CN111128345A - Medical image acquisition method, medical scanning device and computer storage medium - Google Patents
Medical image acquisition method, medical scanning device and computer storage medium Download PDFInfo
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Abstract
The present application relates to a method of medical image acquisition, a medical scanning device and a computer storage medium. The method comprises the following steps: acquiring scanning equipment information and measured object information; inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object; and scanning the tested object according to the scanning parameters to obtain a medical image. In the method, the medical scanning equipment can automatically obtain the scanning parameters of the tested object according to the scanning equipment information and the tested object information without manual intervention, so that the intelligence and efficiency of the medical image acquisition process are improved; and the trained scanning parameter recommendation model determines the scanning parameters, so that the accuracy of the scanning parameters is improved, and the accuracy of the obtained medical image is further improved.
Description
Technical Field
The present application relates to the field of medical technology, and in particular, to a method for acquiring a medical image, a medical scanning device, and a computer storage medium.
Background
In the medical field, medical image scanning apparatuses have become important apparatuses for diagnosing human diseases, such as a conventional direct Digital Radiography (DR), a Computed Tomography (CT), a Nuclear Magnetic Resonance (MR), a Positron Emission Tomography (PET), and the like. Different scanning strategies, such as setting different device scanning parameters, are often required for different scanning devices or different patients.
In the conventional technology, when medical images of different patients are acquired, a radiologist usually inputs corresponding scanning parameters on a scanning device according to his own experience, and then the scanning device scans the patients according to the scanning parameters to obtain the medical images.
However, the medical image acquisition efficiency and accuracy of the conventional technology are low due to the strong subjectivity of the scan parameters input by the doctor.
Disclosure of Invention
Based on this, it is necessary to provide a medical image acquisition method, a medical scanning apparatus and a computer storage medium, aiming at the problems of low efficiency and low accuracy of acquiring medical images in the conventional technology.
In a first aspect, an embodiment of the present application provides a method for acquiring a medical image, including:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
and scanning the tested object according to the scanning parameters to obtain a medical image.
In a second aspect, an embodiment of the present application provides an apparatus for acquiring a medical image, including:
the acquisition module is used for acquiring scanning equipment information and measured object information;
the determining module is used for inputting the scanning equipment information and the information of the tested object into a preset scanning parameter recommendation model and determining the scanning parameters of the tested object;
and the scanning module is used for scanning the tested object according to the scanning parameters to obtain a medical image.
In a third aspect, an embodiment of the present application provides a medical scanning apparatus, including a memory, a processor, and a scanner, where the memory stores a computer program, and the processor implements the following steps when executing the computer program:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
and the scanner is used for scanning the tested object according to the scanning parameters to obtain a medical image.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
the scan parameters are sent to the medical scanning device.
The medical image acquisition method, the medical image acquisition device, the medical scanning equipment and the computer storage medium can acquire scanning equipment information and measured object information; inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object; and scanning the tested object according to the scanning parameters to obtain a medical image. In the method, the medical scanning equipment can automatically obtain the scanning parameters of the tested object according to the scanning equipment information and the tested object information without manual intervention, so that the intelligence and efficiency of the medical image acquisition process are improved; and the trained scanning parameter recommendation model determines the scanning parameters, so that the accuracy of the scanning parameters is improved, and the accuracy of the obtained medical image is further improved.
Drawings
FIG. 1 is a flow chart illustrating a method for acquiring a medical image according to an embodiment;
FIG. 2 is a schematic flow chart diagram illustrating a method for acquiring a medical image according to another embodiment;
FIG. 3 is a flow chart illustrating a method for acquiring medical images according to yet another embodiment;
FIG. 4 is a flowchart illustrating a method for acquiring medical images according to yet another embodiment;
FIG. 4a is a schematic diagram of a similarity metric function model according to an embodiment;
FIG. 5 is a schematic diagram of an apparatus for acquiring a medical image according to an embodiment;
fig. 6 is a schematic internal structural diagram of a medical scanning device according to an embodiment.
