CN112932522A - Medical image acquisition method and device and computer equipment - Google Patents
Medical image acquisition method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a medical image acquisition method, a medical image acquisition device and computer equipment, wherein the medical image acquisition method comprises the following steps: acquiring identity information of a scanning object and a first image of the scanning object, wherein the first image is acquired through camera equipment; inputting the first image into a trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of the scanning object; acquiring a second positioning image of the scanned object stored in a database based on the identity information; and registering the first positioning image and the second positioning image to obtain a third positioning image. According to the medical image acquisition method, the medical image acquisition device and the computer equipment, the first positioning image is acquired through the placement position information of the scanned object, the first positioning image is compared with the second positioning image in the database, the third positioning image is acquired after registration, the positioning image does not need to be acquired when the scanned object is scanned again, the efficiency is high, and radiation damage to the scanned object is reduced.
Description
Technical Field
The present application relates to the field of medical imaging technologies, and in particular, to a medical image acquisition method, apparatus, and computer device.
Background
In the PET-CT workflow, the conventional scanning procedure is: the scanning object scans according to a certain body position and parameters to obtain a set of CT positioning images as positioning sheets, positioning planning is carried out according to the positioning sheets, then the scanning object is positioned, PET scanning is carried out after the positioning is accurate, so that the scanning position is more accurate, and the imaging effect is better.
However, the acquisition of the positioning image needs to be performed again for the next or the second re-scan of the same scanning object, which is inefficient, and radiation damage to the scanning object may be caused to a certain extent due to the radiation in the CT scan.
At present, no effective solution is provided for the problems that in the related art, the scanning object needs to acquire a positioning image again in each scanning, the efficiency is low, and the scanning object is damaged by radiation to a certain extent.
Disclosure of Invention
The embodiment of the application provides a medical image acquisition method, a medical image acquisition device and computer equipment, and aims to at least solve the problems that in the related art, a positioning image needs to be acquired again when a scanning object is scanned every time, the efficiency is low, and radiation damage to the scanning object is caused to a certain degree.
In a first aspect, an embodiment of the present application provides a medical image acquisition method, including:
acquiring identity information of a scanning object and a first image of the scanning object, wherein the first image is acquired through camera equipment;
inputting the first image into a trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of the scanning object;
acquiring a second positioning image of the scanned object stored in a database based on the identity information;
and registering the first positioning image and the second positioning image to obtain a third positioning image.
In some of these embodiments, the medical image acquisition method further comprises:
selecting a scanning starting position and a scanning ending position on the third positioning image;
determining a scanning range according to the scanning starting position and the scanning ending position;
and scanning the scanning object based on the scanning range.
In some of these embodiments, the medical image acquisition method further comprises:
and comparing the second positioning image with the third positioning image, and determining whether the body position of the scanning object needs to be adjusted according to the comparison result.
In some embodiments, the comparing the second scout image and the third scout image, and determining whether the body position of the scanned object needs to be adjusted according to the comparison result includes:
comparing the second positioning image with the third positioning image to obtain a position difference value;
comparing the position difference value with a preset threshold value;
if the position difference value is larger than the preset threshold value, the body position of the scanned object needs to be adjusted.
In some embodiments, the position difference includes an X-direction difference, a Y-direction difference, and a Z-direction difference, and the comparing the position difference with a preset threshold further includes:
respectively comparing the difference value in the X direction, the difference value in the Y direction and the difference value in the Z direction with a preset threshold value;
if the difference value in any direction is larger than the preset threshold value, the body position of the scanned object needs to be adjusted.
In some embodiments, the inputting the first image into the trained neural network to obtain the first positioning image further comprises:
acquiring a first training image and a corresponding first training positioning image;
establishing a training set based on the first training image and a first training scout image;
training an initial neural network model based on the training set to obtain a trained neural network.
In some of these embodiments, the initial neural network model comprises a condition-generating confrontation network model.
In some of these embodiments, the medical image acquisition method further comprises:
and sending the first positioning image and the second positioning image to an upper computer for displaying.
In a second aspect, an embodiment of the present application provides a medical image acquisition apparatus, including:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring identity information of a scanned object and a first image of the scanned object, and the first image is acquired through camera equipment;
the positioning module is used for inputting the first image into the trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of the scanning object;
the historical image acquisition module is used for acquiring a second positioning image of the scanning object stored in the database based on the identity information;
and the registration module is used for registering the first positioning image and the second positioning image to obtain a third positioning image.
