Image generation method, apparatus, device, and storage medium

Abstract

The application relates to an image generation method, device and equipment and a storage medium. The application comprises the following steps: obtaining a tissue-to-blood concentration ratio corresponding to each scanning time within a first scanning time length; and generating a target tissue uptake rate image of a scanning object according to the tissue-to-blood concentration ratio and a preset mapping relationship. Since the mapping relationship represents the relationship among the scanning time, a blood activity value corresponding to the scanning time and an accumulated blood activity value corresponding to the scanning time, the accumulated blood activity value within a scanning time period corresponding to the scanning time is obtained based on the mapping relationship, so that the target tissue uptake rate image is obtained based on the accumulated blood activity value, the blood activity value and the tissue-to-blood concentration ratio, and long-time continuous scanning of the scanning object is not required.

Description

TECHNICAL FIELD

[0001] The present application relates to the medical technical field, and in particular to an image generation method and device, equipment and a storage medium. BACKGROUND

[0002] K i A parameter image is a macroscopic parameter image commonly used in quantitative analysis in Positron Emission Computed Tomography (PET), representing the uptake rate of a tracer in tissue. This parameter image can provide better lesion detection capability and prediction of lesion conditions.

[0003] Currently, a dynamic PET image is obtained by long-time full dynamic scanning of a scanning object, and K i is obtained based on the dynamic PET image. SUMMARY

[0004] Therefore, it is necessary to provide an image generation method, device, equipment and storage medium capable of reducing scanning time.

[0005] In a first aspect, the present application provides an image generation method, comprising:

[0006] obtaining a tissue-to-blood concentration ratio corresponding to each scanning time within a first scanning time; the tissue-to-blood concentration ratio being a ratio of a tissue activity value to a blood activity value of a scanning object at each scanning time;

[0007] generating a target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and a preset mapping relationship; wherein the mapping relationship is used to represent a mapping relationship among a scanning time, a blood activity value corresponding to the scanning time, and a cumulative blood activity value within a scanning time period corresponding to the scanning time.

[0008] In one embodiment, the generating a target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and a preset mapping relationship comprises:

[0009] obtaining an initial tissue uptake rate image and an initial distribution volume image according to a preset feature extraction model and the tissue-to-blood concentration ratio; the standard time being a ratio between a cumulative blood activity value within a scanning time period corresponding to the scanning time within the first scanning time and a blood activity value corresponding to the scanning time;

[0010] According to the mapping relationship, a standard time corresponding to each scanning time is obtained; and according to the initial tissue uptake rate image, the initial distribution volume image and the standard time, the target tissue uptake rate image is generated.

[0011] In one of the embodiments, the generating the target tissue uptake rate image according to the initial tissue uptake rate image, the initial distribution volume image and the standard time comprises:

[0012] According to the initial tissue uptake rate image, the tissue-to-blood concentration ratio of each tissue and the standard time, an intermediate distribution volume image is obtained;

[0013] According to the intermediate distribution volume image, the initial distribution volume image is corrected to obtain a target distribution volume image;

[0014] According to the initial tissue uptake rate image, the target distribution volume image and the standard time, the target tissue uptake rate image is generated.

[0015] In one of the embodiments, the generating the target tissue uptake rate image according to the initial tissue uptake rate, the target distribution volume and the standard time comprises:

[0016] According to the initial tissue uptake rate image, the target distribution volume image and the standard time, the tissue-to-blood concentration ratio of each tissue is corrected to obtain a corrected tissue-to-blood concentration ratio of each scanning time;

[0017] According to the difference value of the tissue-to-blood concentration ratio of each tissue and the corrected tissue-to-blood concentration ratio, the target tissue uptake rate image is generated.

[0018] In one of the embodiments, the generating the target tissue uptake rate image according to the difference value of the tissue-to-blood concentration ratio of each tissue and the corrected tissue-to-blood concentration ratio comprises:

[0019] In the case that the difference value is greater than a preset difference threshold, the parameters of the feature extraction model are updated according to the difference value, the updated feature extraction model is taken as a feature extraction model, and the step of obtaining the initial tissue uptake rate image and the initial distribution volume image according to the preset feature extraction model and the tissue-to-blood concentration ratio of each tissue is executed until the difference value is less than or equal to the difference threshold;

[0020] In the case that the difference value is less than or equal to the difference threshold, the corresponding initial tissue uptake rate image is taken as the target tissue uptake rate image.

[0021] In one of the embodiments, the generating the target tissue uptake rate image of the scanning object according to the blood concentration ratio of each of the tissues and a preset mapping relationship comprises:

[0022] obtaining a standard time corresponding to each of the scanning time points based on the mapping relationship; the standard time is a ratio between a cumulative blood activity value in a scanning time period corresponding to the scanning time point and a blood activity value corresponding to the scanning time point within the first scanning time length;

[0023] fitting the blood concentration ratio of each of the tissues and the standard time corresponding to the scanning time point to generate the target tissue uptake rate image.

[0024] In one of the embodiments, the obtaining process of the mapping relationship comprises:

[0025] obtaining a sample standard time of each of the scanning time points; the sample standard time is a ratio between a cumulative blood activity value in a scanning time period corresponding to each of the scanning time points and a blood activity value corresponding to the scanning time point within a second scanning time length; the second scanning time length is greater than the first scanning time length;

[0026] fitting a plurality of the scanning time points and the sample standard time corresponding to the scanning time point to obtain the mapping relationship.