Detailed Description
The method for acquiring the medical image, provided by the embodiment of the application, can be suitable for the process of scanning the patient to obtain the medical image, can automatically output the scanning parameters matched with the patient, and scans the patient according to the scanning parameters. Among them, the method may be applicable to a Magnetic Resonance Imaging (MRI) process, a Computed Tomography (CT) process, a Positron Emission Tomography (PET) process, and the like. In the conventional technology, when medical images of different patients are acquired, a radiologist usually inputs corresponding scanning parameters on a scanning device according to his own experience, and then scans the patients according to the scanning parameters to obtain the medical images. However, the medical image acquisition efficiency and accuracy of the conventional technology are low due to the strong subjectivity of the scan parameters input by the doctor. The present application provides a medical image acquisition method, a medical scanning device and a computer storage medium, which aim to solve the above technical problems.
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application are further described in detail by the following embodiments in conjunction with the accompanying drawings. 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.
It should be noted that the execution subject of the method embodiments described below may be a medical image acquisition apparatus, which may be implemented as part of or all of a medical scanning device by software, hardware, or a combination of software and hardware. The following method embodiments are described by taking the subject as a medical scanning device, which may be a magnetic resonance apparatus, a CT machine, a PET machine, or the like.
Fig. 1 is a flowchart illustrating a method for acquiring a medical image according to an embodiment. The present embodiments relate to a specific process in which a medical scanning apparatus determines scan parameters of a subject and scans the subject based on scanning apparatus information and subject information. As shown in fig. 1, the method includes:
s101, scanning equipment information and measured object information are acquired.
Specifically, the medical scanning device first obtains the corresponding scanning device information and the measured object information, optionally, the scanning device information may be model information of the scanning device, such as an X001 model magnetic resonance instrument, a Y002 model CT machine, and the like, and may further include hardware configuration of the scanning device, such as maximum scanning intensity, fastest scanning speed, and the like that can be achieved by the magnetic resonance instrument.
Optionally, the subject information may include at least one of age information, height information, weight information, and scanning area information of the subject, which may be obtained by the medical scanning device from a current medical system, for example, when the medical image of the patient is scanned at a hospital, the current information of the patient may be obtained from a patient case library of the hospital. Optionally, the information of the subject may further include other information such as the current hemoglobin index, blood pressure index, bone density, etc. of the subject; the method can also comprise initial diagnosis information given by a doctor during initial diagnosis of the tested object, namely the disease condition which needs to be diagnosed currently, for example, the doctor initially diagnoses that the patient has the lung nodule, and the patient is allowed to take a CT image to confirm whether the lung nodule exists.
And S102, inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object.
Specifically, the medical scanning device may input the scanning device information and the information of the object to be measured into a preset scanning parameter recommendation model, and the scanning parameter recommendation model may determine the scanning parameter corresponding to the object to be measured according to the input information. Different scanning parameters can be output aiming at different scanning devices of the same tested object; different scan parameters may also be output for different subjects within the same scanning apparatus. Optionally, the scan parameters output by the scan parameter recommendation model may include one or more sets, and when the output scan parameters are multiple sets, the medical scanning device may further fuse the multiple sets of scan parameters to obtain the final scan parameters of the object to be measured. Optionally, the outputted scan parameters may include scan intensity, scan speed and scan direction, and may further include applied voltage, applied power, scan time, scan start position and end position, and the like. Alternatively, the scan intensity may be a dose of a contrast agent to be injected, an intensity of radiation, or the like for a CT machine and a PET machine, and the scan intensity may be a magnetic field intensity of radiation for a magnetic resonance apparatus.
Optionally, the scan parameter recommendation model may be a neural network model, a Support Vector Machine (SVM), a Decision Tree (Decision Tree), or a bayesian classification model, or may be a classification model learned by other machines, as long as it can classify the object to be detected according to the scanning device information and the object information to be detected after the training is completed, so as to obtain the corresponding scan parameter.
Optionally, the scan parameter recommendation model may further output a scan protocol, which may include scan parameters and scan steps performed according to the scan parameters.
And S103, scanning the tested object according to the scanning parameters to obtain a medical image.
Specifically, after the medical scanning device obtains the scanning parameters of the object to be measured, the object to be measured can be scanned according to the scanning parameters to obtain the corresponding medical image.
In the method for acquiring a medical image according to this embodiment, the medical scanning device inputs the acquired scanning device information and the information of the object to be measured into a preset scanning parameter recommendation model to obtain the scanning parameters of the object to be measured, and then scans the object to be measured according to the scanning parameters to obtain the medical image. In the method, the medical scanning equipment can automatically obtain the scanning parameters of the tested object according to the scanning equipment information and the tested object information without manual intervention, so that the intelligence and efficiency of the medical image acquisition process are improved; and the trained scanning parameter recommendation model determines the scanning parameters, so that the accuracy of the scanning parameters is improved, and the accuracy of the obtained medical image is further improved.