In a third aspect, the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the medical image acquisition method according to the first aspect.
Compared with the related art, the medical image acquisition method, the medical image acquisition device and the computer equipment provided by the embodiment of the application acquire the identity information of the scanned object and the first image of the scanned object, wherein the first image is acquired through the camera equipment; inputting the first image into a trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of the scanning object; acquiring a second positioning image of the scanned object stored in a database based on the identity information; and the first positioning image is obtained by the mode of registering the first positioning image and the second positioning image, the first positioning image is obtained by the placing position information of the scanned object and is compared with the second positioning image in the database, and the third positioning image is obtained after registration, so that the positioning image is not required to be obtained when the scanned object is scanned again, the efficiency is higher, and the radiation damage to the scanned object is reduced.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart illustrating a medical image acquisition method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a medical image acquisition method according to another embodiment of the present invention;
FIG. 3 is a block diagram of a medical image acquisition apparatus according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The scout image is a complete profile image of the scanned object. Each clinical protocol of the hospital comprises a positioning image for the target to be detected, and the acquisition mode of the positioning image comprises the following steps: the bulb tube is still at a preset position and does not rotate, only the sickbed is controlled to move in parallel along the z direction during scanning, and a positioning image is obtained through scanning. The positioning image can assist a doctor to set the starting position and the scanning length of the tomography or the spiral scanning, adjust the scanning range and provide a reference for reducing the radiation dose of the tomography or the spiral scanning.
When a scanning object is scanned in a traditional scanning mode every time, pre-scanning needs to be carried out again to obtain a positioning image, after the positioning image is obtained, a doctor determines an area needing important observation in the positioning image, and uses the corresponding area as an area of interest to determine a scanning range, so that not only is the scanning efficiency reduced, but also the scanning object is subjected to radiation damage in the acquisition process of the positioning image.
Referring to fig. 1, fig. 1 is a schematic flow chart of a medical image acquisition method according to an embodiment of the invention.
In this embodiment, the medical image acquisition method includes:
s101, identity information of a scanning object and a first image of the scanning object are obtained, and the first image is obtained through the image pickup equipment.
Illustratively, the first time the scan object is scanned, a scan record is left in the system, the scan record including the identity information of the scan object. It can be understood that the identity information is the only identification information of the scanned object, and can be an identity card number or a visit number, and the like, and only the identity of the scanned object needs to be identified. In this embodiment, the first image is obtained by the image capturing apparatus and is used to show an external state of the scan object, such as placement position information of the scan object. It can be understood that the image capturing device may be a visible light camera or an infrared camera, or may be other image capturing devices, and only the external state of the scanned object needs to be acquired.
S102, inputting the first image into the trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of a scanning object.
It can be understood that after the initial neural network model is trained, a two-dimensional plane image representing the complete contour of the scanned object, i.e. the first scout image, can be obtained through analysis and processing based on the input first image.
S103, acquiring a second positioning image of the scanning object stored in the database based on the identity information.
For example, when the scanning object is scanned for the first time, a pre-scan is performed to obtain a positioning image, that is, a second positioning image, the second positioning image is stored in the database and is bound to the identity information of the scanning object, and the second positioning image can be obtained from the database through the identity information.
And S104, registering the first positioning image and the second positioning image to obtain a third positioning image.
In this embodiment, the first scout image is a current scout image obtained through a neural network based on a current external state of a scanned object, and the second scout image is a historical scout image obtained through pre-scanning and stored in a database.
The medical image acquisition method comprises the steps of acquiring identity information of a scanned object and a first image of the scanned object, wherein the first image is acquired through camera equipment; inputting the first image into the trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of a scanned object; acquiring a second positioning image of the scanned object stored in the database based on the identity information; the first positioning image and the second positioning image are registered to obtain a third positioning image, the first positioning image is obtained through the placing position information of the scanned object and is compared with the second positioning image in the database, the third positioning image is obtained after registration, the positioning image does not need to be obtained when the scanned object is scanned again, the efficiency is high, and radiation damage to the scanned object is reduced.
In another embodiment, the medical image acquisition method further comprises the steps of:
step 1, selecting a scanning starting position and a scanning ending position on a third positioning image;
step 2, determining a scanning range according to the scanning starting position and the scanning ending position;
and 3, scanning the scanning object based on the scanning range.