[0027] In a second aspect, the present application further provides an image generation device, comprising:

[0028] a first obtaining module, configured to obtain a blood concentration ratio of each of the tissues corresponding to each of the scanning time points within a first scanning time length; the blood concentration ratio of each of the tissues is a ratio between a tissue activity value and a blood activity value of a scanning object at each of the scanning time points;

[0029] a generating module, configured to generate a target tissue uptake rate image of the scanning object according to the blood concentration ratio of each of the tissues and a preset mapping relationship; the mapping relationship is used to represent a mapping relationship between a scanning time point, a blood activity value corresponding to the scanning time point and a cumulative blood activity value in a scanning time period corresponding to the scanning time point.

[0030] In a third aspect, the present application further provides a computer device, comprising a memory and a processor; the memory stores a computer program; and the processor implements the following steps when executing the computer program:

[0031] obtaining a blood concentration ratio of each of the tissues corresponding to each of the scanning time points within a first scanning time length; the blood concentration ratio of each of the tissues is a ratio between a tissue activity value and a blood activity value of a scanning object at each of the scanning time points;

[0032] According to the blood concentration ratio of each tissue and a preset mapping relationship, a target tissue uptake rate image of the scanning object is generated; wherein the mapping relationship is used to represent the mapping relationship among the scanning time, the blood activity value corresponding to the scanning time and the cumulative blood activity value in the scanning time period corresponding to the scanning time.

[0033] In a fourth aspect, the present application further provides a computer readable storage medium, which has a computer program stored thereon, and the computer program is executed by a processor to implement the following steps:

[0034] The blood concentration ratio of each tissue corresponding to each scanning time in the first scanning duration is acquired; each blood concentration ratio of the tissue is a ratio of the tissue activity value of the scanning object to the blood activity value at each scanning time;

[0035] According to the blood concentration ratio of each tissue and a preset mapping relationship, a target tissue uptake rate image of the scanning object is generated; wherein the mapping relationship is used to represent the mapping relationship among the scanning time, the blood activity value corresponding to the scanning time and the cumulative blood activity value in the scanning time period corresponding to the scanning time.

[0036] In a fifth aspect, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the following steps:

[0037] The blood concentration ratio of each tissue corresponding to each scanning time in the first scanning duration is acquired; each blood concentration ratio of the tissue is a ratio of the tissue activity value of the scanning object to the blood activity value at each scanning time;

[0038] According to the blood concentration ratio of each tissue and a preset mapping relationship, a target tissue uptake rate image of the scanning object is generated; wherein the mapping relationship is used to represent the mapping relationship among the scanning time, the blood activity value corresponding to the scanning time and the cumulative blood activity value in the scanning time period corresponding to the scanning time.

[0039] The image generation method, device, equipment and storage medium described above, by obtaining the tissue-to-blood concentration ratio corresponding to each scanning moment in the first scanning duration, generating the target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and the preset mapping relationship; wherein the tissue-to-blood concentration ratio is the ratio of the tissue activity value to the blood activity value of the scanning object at each scanning moment, and the mapping relationship is used to represent the mapping relationship among the scanning moment, the blood activity value corresponding to the scanning moment and the cumulative blood activity value in the scanning time period corresponding to the scanning moment. Since the mapping relationship represents the relationship among the scanning moment, the blood activity value corresponding to the scanning moment and the cumulative blood activity value corresponding to the scanning moment, the cumulative blood activity value in the scanning time period corresponding to the scanning moment is obtained based on the mapping relationship in the embodiment of the application, so that the target tissue uptake rate image is obtained based on the cumulative blood activity value, the blood activity value and the tissue-to-blood concentration ratio, and long-time continuous scanning of the scanning object is not needed. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0041] Figure 1 An application environment diagram of the image generation method in an embodiment;

[0042] Figure 2 A flowchart of the image generation method in an embodiment;

[0043] Figure 3 A schematic diagram of the target tissue uptake rate image in an embodiment;

[0044] Figure 4 A schematic diagram of the target tissue uptake rate image in another embodiment;

[0045] Figure 5 A flowchart of the image generation method in an embodiment;

[0046] Figure 6 A flowchart of the target tissue uptake rate image generation method in an embodiment;

[0047] Figure 7 A flowchart of the target tissue uptake rate image generation method in another embodiment;

[0048] Figure 8Flowchart of the method for generating a target tissue uptake rate image in another embodiment;

[0049] Figure 9 Flowchart of the method for obtaining a mapping relationship in an embodiment;

[0050] Figure 10 Diagram of the mapping relationship in an embodiment;

[0051] Figure 11 Diagram of the mapping relationship in another embodiment;

[0052] Figure 12 Diagram of the mapping relationship in another embodiment;

[0053] Figure 13 Diagram of the mapping relationship in another embodiment;

[0054] Figure 14 Flowchart of the method for generating an image in another embodiment;

[0055] Figure 15 Structural block diagram of the image generation device in an embodiment. DETAILED DESCRIPTION

[0056] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed descriptions will be given below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The biggest advantage of PET imaging compared with other imaging is that it can be quantitatively analyzed, but accurate quantitative analysis requires long-time acquisition. Even healthy people are difficult to maintain stillness for such a long time, and it also increases the scanning cost and reduces the turnover rate of the hospital. In clinical diagnosis, because the patient cannot be scanned for a long time, the standard uptake value (SUV) is widely used for semi-quantitative analysis in conventional PET imaging, but the distribution of PET tracers is a dynamic process, and many factors will interfere with SUV, thereby affecting the clinical diagnosis. Compared with semi-quantitative analysis parameters such as SUV, is a commonly used macroscopic parameter in quantitative analysis, which represents the comprehensive uptake of drugs in tissues. This parameter can provide better lesion detection capability.