Optionally, in some embodiments, the scan parameter recommendation model is a neural network model, and before using the model, the model needs to be trained to obtain a converged scan parameter recommendation model. Fig. 2 is a flowchart illustrating a method for acquiring a medical image according to another embodiment. On the basis of the above embodiment, optionally, the training mode of the scan parameter recommendation model includes:
s201, acquiring scanning parameter data sets when different scanning equipment scans different tested objects; the scan parameter dataset includes a correspondence between scan device information, subject information, and scan parameters.
Specifically, in the scan parameter recommendation model training phase, the medical scanning device needs to acquire a large amount of training data, where the training data is a scan parameter data set when different scanning devices scan different objects to be tested, and the scan parameter data set includes correspondence between scanning device information, object information to be tested, and scan parameters, that is, a scanning device a scans an object to be tested C using a scan parameter B. The obtained scanning device information (such as the device model) and the information of the tested object (such as age information, height information, weight information and scanning part information) are used as sample data, and the corresponding scanning parameters are used as gold standards to train the scanning parameter recommendation model. Alternatively, the scan parameter data set may be acquired by a Medical scanning device from a Picture Archiving and Communication System (PACS), a Radiology Information System (RIS), or an Electronic Medical Record (EMR).
Optionally, the scan parameter data set may further include medical images obtained when different scanning devices scan different objects; in order to facilitate the training of the scan parameter recommendation model, the medical images can be preprocessed into image data with the same size and the same format. Optionally, after the scanning parameter data set is obtained, the medical scanning device may store the scanning parameter data set in a database for subsequent calling, where the database may be stored in the medical scanning device, may also be stored in another computer device that communicates with the medical scanning device, and may also be stored in a cloud server, which is not limited in this embodiment.
Since in actual operation of the scanning device, there are usually a plurality of scanning parameters (such as required scanning intensity, scanning speed, etc.), optionally, the scanning parameters in the scanning parameter data set may be embodied in scanning categories, each of which characterizes a set of scanning parameters, such as scanning category a (scanning intensity 1, scanning speed 1), scanning category B (scanning intensity 2, scanning speed 2), etc.
S202, taking the scanning parameter data set as training data, and training the initial scanning parameter recommendation model to obtain the scanning parameter recommendation model.
Specifically, the medical scanning device may input the scanning parameter data set as input data into an initial scanning parameter recommendation model for processing, where the initial scanning parameter recommendation model may output scanning parameters of different objects to be tested. Then, the medical scanning equipment compares the output scanning parameters with real scanning parameters (namely the gold standard) in the scanning parameter data set to obtain the difference between the output scanning parameters and the real scanning parameters in the scanning parameter data set as loss, and trains the initial scanning parameter recommendation model by using the loss until the loss meets the preset condition, and the initial scanning parameter recommendation model is trained to obtain the scanning parameter recommendation model. Optionally, the preset condition that the loss meets may be that the loss value reaches convergence, or may be less than or equal to a set threshold.
Optionally, the scan parameter recommendation model may be a Convolutional Neural Network (CNN), a Full Convolutional Network (FCN), or a neural network model, which is not limited in this embodiment.
Optionally, after obtaining the trained scanning parameter recommendation model, the medical scanning device inputs the acquired scanning device information and the information of the object to be tested into the scanning parameter recommendation model, and determining the scanning parameter of the object to be tested may include: inputting the information of the scanning equipment and the information of the object to be detected into a scanning parameter recommendation model to obtain a scanning recommendation category; and determining the scanning parameters of the tested object according to the recommended scanning category and the corresponding relation between the recommended scanning category and the scanning parameters.
Specifically, after a series of operations such as convolution and pooling are performed on input scanning device information and measured object information by the scanning parameter recommendation model, a scanning recommendation category of the measured object can be obtained, and then a final scanning parameter is obtained according to a corresponding relation between the scanning recommendation category and the scanning parameter. As can be seen from the above description, each scan recommendation category corresponds to a set of scan parameters, and then the required scan parameters can be determined after the scan recommendation categories are obtained. Optionally, the correspondence between the scan recommendation category and the scan parameter may be stored in a database or a data table.