Illustratively, before scanning, the physician determines the region needing to be focused by the scout image, and takes the corresponding region as the region of interest to determine the scanning range. Therefore, after the third positioning image representing the complete contour of the scanning object is obtained, the scanning starting position and the scanning ending position are defined based on the scanning requirement and the actual situation of the scanning object, and the scanning is carried out by taking the range framed by the scanning starting position and the scanning ending position as the scanning range.
In this embodiment, the medical image acquiring method may be applied to a PET-CT system, that is, a first positioning image is obtained through a first image, a second positioning image is obtained through identity information, a third positioning image is obtained based on the first positioning image and the second positioning image, and a scanning range is defined on the third positioning image according to a scanning requirement, so as to perform PET scanning. It is understood that the above medical image acquisition method can be applied not only in a PET-CT system, but also in any scanning scenario requiring acquisition of a scout image, and is not limited herein.
In another embodiment, the medical image acquisition method further comprises the steps of:
and comparing the second positioning image with the third positioning image, and determining whether the body position of the scanning object needs to be adjusted according to the comparison result.
It can be understood that after the third scout image is obtained, the scanning range needs to be determined based on the third scout image for scanning, the scanning range planning may be performed again based on the third scout image, or the body position of the scanned object may be adjusted based on the second scout image, so as to directly perform scanning by using the scanning range planning corresponding to the second scout image.
Exemplarily, the second scout image not only represents the complete contour of the scanned object, but also performs scanning range planning, so that if the second scout image and the third scout image can be well matched, it is indicated that the current body position of the scanned object is substantially consistent with the body position corresponding to the second scout image, the scanning range planning corresponding to the second scout image can be directly adopted for scanning, and the scanning range planning does not need to be performed again; if the matching can not be completed, the body position of the scanning object needs to be adjusted.
In another embodiment, the step of comparing the second scout image with the third scout image and determining whether the body position of the scanned object needs to be adjusted according to the comparison result comprises the following steps:
step 1, comparing the second positioning image with the third positioning image to obtain a position difference value;
step 2, comparing the position difference value with a preset threshold value;
and 3, if the position difference value is larger than a preset threshold value, the body position of the scanned object needs to be adjusted.
Exemplarily, the position difference value is a deviation value of the second positioning image and the third positioning image in each direction, the preset threshold value can be set by a user in advance, if the deviation value is within the preset threshold value, the deviation is small, and the scanning range planning corresponding to the second positioning image can still be directly adopted; if the deviation value is greater than the preset threshold value, it indicates that the deviation is large, and the body position of the scanned object needs to be adjusted to be matched with the second positioning image.
The embodiment sets a preset threshold as a standard, compares the preset threshold with the position difference value to judge the matching degree of the second positioning image and the third positioning image, and determines matching without complete correspondence between the two positioning images, but determines matching when the difference value is within the threshold range, so that the adaptability is stronger.
In another embodiment, the position difference includes an X-direction difference, a Y-direction difference, and a Z-direction difference, and comparing the position difference with a preset threshold further includes the following steps:
step 1, comparing the difference value in the X direction, the difference value in the Y direction and the difference value in the Z direction with a preset threshold value respectively;
and 2, if the difference value in any direction is greater than a preset threshold value, the body position of the scanned object needs to be adjusted.
In this embodiment, comparing the second scout image with the third scout image to obtain the position difference includes obtaining a transformation matrix based on the key points of the second scout image and the third scout image, and obtaining the position difference based on the transformation matrix. Specifically, the rigid registration has 9 parameters, including X, Y, Z and rotation angles in all directions, and the difference between the parameters of the second scout image and the third scout image can be obtained based on the transformation matrix.
It can be understood that the position difference includes an X-direction difference, a Y-direction difference, and a Z-direction difference, and if the difference in any one direction is greater than a preset threshold, it indicates that the deviation is too large, and the body position of the scanning object in the corresponding direction needs to be adjusted to match the second positioning image. And matching the second positioning image and the third positioning image successfully only when the difference values in the three directions are within the preset threshold range, and scanning by directly adopting the scanning range plan corresponding to the second positioning image. In other embodiments, the difference values in other directions or dimensions may be used as the determination criteria, and only the deviation degree of the body position of the scanning object needs to be determined.
In the embodiment, the difference value in the X direction, the difference value in the Y direction and the difference value in the Z direction are used as the position difference value, and are compared with the preset threshold value to judge the matching degree of the second positioning image and the third positioning image, and the matching is considered only when the difference values in the three directions are within the preset range, so that the comparison is more accurate, and the scanning effect is better.