[0058] PET scanning is a highly sensitive non-invasive method for studying in vivo conditions of human physiology, metabolism and molecular pathways. Through dynamic PET imaging, the uptake rate constant K obtained by analyzing the PET image under the F-FDG tracer can be used to calculate the value of the parameter, which is a commonly used macroscopic parameter in quantitative analysis, representing the comprehensive uptake of drugs in tissues. This parameter can provide better lesion detection capability. 18 F-fluorodeoxyglucose (F-FDG) tracer 18 PET image analysisi Parametric images, for 18 F-FDG uptake provides fine quantification. In addition, K i Parametric images excel in differentiating benign and malignant lesions, improving lesion detection capability, and can play a key role in guiding treatment decisions and designing clinical trials.

[0059] However, using 18 F-FDG PET to generate K i Parametric images usually require more than 60 minutes of scanning time, which can cause patient discomfort and increase the risk of motion-related artifacts in the image. In addition, these extended scanning times can also reduce the turnover rate of the device, resulting in increased operating costs and separately increasing the economic burden on patients. Therefore, the present application proposes an image generation method, device, equipment and storage medium which can reduce the scanning time.

[0060] The image generation method provided by the embodiments of the present application can be applied to the application environment as shown in Figure 1 The application environment includes a computer device, which can be a server, and the internal structure diagram of the computer device can be as shown in Figure 1 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes 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 operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store image generation related data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement an image generation method. The server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0061] Those skilled in the art can understand that Figure 1 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0062] In one exemplary embodiment, such as Figure 2 As shown, an image generation method is provided, which can be applied to... Figure 1 The following explanation uses a computer device as an example, including the following steps S201 to S202. Wherein:

[0063] S201, obtain the tissue-to-blood concentration ratio corresponding to each scanning moment within the first scanning duration; the tissue-to-blood concentration ratio is the ratio of the tissue activity value to the blood activity value of the scanned object at each scanning moment.

[0064] The first scan duration can be 50-60 minutes, 20-30 minutes, 30-60 minutes, etc., after the tracer is injected.

[0065] In this embodiment, the tissue activity value at each scanning moment can be obtained from the PET images obtained after scanning the object. Since the first scanning duration is performed, multiple frames of PET images are obtained. For each scanning moment, the tissue activity value corresponding to each pixel can be obtained based on the PET image corresponding to that scanning moment. Blood activity value can also be obtained based on PET image analysis.

[0066] Using blood activity values ​​at each scan time The tissue activity values ​​at the corresponding scan time are normalized to obtain the tissue-to-blood concentration ratio. This is because the tissue activity value includes the tissue activity value corresponding to each pixel in the PET image. Therefore, the tissue-to-blood concentration ratio at each scan time includes the tissue-to-blood concentration ratio corresponding to each pixel. That is, the tissue-to-blood concentration ratio of a pixel can be expressed as... .

[0067] S202, Based on the blood concentration ratio of each tissue and the preset mapping relationship, generate the target tissue uptake rate image of the scanned object; wherein, the mapping relationship is used to characterize the mapping relationship between the scan time, the blood activity value corresponding to the scan time, and the cumulative blood activity value within the scan time period corresponding to the scan time.

[0068] Patlak graphical analysis is a kinetic model commonly used in medical imaging, particularly in positron emission tomography (PET), to quantify the uptake and metabolism of certain tracers by tissues. Therefore, K can be obtained based on Patlak graphical analysis. i Parametric image, i.e., based on The uptake rate of the target tissue, K i v is the tissue uptake rate parameter corresponding to a single pixel. i Let K be the distribution volume corresponding to each pixel, and 0-t be the scanning time period. iAfter obtaining the parameters, K is obtained. i Parametric image. Assuming a 60-minute scan, K is obtained based on the Patlak graphical analysis method. i Parameter image as Figure 3 As shown.

[0069] Since this application performs PET scanning based on the first scan duration, which is shorter than the complete scan duration, the short scan duration cannot obtain the cumulative blood activity value within the 0-t scan time period.

[0070] The mapping relationship is used to characterize the mapping relationship between the scan time, the blood activity value corresponding to the scan time, and the cumulative blood activity value within the scan time period corresponding to the scan time. Since the blood activity value corresponding to the scan time is known, substituting the scan time into the mapping relationship yields the cumulative blood activity value corresponding to the scan time, thus obtaining the aforementioned result. This is the ratio of blood concentration integrated to instantaneous concentration, or it can also be called normalized time. Therefore, the mapping relationship can be used as a representation of the ratio of blood concentration integrated to instantaneous concentration. For example, if the first scan duration is 20-60 minutes after the tracer injection, and the blood activity value at 20 minutes is known, the cumulative blood activity value within 0-20 minutes can be obtained based on the mapping relationship.

