a processor, implements a method (100) of determining exposure parameters in X-ray imaging according to any one of claims 1-6.
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| CN202211224897.7A CN117911306A (en) | 2022-10-09 | 2022-10-09 | Method, apparatus, storage medium and program product for determining exposure parameters |
| PCT/CN2022/132985 WO2024077712A1 (en) | 2022-10-09 | 2022-11-18 | Method and apparatus for determining exposure parameter, storage medium, and program product |
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| CN202211224897.7A CN117911306A (en) | 2022-10-09 | 2022-10-09 | Method, apparatus, storage medium and program product for determining exposure parameters |
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| WO2021213412A1 (en) * | 2020-04-20 | 2021-10-28 | Shanghai United Imaging Healthcare Co., Ltd. | Imaging systems and methods |
| CN111631742A (en) * | 2020-06-05 | 2020-09-08 | 上海联影医疗科技有限公司 | X-ray imaging method and system based on surface light source |
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