In another embodiment, the step of inputting the first image into the trained neural network to obtain the first positioning image further comprises the following steps:
step 1, acquiring a first training image and a corresponding first training positioning image;
step 2, establishing a training set based on the first training image and the first training orientation image;
and 3, training the initial neural network model based on the training set to obtain a trained neural network.
The first training image is a training image obtained through the camera equipment, and the corresponding first training positioning image is obtained by pre-scanning the scanning object when the scanning object is in the body position of the first training image. It will be appreciated that the first training image and the first training scout are known data. In this embodiment, after the training of the initial neural network model is completed through the training set composed of the first training image and the first training positioning image, the first positioning image of the body position corresponding to the scanned object can be output only by inputting the first image representing the external state of the user, which is acquired by the image capturing device.
In another embodiment, the initial neural Network model comprises a Conditional Generative Adaptive Network (CGAN) model. In other embodiments, other neural network models may be selected as the initial neural network model, and may be selected by the user according to actual conditions.
In another embodiment, the medical image acquisition method further comprises the steps of:
and sending the first positioning image and the second positioning image to an upper computer for displaying.
Exemplarily, the first positioning image and the second positioning image are sent to an upper computer to be displayed in real time, a user can compare the images in real time and make corresponding adjustment, and the registration result is more accurate.
In another embodiment, the key point information can be further acquired based on the first positioning image, that is, a plurality of key points are acquired based on the first positioning image, the key points are connected to form a key point schematic diagram, and the key point schematic diagram and the second positioning image are sent to an upper computer to be displayed.
The key point schematic diagram and the second positioning image are sent to the upper computer to be displayed in real time, the comparison is more visual, and the registration effect is more accurate.
Referring to fig. 2, fig. 2 is a flowchart illustrating a medical image acquiring method according to another embodiment of the present invention.
In this embodiment, a patient is first registered before scanning, identity information of the patient is obtained from registration information, and whether the identity information exists in a database is determined, if the identity information does not exist in the database, scanning is performed by using a conventional scanning process; if the identity information exists in the database, a camera is opened, a whole-body picture of the patient is shot, the whole-body picture of the patient and related information are sent to a memory, the whole-body picture of the patient is input into a trained neural network to obtain a first positioning image, a second positioning image is read from the database based on the identity information of the patient, registration is carried out based on the first positioning image and the second positioning image to obtain a third positioning image, the second positioning image and the third positioning image are compared, whether the body position of the patient needs to be adjusted or not is determined according to the comparison result, if adjustment is needed, the patient is guided to adjust the body position, and PET scanning is carried out after the adjustment is finished.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The present embodiment further provides a medical image acquiring apparatus, which is used for implementing the above embodiments and preferred embodiments, and the description of the apparatus is omitted for brevity. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a block diagram of a medical image acquisition apparatus according to an embodiment of the present application, as shown in fig. 3, the apparatus including:
the acquiring module 10 is configured to acquire identity information of a scanned object and a first image of the scanned object, where the first image is acquired by an image capturing device.
And the positioning module 20 is configured to input the first image into the trained neural network to obtain a first positioning image, where the first positioning image is a two-dimensional planar image of the scanned object.
And the historical image acquisition module 30 is used for acquiring a second positioning image of the scanning object stored in the database based on the identity information.
And the registration module 40 is configured to register the first scout image and the second scout image to obtain a third scout image.
Medical image acquisition apparatus, further comprising: and a scanning module.
A scanning module to:
selecting a scanning starting position and a scanning ending position on the third positioning image;
determining a scanning range according to the scanning starting position and the scanning ending position;
the scan object is scanned based on the scan range.
Medical image acquisition apparatus, further comprising: and a comparison module.
And the comparison module is used for comparing the second positioning image with the third positioning image and determining whether the body position of the scanning object needs to be adjusted or not according to the comparison result.
A comparison module further configured to:
comparing the second positioning image with the third positioning image to obtain a position difference value;
comparing the position difference value with a preset threshold value;
if the position difference value is larger than the preset threshold value, the body position of the scanned object needs to be adjusted.
A comparison module further configured to:
respectively comparing the difference value in the X direction, the difference value in the Y direction and the difference value in the Z direction with a preset threshold value;
if the difference value in any direction is larger than the preset threshold value, the body position of the scanned object needs to be adjusted.
Medical image acquisition apparatus, further comprising: and a training module.
A training module to:
acquiring a first training image and a corresponding first training positioning image;
establishing a training set based on the first training image and the first training scout image;
training the initial neural network model based on the training set to obtain a trained neural network.
Medical image acquisition apparatus, further comprising: and a display module.