[0071] In this embodiment, a mapping relationship is used to obtain the ratio of the cumulative blood activity value at each scanning time to the blood activity value at each scanning time, and this ratio is used as the standard time. Since a first scanning duration is scanned, for each pixel, the tissue-to-blood concentration ratio at multiple scanning times within the first scanning duration is used. By fitting the multiple tissue-to-blood concentration ratios with the standard time of the corresponding scanning time, the target tissue uptake rate K corresponding to that pixel can be obtained. i Thus, the uptake rate K of the target tissue is obtained. i The parametric image, i.e., the target tissue uptake rate image. K i The results of the parametric image are as follows Figure 4 As shown, it can be seen that scanning for 40 minutes is related to the above. Figure 3 K obtained from a 60-minute scan i The parameter images are basically consistent, and the differences are negligible.

[0072] In another possible implementation, multiple tissue-to-blood concentration ratios can be input into the feature extraction network model to obtain an initial tissue uptake rate image and an initial distribution volume image. The feature extraction network is then updated based on multiple standard times, the initial distribution volume image, and the initial tissue uptake rate image to obtain the target tissue uptake rate image.

[0073] In the image generation method, a tissue blood concentration ratio corresponding to each scanning moment in a first scanning duration is obtained, and a target tissue uptake rate image of the scanning object is generated according to the tissue blood concentration ratio and a preset mapping relationship. The tissue blood concentration ratio is a ratio of a tissue activity value of the scanning object at each scanning moment to a blood activity value, and the mapping relationship is used to represent a mapping relationship among the scanning moment, the blood activity value corresponding to the scanning moment, and an accumulated blood activity value in a scanning time period corresponding to the scanning moment. Since the mapping relationship represents the relationship among the scanning moment, the blood activity value corresponding to the scanning moment, and the accumulated blood activity value corresponding to the scanning moment, the accumulated blood activity value in the scanning time period corresponding to the scanning moment is obtained based on the mapping relationship, so that the target tissue uptake rate image is obtained based on the accumulated blood activity value, the blood activity value, and the tissue blood concentration ratio, without the need for long-time continuous scanning of the scanning object.

[0074] In one embodiment, generating the target tissue uptake rate image of the scanning object according to the tissue blood concentration ratio and the preset mapping relationship includes: obtaining an initial tissue uptake rate image and an initial distribution volume image according to a preset feature extraction model and the tissue blood concentration ratio; obtaining a standard time corresponding to each scanning moment based on the mapping relationship; and generating the target tissue uptake rate image according to the initial tissue uptake rate image, the initial distribution volume image, and the standard time.

[0075] The standard time is a ratio of the accumulated blood activity value in the scanning time period corresponding to the scanning moment to the blood activity value corresponding to the scanning moment in the first scanning duration, that is, an integral blood concentration ratio, or also referred to as a normalized time.

[0076] Optionally, the feature extraction model can be a recurrent neural network, or a machine learning algorithm, etc.

[0077] In the embodiment of the present application, as shown in Figure 5 , the feature extraction model can include an input layer (a circle shown in Figure 5 ), a bidirectional long short-term memory layer BiLSTM (a square shown in Figure 5 ), and a fully connected layer (an octagon shown in Figure 5 ). The BiLSTM layer is configured to have 2-dimensional LSTM units in a hidden layer. The tissue blood concentration ratio at each scanning moment is input into the BiLSTM layer to extract the time feature of the tissue blood concentration ratio, to obtain a time feature vector. The time feature vector is input into the fully connected layer, and the fully connected layer is used to perform dimension conversion and other processing on the time feature vector, to output an initial tissue uptake rate K i0 and an initial distribution volume v i0, i.e. the initial tissue uptake rate image and the initial distribution volume image.

[0078] Assuming that the mapping relationship is a linear mapping relationship, the mapping relationship is represented as , a is the slope of the mapping relationship, and b is the intercept of the mapping relationship. Based on the mapping relationship, the ratio between the cumulative blood activity value in the scanning time period corresponding to each scanning time and the blood activity value corresponding to each scanning time, i.e. the standard time, can be obtained.

[0079] Further, the blood-to-tissue concentration ratio can be corrected according to the initial tissue uptake rate image, the initial distribution volume image, and each standard time to obtain a corrected blood-to-tissue concentration ratio. For example, the blood-to-tissue concentration ratio of the first pixel point at the 20th minute can be corrected by using the initial tissue uptake rate parameter of the first pixel point in the initial tissue uptake rate image, the initial distribution volume parameter of the first pixel point in the initial distribution volume image, and the standard time at the 20th minute to obtain the corrected blood-to-tissue concentration ratio. Thus, the parameter of the feature extraction network model is iteratively updated according to each corrected blood-to-tissue concentration ratio and each blood-to-tissue concentration ratio to output the target tissue uptake rate image.

[0080] In another possible implementation, the intermediate distribution volume image can also be obtained according to the initial tissue uptake rate image, the initial distribution volume image, and each standard time, the initial distribution volume image is corrected according to the intermediate distribution volume image to obtain a target distribution volume image, and the target tissue uptake rate image is further generated according to the initial tissue uptake rate image, the target distribution volume image, and each standard time.