In the method for acquiring a medical image according to this embodiment, the scan parameter recommendation model is a neural network model, and the medical scanning device may first train the initial scan parameter recommendation model using the scan parameter data set to obtain the scan parameter recommendation model, and then input the scanning device information and the information of the object to be measured into the scan parameter recommendation model to determine the scan parameters of the object to be measured. According to the method, the neural network model with the converged training is used as the scanning parameter recommendation model, so that the efficiency and the accuracy of the process of determining the scanning parameters can be improved, and the acquisition efficiency and the accuracy of the medical images are further improved.
Fig. 3 is a flowchart illustrating a method for acquiring a medical image according to another embodiment. On the basis of the foregoing embodiment, optionally, after S102, the method further includes:
s301, determining whether the scan parameter matches the object under test based on the object information.
And S302, if yes, executing the step of scanning the tested object according to the scanning parameters.
And S303, if not, determining new scanning parameters according to the information of the tested object.
Specifically, after the medical scanning device determines the scanning parameters of the object to be measured, it may be determined whether the scanning parameters match the object to be measured according to the object information. Optionally, assuming that the object information includes 4 index information, the scanning parameter includes 2 parameters, and each index information has a corresponding parameter value range under 2 parameters, the medical scanning device determines whether the scanning parameter matches the object by determining whether the obtained scanning parameter is within the corresponding parameter value range.
For example, assume that 4 pieces of index information are (age, height, weight, and scanning location), and 2 pieces of parameters are (scanning intensity, scanning speed), where different ages have different value ranges under the scanning intensity and scanning speed, different heights have different value ranges under the scanning intensity and scanning speed, different weights have different value ranges under the scanning intensity and scanning speed, and different scanning locations have different value ranges under the scanning intensity and scanning speed, and according to the current information of the object to be measured, it is determined whether each value in the obtained scanning parameters is within the corresponding value range.
If the scanning parameters are judged to be matched with the tested object, the medical scanning equipment scans the tested object according to the scanning parameters to obtain a medical image. And if the obtained scanning parameters are not matched with the tested object, determining new scanning parameters according to the information of the tested object, namely adjusting the obtained scanning parameters according to the value range to determine the new scanning parameters, and scanning the tested object according to the new scanning parameters to obtain the medical image. In practical application, optionally, the obtained scanning parameters may be checked by a doctor to determine whether the obtained scanning parameters match the current information to be measured, if the obtained scanning parameters do not match the current information to be measured, a new scanning parameter may be input by the doctor, and the medical scanning device scans the object to be measured according to the received new scanning parameters to obtain a medical image.
Optionally, in some embodiments, after determining new scan parameters according to the subject information, the method further includes: and updating the scanning parameter recommendation model by taking the scanning equipment information, the tested object information and the new scanning parameters as training data. That is, when a new scan parameter needs to be determined, it indicates that there is an error in the scan parameter output according to the scan parameter recommendation model, and then the scan parameter recommendation model may be trained again according to the new scan parameter to update the scan parameter recommendation model, so as to further improve the accuracy of the scan parameter recommendation model and improve the accuracy of the subsequently obtained scan parameter.
In the method for acquiring a medical image according to this embodiment, the medical scanning device determines whether the scanning parameter matches the object to be measured according to the object to be measured information, scans the object to be measured according to the scanning parameter if the scanning parameter matches the object to be measured information, and determines a new scanning parameter according to the object to be measured information if the scanning parameter does not match the object to be measured information. Therefore, the accuracy of the obtained scanning parameters can be further improved in the judging process, and larger errors are prevented.
Optionally, in some embodiments, the scan parameter recommendation model is a similarity metric function model, and fig. 4 is a flowchart of a medical image acquisition method according to yet another embodiment. This embodiment relates to a specific process in which a medical scanning device determines scan parameters of a subject based on scanning device information and subject information. On the basis of the foregoing embodiment, optionally, as shown in fig. 4, S102 may include:
s401, an information list of the same object as the scanning apparatus information is acquired.
Specifically, the medical scanning apparatus may acquire an information list of the same object as the current scanning apparatus information from the scanning parameter data set, where the information list may include object information and scanning parameters, that is, an information list of objects that have been scanned by using the scanning apparatus before.
S402, acquiring information with the maximum similarity to the measured object information from the information list by using the similarity measurement function model.
Specifically, the medical scanning parameters acquire information with the greatest similarity to the information of the subject from the information list by using the similarity metric function model, for example, the current subject information is (30 years old, 160cm, 50kg, scanning head), and the information with the greatest similarity to the information acquired from the information list is (30 years old, 161cm, 50.2kg, scanning head).