And the display module is used for sending the first positioning image and the second positioning image to the upper computer for displaying.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, the medical image acquisition method described in conjunction with fig. 1 in the embodiment of the present application may be implemented by a computer device. Fig. 4 is a hardware structure diagram of a computer device according to an embodiment of the present application.
The computer device may comprise a processor 51 and a memory 52 in which computer program instructions are stored.
Specifically, the processor 51 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 52 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions executed by the processor 51.
The processor 51 realizes any one of the medical image acquisition methods in the above embodiments by reading and executing computer program instructions stored in the memory 52.
In some of these embodiments, the computer device may also include a communication interface 53 and a bus 50. As shown in fig. 4, the processor 51, the memory 52, and the communication interface 53 are connected via the bus 50 to complete mutual communication.
The communication interface 53 is used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application. The communication interface 53 may also enable communication with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The computer device may execute the medical image acquisition method in the embodiments of the present application based on the acquired computer program instructions, thereby implementing the medical image acquisition method described in connection with fig. 1.
In addition, in combination with the medical image acquisition method in the above embodiments, the embodiments of the present application may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the medical image acquisition methods of the above embodiments.
According to the medical image acquisition method, the medical image acquisition device and the computer equipment, the identity information of the scanned object and the first image of the scanned object are acquired, and the first image is acquired through the camera equipment; inputting the first image into the trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of a scanned object; acquiring a second positioning image of the scanned object stored in the database based on the identity information; the first positioning image and the second positioning image are registered to obtain a third positioning image, the first positioning image is obtained through the placing position information of the scanned object and is compared with the second positioning image in the database, the third positioning image is obtained after registration, the positioning image does not need to be obtained when the scanned object is scanned again, the efficiency is high, and radiation damage to the scanned object is reduced.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A medical image acquisition method, characterized by comprising:
acquiring identity information of a scanning object and a first image of the scanning object, wherein the first image is acquired through camera equipment;
inputting the first image into a trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of the scanning object;
acquiring a second positioning image of the scanned object stored in a database based on the identity information;
and registering the first positioning image and the second positioning image to obtain a third positioning image.
2. The medical image acquisition method according to claim 1, further comprising:
selecting a scanning starting position and a scanning ending position on the third positioning image;
determining a scanning range according to the scanning starting position and the scanning ending position;
and scanning the scanning object based on the scanning range.
3. The medical image acquisition method according to claim 1, further comprising:
and comparing the second positioning image with the third positioning image, and determining whether the body position of the scanning object needs to be adjusted according to the comparison result.
4. The medical image acquisition method according to claim 3, wherein the comparing the second scout image and the third scout image, and the determining whether the posture of the scanned object needs to be adjusted according to the comparison result comprises:
comparing the second positioning image with the third positioning image to obtain a position difference value;
comparing the position difference value with a preset threshold value;
if the position difference value is larger than the preset threshold value, the body position of the scanned object needs to be adjusted.
5. The medical image obtaining method according to claim 4, wherein the position difference values include an X-direction difference value, a Y-direction difference value, and a Z-direction difference value, and the comparing the position difference value with a preset threshold further includes:
respectively comparing the difference value in the X direction, the difference value in the Y direction and the difference value in the Z direction with a preset threshold value;
if the difference value in any direction is larger than the preset threshold value, the body position of the scanned object needs to be adjusted.
6. The medical image acquisition method according to claim 1, wherein the inputting the first image into the trained neural network to obtain the first positioning image further comprises:
acquiring a first training image and a corresponding first training positioning image;
establishing a training set based on the first training image and a first training scout image;
training an initial neural network model based on the training set to obtain a trained neural network.
7. A medical image acquisition method according to claim 6, wherein the initial neural network model comprises a conditionally generated confrontation network model.
8. The medical image acquisition method according to claim 1, further comprising:
and sending the first positioning image and the second positioning image to an upper computer for displaying.
9. A medical image acquisition apparatus, characterized by comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring identity information of a scanned object and a first image of the scanned object, and the first image is acquired through camera equipment;
the positioning module is used for inputting the first image into the trained neural network to obtain a first positioning image, wherein the first positioning image is a two-dimensional plane image of the scanning object;
the historical image acquisition module is used for acquiring a second positioning image of the scanning object stored in the database based on the identity information;
and the registration module is used for registering the first positioning image and the second positioning image to obtain a third positioning image.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the medical image acquisition method as claimed in any one of claims 1 to 8 when executing the computer program.
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