[0081] In the embodiments of the present application, the initial tissue uptake rate image and the initial distribution volume image are obtained according to the preset feature extraction model and the blood-to-tissue concentration ratio, the standard time corresponding to each scanning time is obtained based on the mapping relationship, and the target tissue uptake rate image is generated according to the initial tissue uptake rate image, the initial distribution volume image, and each standard time. Since the PET image can contain redundant information such as noise, the target tissue uptake rate image is obtained by using the feature extraction model based on the mapping relationship, so that a high-quality target tissue uptake rate image can be obtained while reducing the scanning time.

[0082] Figure 6 The flowchart of the method for generating the target tissue uptake rate image in one embodiment is as follows: Figure 6As shown, this application embodiment relates to a possible implementation of how to generate a target tissue uptake rate image based on an initial tissue uptake rate image, an initial distribution volume image, and various standard times, including the following steps:

[0083] S601, based on the initial tissue uptake rate image, the ratio of each tissue to blood concentration, and each standard time, obtain the intermediate distribution volume image.

[0084] In this embodiment of the application, the average tissue-to-blood concentration ratio is obtained by averaging the tissue-to-blood concentration ratio corresponding to each scanning time at each pixel point, as described above. Figure 5 Taking the mapping relationship in the embodiment as an example, the standard time at each scanning moment can be obtained based on the mapping relationship. The average standard time is then calculated by averaging the standard times at each scanning moment. For each pixel in the initial tissue uptake rate image, the product of the initial tissue uptake rate parameter and the average standard time for each pixel in the initial tissue uptake rate image is obtained. By subtracting the product of the corresponding pixel from the average tissue-to-blood concentration ratio for each pixel, the intermediate distribution volume parameter for each pixel can be obtained, thus obtaining the intermediate distribution volume image. For example, if the first scan duration is 50-60 minutes after the tracer injection, the standard time corresponding to each scanning moment in the 50-60 minute period can be obtained according to the mapping relationship. The average standard time is then calculated by averaging the standard times. The product of the initial tissue uptake rate parameter and the average standard time for each pixel in the initial tissue uptake rate image is obtained. Further, by subtracting the product of the corresponding pixel from the average tissue-to-blood concentration ratio for each pixel in the 50-60 minute period, the intermediate distribution volume parameter for each pixel is obtained, i.e., the intermediate distribution volume image.

[0085] The intermediate distribution volume parameters of each pixel in the above intermediate distribution volume image It can be represented as ,in, The average tissue-to-blood concentration ratio for each pixel. This is the average standard time.

[0086] S602, Correct the initial distribution volume image based on the intermediate distribution volume image to obtain the target distribution volume image.

[0087] In this embodiment, the target distribution volume image can be obtained by averaging the corresponding pixels in the intermediate distribution volume image and the initial distribution volume image. Specifically, for each pixel in the initial distribution volume image, the average of the intermediate distribution volume parameter and the initial distribution volume parameter for that pixel is obtained, and this average is used as the target distribution volume parameter for that pixel. This will yield the target distribution volume image, i.e. .

[0088] In a possible implementation, the intermediate distribution volume image and the initial distribution volume image can also be fused by weighting to obtain the target distribution volume image.

[0089] S603, generating a target tissue uptake rate image based on the initial tissue uptake rate image, the target distribution volume image and the standard times.

[0090] In the embodiment of the present application, for each pixel point, the tissue-to-blood concentration ratio of the pixel point at each scanning time can be corrected based on the initial tissue uptake rate parameter of the corresponding pixel point, the target distribution volume parameter and the standard times of each scanning time to obtain the corrected tissue-to-blood concentration ratio. According to the difference value of the corrected tissue-to-blood concentration ratio of each pixel point at each scanning time and the corresponding tissue-to-blood concentration ratio, the target tissue uptake rate image is generated.

[0091] In a possible implementation, the initial tissue uptake rate image can also be processed by optimization according to the target distribution volume image and the standard times to obtain the target tissue uptake rate image.

[0092] In the embodiment of the present application, the intermediate distribution volume image is obtained according to the initial tissue uptake rate image, the tissue-to-blood concentration ratios and the standard times, the initial distribution volume image is corrected according to the intermediate distribution volume image to obtain the target distribution volume image, and the target tissue uptake rate image is generated based on the initial tissue uptake rate image, the target distribution volume image and the standard times. In the embodiment of the present application, since the feature extraction network has two convergence nodes, i.e., the tissue uptake rate convergence node and the distribution volume convergence node, the initial distribution volume image is corrected by using the intermediate distribution volume image, and the distribution volume parameter output by the feature extraction model is aligned with the tissue uptake rate derived from the Patlak equation, so that the convergence of the feature extraction network model is mainly affected by the initial tissue uptake rate image, and the quality of the generated target tissue uptake rate image is improved.

[0093] Figure 7 For another flowchart of the target tissue uptake rate image generation method in the embodiment, as shown in Figure 7 the present application relates to a possible implementation of how to generate a target tissue uptake rate image based on initial tissue uptake rates, target distribution volumes and standard times, which includes the following steps:

[0094] S701, correcting the tissue-to-blood concentration ratios to obtain the corrected tissue-to-blood concentration ratios at each scanning time based on the initial tissue uptake rate image, the target distribution volume image and the standard times.

[0095] In the embodiment of the present application, for each pixel point, the tissue-to-blood concentration ratio of the pixel point at each scanning time can be corrected based on the initial tissue uptake rate parameter of the corresponding pixel point, the target distribution volume parameter and the standard time of each scanning time, to obtain the corrected tissue-to-blood concentration ratio Y(t). That is, it can be expressed as .