And S403, determining the scanning parameter corresponding to the information with the maximum similarity as the scanning parameter of the tested object.
Specifically, since the information list may include the information of the object to be measured and the scanning parameters, after the information with the largest similarity is obtained, the scanning parameters corresponding to the information may also be obtained, and the medical scanning apparatus may use the scanning parameters as the scanning parameters of the object to be measured. Optionally, the number of the information with the maximum similarity may be multiple, and the medical scanning device may fuse the scanning parameters corresponding to the multiple information with the maximum similarity, and use the fusion result as the scanning parameters of the object to be measured. Optionally, the fusion process may be to average the values of the scanning parameters to obtain the scanning parameters of the measured object.
Optionally, after the scanning parameters of the object to be measured are determined, the object to be measured information, the scanning device information, and the scanning parameters may be added to the scanning parameter data set, so that the subsequent scanning parameter recommendation model outputs more accurate scanning parameters.
For example, as shown in fig. 4a, the process of obtaining the information with the greatest similarity to the measured object information by using the similarity metric function model may be: for better illustration, taking the subject information as (age, height, weight) as an example, the 3 pieces of information may be represented as an xyz coordinate system, and assuming that there are 5 pieces of subject information (A, B, C, D, E) in the current information list, the 5 pieces of subject information may correspond to 5 points (e.g., black solid points in the figure) in the xyz coordinate system, and the 5 points are connected to the origin to form 5 vectors; and then, the current measured object information (F) is also corresponding to an xyz coordinate system (such as a hollow point in the figure), and is also connected with the origin to form a vector, so that the smaller the cosine value of which included angle is in the included angles between the vector and the other 5 vectors, the closer the two vectors are, and the greater the similarity between the information corresponding to the vector and the current measured object information is. As shown in fig. 4a, if the cosine value of the included angle between F and a is minimum, which means that the similarity between the information of a and F is maximum, the scanning parameter of a can be recommended to F.
Optionally, in some embodiments, the neural network model may be combined with the similarity metric function model to determine the final scan parameters. The neural network model can determine a plurality of groups of scanning parameters according to the acquired scanning equipment information and the information of the tested object; then, from the measured object information corresponding to the plurality of groups of scanning parameters, the information with the maximum similarity to the current measured object information is determined by using a similarity measurement function model, and the group of scanning parameters corresponding to the information with the maximum similarity is used as the recommended parameters of the current measured object.
In the method for acquiring a medical image according to this embodiment, the scan parameter recommendation model is a similarity measurement function model, the medical scanning device may first acquire an information list of a measured object having the same information as the scanning device, acquire information having the greatest similarity with the information of the measured object from the information list by using the similarity measurement function model, and determine a scan parameter corresponding to the information having the greatest similarity as a scan parameter of the measured object. According to the method, the similarity measurement function model is used as the scanning parameter recommendation model, so that the process efficiency and accuracy of determining the scanning parameters can be improved, and the acquisition efficiency and accuracy of the medical image are improved.
It should be understood that although the various steps in the flowcharts of fig. 1-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
Fig. 5 is a schematic structural diagram of an apparatus for acquiring a medical image according to an embodiment. As shown in fig. 5, the apparatus includes: an acquisition module 11, a determination module 12 and a scanning module 13.
Specifically, the acquiring module 11 is configured to acquire scanning device information and object information.
And the determining module 12 is configured to input the scanning device information and the object information into a preset scanning parameter recommendation model, and determine a scanning parameter of the object.
And the scanning module 13 is used for scanning the tested object according to the scanning parameters to obtain a medical image.
The apparatus for acquiring a medical image provided by this embodiment may perform the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
In one embodiment, the scan parameter recommendation model is a neural network model, and the apparatus further includes a training module configured to obtain scan parameter data sets when different scanning devices scan different objects; the scanning parameter data set comprises corresponding relations among scanning equipment information, measured object information and scanning parameters; and training the initial scanning parameter recommendation model by taking the scanning parameter data set as training data to obtain the scanning parameter recommendation model.
In one embodiment, the determining module 12 is specifically configured to input the scanning device information and the measured object information into a scanning parameter recommendation model to obtain a scanning recommendation category; and determining the scanning parameters of the tested object according to the recommended scanning category and the corresponding relation between the recommended scanning category and the scanning parameters.