[0096] S702, generating a target tissue uptake rate image according to the difference value between the tissue-to-blood concentration ratio and the corresponding corrected tissue-to-blood concentration ratio.

[0097] In the embodiment of the present application, the parameters of the feature extraction model can be optimized according to the difference value between the tissue-to-blood concentration ratio and the corrected tissue-to-blood concentration ratio, to obtain a new feature extraction model, and the target tissue uptake rate image is obtained based on the new feature extraction model and the tissue-to-blood concentration ratio at each scanning time. Or, according to the difference value between the tissue-to-blood concentration ratio and the corrected tissue-to-blood concentration ratio, the initial tissue uptake rate image is optimized to obtain the target tissue uptake rate image.

[0098] Specifically, generating a target tissue uptake rate image according to the difference value between the tissue-to-blood concentration ratio and the corrected tissue-to-blood concentration ratio includes: in the case that the difference value is greater than a preset difference threshold, updating the parameters of the feature extraction model according to each difference value, taking the updated feature extraction model as the feature extraction model, and returning to execute the step of obtaining the initial tissue uptake rate image and the initial distribution volume image according to the preset feature extraction model and the tissue-to-blood concentration ratio until the difference value is less than or equal to the difference threshold; in the case that the difference value is less than or equal to the difference threshold, taking the corresponding initial tissue uptake rate image as the tissue uptake rate image.

[0099] In the embodiment of the present application, the parameters of the feature extraction model are updated according to each difference value, the updated feature extraction model is taken as the feature extraction model, and the tissue-to-blood concentration ratio at each scanning time is continuously input into the feature extraction model (i.e. the updated feature extraction model), and the above-mentioned step of obtaining the initial tissue uptake rate image and the initial distribution volume image by using the feature extraction model and the tissue-to-blood concentration ratio is continuously repeated. If the difference value is less than or equal to the preset difference threshold, the initial tissue uptake rate image output by the feature extraction model (i.e. the updated feature extraction model) this time is taken as the target tissue uptake rate image. If the difference value is greater than the preset difference threshold, the feature extraction network model is continuously updated until the difference value is less than or equal to the preset difference threshold. Continue with the above Figure 3 For example, the tracer is 18F-FDG, using the method proposed in the embodiments of the present application, the PET image (i.e. tissue activity value and blood activity value) obtained from the short dynamic PET scan can generate K i parameter image without using any long dynamic PET image for training. Unlike the Patlak graphical analysis method which requires the time activity curve (TAC) of blood obtained during the entire scan, i.e. the blood activity value during the entire scan, the method proposed in the embodiments of the present application only needs a short blood TAC.

[0100] Optionally, the feature extraction model can be updated using a mean-square error (MSE) loss function, and the MSE loss function is defined as follows:

[0101] , N is the total number of tissue-to-blood concentration ratios.

[0102] In the embodiments of the present application, based on the initial tissue uptake rate image, the target distribution volume image and each standard time, the tissue-to-blood concentration ratio of each tissue is corrected to obtain the corrected tissue-to-blood concentration ratio at each scan time, and the target tissue uptake rate image is generated according to the difference value between the tissue-to-blood concentration ratio and the corresponding corrected tissue-to-blood concentration ratio. In the actual application process of the present application, the difference value between the tissue-to-blood concentration ratio and the corresponding corrected tissue-to-blood concentration ratio is used for self-supervised training to obtain the target tissue uptake rate image. The feature extraction model does not need to be pre-trained using any long dynamic PET data, and a large amount of training data is not needed, which avoids the situation that the pre-trained model cannot be applied to any scan object, and improves the flexibility of generating the target tissue uptake rate image.

[0103] Figure 8 The flowchart of the method for generating the target tissue uptake rate image in another embodiment is shown in FIG. 8, and the embodiments of the present application relate to another possible implementation manner for generating the target tissue uptake rate image of the scan object according to the mapping relationship between the tissue-to-blood concentration ratio and the preset mapping relationship, which includes the following steps: Figure 8

[0104] S801, based on the mapping relationship, obtain the corresponding standard time at each scan time; the standard time is the ratio between the cumulative blood activity value in the scan time period corresponding to the scan time within the first scan time and the blood activity value corresponding to the scan time.

[0105] ​In the embodiment of the present application, since the mapping relationship is used to represent the relationship among the scanning time, the blood activity value corresponding to the scanning time, and the cumulative blood activity value in the scanning time period corresponding to the scanning time, the cumulative blood activity value in the scanning time period corresponding to the scanning time can be obtained by combining the scanning time with the mapping relationship, so that the standard time corresponding to the scanning time is obtained based on the cumulative blood activity value and the blood activity value corresponding to the scanning time.

[0106] S802, fitting the blood concentration ratio of each tissue and the standard time corresponding to the scanning time to generate a target tissue uptake rate image.

[0107] In the embodiment of the present application, for each pixel point, the blood concentration ratio of each scanning time and the standard time corresponding to the scanning time are fitted to obtain the target tissue uptake rate parameter of the pixel point, so as to obtain the target tissue uptake rate image.

[0108] In the embodiment of the present application, based on the mapping relationship, the standard time corresponding to each scanning time is obtained, which can reduce the scanning time, and the cumulative blood activity value corresponding to each scanning time and the ratio of the blood activity value of each scanning time can be obtained without long-time scanning. Further, the blood concentration ratio of each tissue and the standard time corresponding to the scanning time are fitted to generate a target tissue uptake rate image, so that the target tissue uptake rate image generation is simpler, and the efficiency of the target tissue uptake rate image generation is improved.

[0109] Figure 9 For an embodiment of the mapping relationship acquisition method, as shown in the flowchart of the mapping relationship acquisition method in FIG. 8, the method comprises the following steps: Figure 9

[0110] S901, obtaining the sample standard time of each scanning time; the sample standard time is the ratio of the cumulative blood activity value in the scanning time period corresponding to each scanning time in the second scanning time to the blood activity value corresponding to the scanning time; the second scanning time is longer than the first scanning time.

[0111] S902, fitting the multiple scanning times and the sample standard time corresponding to the scanning time to obtain the mapping relationship.

[0112] The second scanning time is the time required for a complete scan of the patient, and the second scanning time is different when the tracer is different. For example, when the tracer is F-FDG, the second scanning time is generally 60 minutes to 180 minutes. 18

[0113] In the embodiment of the present application, the fitting of the multiple scanning times and the sample standard time corresponding to the scanning time can be linear fitting, polynomial fitting, logarithmic fitting, or polynomial fitting. ​​

[0114] As Figure 10 shown, the tracer is 18 F-FDG, fitting the sample standard time of multiple scanning time and corresponding sample standard time within the second scanning time length, it can be found that within 20-60 minutes after injecting the tracer, the sample standard time and time are linearly related, based on this finding, the sample standard time in all data is statistically analyzed and linearly fitted to obtain the mapping relationship based on multiple reference objects, that is, the mapping relationship is the statistical analysis of the sample normalization time in all data, and the expression of population-based normalization time (PBNT) obtained by linear fitting is obtained.

[0115] If the first scanning time length is the scanning time length after 20 minutes after injecting the tracer, the standard time of each scanning time can be obtained by using the linearly fitted mapping relationship; if the first scanning time length is the scanning time length before 20 minutes after injecting the tracer

[0116] , the standard time of each scanning time needs to be obtained based on the mapping relationship fitted before 20 minutes.

[0117] Figure 10 To fit the sample standard time and scanning time of 25 reference objects, the fitting line represented by the dashed line represents the fitting result of each reference object, and 25 fitting lines are obtained. For each fitting line, the determination coefficient (R²) of each fitting line is 0.99 ± 0.01, indicating that the fitting result of each reference object is highly linear. The coefficient of variation (CV) between the sample standard time of each reference object and the fitting line is 1.01%, which proves that the distribution interval of each sample standard time of each reference object is small. After averaging these fitting lines, the sample standard time can be modeled as a function of time by linear fitting, and the mapping relationship can be represented as . The slope a is in the range of 1.67 ± 0.13 (CV = 7.78%), and the intercept b is in the range of -5.84 ± 3.07 (CV = 52.57%), and the obtained mapping relationship is Figure 10 , as shown by the solid line in the middle, preferably, the mapping relationship obtained based on linear simulation can be represented as .

[0118] Further, Figure 11-13 PBNTs constructed under three different tracers are respectively shown, which are FDG tracer, PSMA tracer and Methionine tracer, it can be found that when the state of the tracer is irreversible, Figure 10The PBNT formula obtained in the above method is correct.

[0119] Therefore, the construction method of the mapping relationship can also be integrated into the workstation of the PET device, replacing the traditional method of obtaining the cumulative blood activity value. Even the PET data of a short scan length can be used for quantitative analysis of the tissue uptake rate image. Or, using the PBNT, the traditional short-axis PET data can be used for quantitative analysis of the tissue uptake rate image without scanning the heart to obtain the blood activity value.

[0120] In the embodiment of the present application, the sample standard time of each scanning time is obtained, and the mapping relationship is obtained by fitting the plurality of scanning times and the sample standard time corresponding to the scanning time, thereby laying a foundation for obtaining the target tissue uptake rate image based on the mapping relationship.

[0121] Figure 14 For another embodiment of the image generation method, as shown in the flowchart of the image generation method, Figure 14 as shown, the method comprises the following steps:

[0122] S1401, obtaining the tissue-to-blood concentration ratio corresponding to each scanning time in the first scanning length; the tissue-to-blood concentration ratio is the ratio of the tissue activity value to the blood activity value of the scanning object at each scanning time;

[0123] S1402, obtaining the initial tissue uptake rate image and the initial distribution volume image according to the preset feature extraction model and the tissue-to-blood concentration ratio;

[0124] S1403, obtaining the standard time corresponding to each scanning time based on the mapping relationship; the standard time is the ratio between the cumulative blood activity value in the scanning time period corresponding to the scanning time in the first scanning length and the blood activity value corresponding to the scanning time;

[0125] S1404, obtaining the intermediate distribution volume image according to the initial tissue uptake rate image, the tissue-to-blood concentration ratio, and the standard time;

[0126] S1405, correcting the initial distribution volume image according to the intermediate distribution volume image to obtain the target distribution volume image;

[0127] S1406, correcting the tissue-to-blood concentration ratio based on the initial tissue uptake rate image, the target distribution volume image, and the standard time to obtain the corrected tissue-to-blood concentration ratio of each scanning time;

[0128] S1407, in the case where the difference between the tissue-to-blood concentration ratio and the corresponding corrected tissue-to-blood concentration ratio is greater than the preset difference threshold, updating the parameters of the feature extraction model according to the difference, and taking the updated feature extraction model as the feature extraction model.