In one embodiment, the apparatus further includes a determining module, configured to determine whether the scan parameter matches the object to be tested according to the object information; if yes, instructing the scanning module 13 to scan the measured object according to the scanning parameters; if not, the instruction determination module 12 determines new scan parameters based on the subject information.
In one embodiment, the training module is further configured to update the scan parameter recommendation model by using the scanning device information, the measured object information, and the new scan parameter as training data.
In one embodiment, the scan parameter recommendation model is a similarity measurement function model, and the determining module 12 is specifically configured to obtain an information list of the objects whose information is the same as that of the scanning device; acquiring information with the maximum similarity to the information of the measured object from the information list by using a similarity measurement function model; and determining the scanning parameter corresponding to the information with the maximum similarity as the scanning parameter of the tested object.
In one embodiment, the number of the information with the maximum similarity is multiple; the determining module 12 is specifically configured to fuse the scanning parameters corresponding to the pieces of information with the largest similarity, and use the fusion result as the scanning parameter of the object to be measured.
In one embodiment, the scanning device information includes model information of the scanning device, and the subject information includes at least one of age information, height information, weight information, and scanning site information of the subject.
For specific limitations of the medical image acquisition apparatus, reference may be made to the above limitations of the medical image acquisition method, which are not described herein again. The modules in the medical image acquisition device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a medical scanning device is provided, which may be a terminal, the internal structure of which may be as shown in fig. 6. The medical scanning device comprises a scanner, a processor, a memory, a network interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the medical scanning device is configured to provide computational and control capabilities. The memory of the medical scanning device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the medical scanning device is used for communicating with an external terminal through network connection. The display screen of the medical scanning device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the medical scanning device can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the medical scanning device, an external keyboard, a touch pad or a mouse and the like. The medical scanning device further comprises a scanner connected via a system bus for scanning a subject to obtain medical images.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a medical scanning device is provided, comprising a memory, a processor and a scanner, the memory storing a computer program, the processor realizing the following steps when executing the computer program:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
and the scanner is used for scanning the tested object according to the scanning parameters to obtain a medical image.
The implementation principle and technical effect of the medical scanning device provided by this embodiment are similar to those of the above method embodiments, and are not described herein again.
In one embodiment, the scan parameter recommendation model is a neural network model, and the processor when executing the computer program further performs the following steps:
acquiring scanning parameter data sets when different scanning equipment scans different tested objects; the scanning parameter data set comprises corresponding relations among scanning equipment information, measured object information and scanning parameters;
and taking the scanning parameter data set as training data, and training an initial scanning parameter recommendation model to obtain a scanning parameter recommendation model.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
inputting the information of the scanning equipment and the information of the object to be detected into a scanning parameter recommendation model to obtain a scanning recommendation category;
and determining the scanning parameters of the tested object according to the recommended scanning category and the corresponding relation between the recommended scanning category and the scanning parameters.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
judging whether the scanning parameters are matched with the tested object or not according to the information of the tested object;
if yes, the scanner scans the measured object according to the scanning parameters;
and if not, determining new scanning parameters according to the measured object information.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and updating the scanning parameter recommendation model by taking the scanning equipment information, the tested object information and the new scanning parameters as training data.
In one embodiment, the scan parameter recommendation model is a similarity metric function model, and the processor when executing the computer program further performs the following steps:
acquiring an information list of a measured object with the same information as the scanning equipment;
acquiring information with the maximum similarity to the information of the measured object from the information list by using a similarity measurement function model;
and determining the scanning parameter corresponding to the information with the maximum similarity as the scanning parameter of the tested object.
In one embodiment, the number of information with the largest similarity is multiple; the processor, when executing the computer program, further performs the steps of:
and fusing the scanning parameters corresponding to the information with the maximum similarity, and taking the fusion result as the scanning parameters of the tested object.
In one embodiment, the scanning device information includes model information of the scanning device, and the subject information includes at least one of age information, height information, weight information, and scanning site information of the subject.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
the scan parameters are sent to the medical scanning device.
The implementation principle and technical effect of the computer-readable storage medium provided by this embodiment are similar to those of the above-described method embodiment, and are not described herein again.
In an embodiment, the scan parameter recommendation model is a neural network model, the computer program, when executed by the processor, further performs the steps of:
acquiring scanning parameter data sets when different scanning equipment scans different tested objects; the scanning parameter data set comprises corresponding relations among scanning equipment information, measured object information and scanning parameters;
and taking the scanning parameter data set as training data, and training an initial scanning parameter recommendation model to obtain a scanning parameter recommendation model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the information of the scanning equipment and the information of the object to be detected into a scanning parameter recommendation model to obtain a scanning recommendation category;
and determining the scanning parameters of the tested object according to the recommended scanning category and the corresponding relation between the recommended scanning category and the scanning parameters.
In one embodiment, the computer program when executed by the processor further performs the steps of:
judging whether the scanning parameters are matched with the tested object or not according to the information of the tested object;
if so, sending the scanning parameters to the medical scanning equipment;
and if not, determining new scanning parameters according to the measured object information.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and updating the scanning parameter recommendation model by taking the scanning equipment information, the tested object information and the new scanning parameters as training data.
In an embodiment, the scan parameter recommendation model is a similarity metric function model, and the computer program, when executed by the processor, further performs the steps of:
acquiring an information list of a measured object with the same information as the scanning equipment;
acquiring information with the maximum similarity to the information of the measured object from the information list by using a similarity measurement function model;
and determining the scanning parameter corresponding to the information with the maximum similarity as the scanning parameter of the tested object.
In one embodiment, the number of information with the largest similarity is multiple; the computer program when executed by the processor further realizes the steps of:
and fusing the scanning parameters corresponding to the information with the maximum similarity, and taking the fusion result as the scanning parameters of the tested object.
In one embodiment, the scanning device information includes model information of the scanning device, and the subject information includes at least one of age information, height information, weight information, and scanning site information of the subject.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as 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 method of medical image acquisition, comprising:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
and scanning the tested object according to the scanning parameters to obtain a medical image.
2. The method of claim 1, wherein the scan parameter recommendation model is a neural network model, and the scan parameter recommendation model is trained in a manner comprising:
acquiring scanning parameter data sets when different scanning equipment scans different tested objects; the scanning parameter data set comprises corresponding relations among scanning equipment information, measured object information and scanning parameters;
and taking the scanning parameter data set as training data, training an initial scanning parameter recommendation model, and obtaining the scanning parameter recommendation model.
3. The method of claim 2, wherein the inputting the scanning device information and the subject information into a preset scanning parameter recommendation model to determine the scanning parameters of the subject comprises:
inputting the scanning equipment information and the measured object information into the scanning parameter recommendation model to obtain a scanning recommendation category;
and determining the scanning parameters of the tested object according to the recommended scanning category and the corresponding relation between the recommended scanning category and the scanning parameters.
4. The method of any of claims 1-3, wherein after determining scan parameters of a subject, the method further comprises:
judging whether the scanning parameters are matched with the tested object or not according to the tested object information;
if yes, executing the step of scanning the tested object according to the scanning parameters;
and if not, determining new scanning parameters according to the measured object information.
5. The method of claim 4, after determining new scan parameters from the subject information, further comprising:
and updating the scanning parameter recommendation model by taking the scanning equipment information, the tested object information and the new scanning parameters as training data.
6. The method of claim 1, wherein the scan parameter recommendation model is a similarity metric function model; the inputting the scanning device information and the object information into a preset scanning parameter recommendation model to determine the scanning parameters of the object includes:
acquiring an information list of a measured object with the same information as the scanning equipment;
acquiring information with the maximum similarity to the measured object information from the information list by using the similarity measurement function model;
and determining the scanning parameter corresponding to the information with the maximum similarity as the scanning parameter of the tested object.
7. The method according to claim 6, wherein the number of the most similar information is plural; the determining the scanning parameter corresponding to the information with the maximum similarity as the scanning parameter of the measured object includes:
and fusing the scanning parameters corresponding to the information with the maximum similarity, and taking the fusion result as the scanning parameters of the tested object.
8. The method of claim 1, wherein the scanning device information comprises model information of the scanning device, and the subject information comprises at least one of age information, height information, weight information, and scanning location information of the subject.
9. A medical scanning device comprising a memory, a processor and a scanner, the memory storing a computer program, characterized in that the processor realizes the following steps when executing the computer program:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
and the scanner is used for scanning the tested object according to the scanning parameters to obtain a medical image.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of:
acquiring scanning equipment information and measured object information;
inputting the scanning equipment information and the measured object information into a preset scanning parameter recommendation model, and determining the scanning parameters of the measured object;
and sending the scanning parameters to a medical scanning device.
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