[0129] S1408, in the case where the difference value is less than or equal to the difference threshold, taking the corresponding initial tissue uptake rate image as the target tissue uptake rate image;

[0130] S1409, based on the mapping relationship, obtaining a standard time corresponding to each scanning time; the standard time is a ratio between a cumulative blood activity value in a scanning time period corresponding to the scanning time within the first scanning time length and the blood activity value corresponding to the scanning time;

[0131] S1410, fitting the tissue-to-blood concentration ratio and the standard time corresponding to the scanning time to generate the target tissue uptake rate image.

[0132] Since the mapping relationship represents the relationship among the scanning time, the blood activity value corresponding to the scanning time, and the cumulative blood activity value corresponding to the scanning time, the embodiment of the present application obtains the cumulative blood activity value in the scanning time period corresponding to the scanning time based on the mapping relationship, so as to obtain the target tissue uptake rate image based on the cumulative blood activity value, the blood activity value, and the tissue-to-blood concentration ratio of the plurality of tissues, without the need for long-time continuous scanning of the scanning object.

[0133] It should be understood that, although each step in the flowchart involved in each of the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0134] Based on the same inventive concept, the embodiment of the present application also provides an image generation device for implementing the above-mentioned image generation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more image generation device embodiments provided below can refer to the limitations of the image generation method in the above text, and will not be repeated here.

[0135] In one exemplary embodiment, as shown in Figure 15 An image generation device is provided, comprising: a first acquisition module 11 and a generation module 12, wherein:

[0136] The first acquisition module 11 is configured to acquire a tissue-to-blood concentration ratio corresponding to each scanning moment in a first scanning duration; the tissue-to-blood concentration ratio is a ratio of a tissue activity value to a blood activity value of the scanning object at each scanning moment;

[0137] The generation module 12 is configured to generate a target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and a preset mapping relationship; the mapping relationship is used to represent a mapping relationship among a scanning moment, a blood activity value corresponding to the scanning moment, and a cumulative blood activity value in a scanning time period corresponding to the scanning moment.

[0138] In one embodiment, the generation module 12 is specifically configured to acquire an initial tissue uptake rate image and an initial distribution volume image according to a preset feature extraction model and the tissue-to-blood concentration ratio; acquire a standard time corresponding to each scanning moment based on the mapping relationship; and generate the target tissue uptake rate image according to the initial tissue uptake rate image, the initial distribution volume image, and the standard time; the standard time is a ratio between the cumulative blood activity value in the scanning time period corresponding to the scanning moment and the blood activity value corresponding to the scanning moment in the first scanning duration.

[0139] In one embodiment, the generation module 12 is specifically configured to acquire an intermediate distribution volume image according to the initial tissue uptake rate image, the tissue-to-blood concentration ratio, and the standard time.

[0140] The initial distribution volume image is corrected according to the intermediate distribution volume image to acquire a target distribution volume image.

[0141] The target tissue uptake rate image is generated based on the initial tissue uptake rate image, the target distribution volume image, and the standard time.

[0142] In one embodiment, the generation module 12 is specifically configured to correct the tissue-to-blood concentration ratio based on the initial tissue uptake rate image, the target distribution volume image, and the standard time to obtain a corrected tissue-to-blood concentration ratio of each scanning moment.

[0143] The target tissue uptake rate image is generated according to a difference value between the tissue-to-blood concentration ratio and the corrected tissue-to-blood concentration ratio.

[0144] In one embodiment, the generation module 12 is specifically configured to take the initial tissue uptake rate image as the target tissue uptake rate image in a case where the difference value is less than or equal to a preset difference threshold.

[0145] In a case where the difference value is greater than the preset difference threshold, parameters of the feature extraction model are updated according to the difference value, the updated feature extraction model is taken as the feature extraction model, and the step of obtaining the initial tissue uptake rate image and the initial distribution volume image according to the preset feature extraction model and the blood concentration ratio of each tissue is executed until the difference value is less than or equal to the difference threshold.

[0146] In one embodiment, the generating module 12 is specifically configured to obtain a standard time corresponding to each scanning time based on the mapping relationship; the standard time is a ratio between a cumulative blood activity value in a scanning time period corresponding to the scanning time within a first scanning duration and a blood activity value corresponding to the scanning time;

[0147] The standard time of each tissue and the blood concentration ratio corresponding to the scanning time are fitted to generate a target tissue uptake rate image.

[0148] In one embodiment, the image generating apparatus further includes:

[0149] The second obtaining module is configured to obtain a sample standard time of each scanning time; the sample standard time is a ratio between a cumulative blood activity value in a scanning time period corresponding to each scanning time within a second scanning duration and a blood activity value corresponding to the scanning time; and the second scanning duration is greater than the first scanning duration.

[0150] The fitting module is configured to fit the sample standard time of each scanning time and the corresponding scanning time to obtain the mapping relationship.

[0151] Each module in the image generating apparatus described above can be realized by software, hardware, and a combination thereof in whole or in part. Each module described above can be embedded in or independent of a processor in a computer device in a hardware form, or can be stored in a memory in a computer device in a software form, so as to be called and executed by a processor to perform operations corresponding to each module.

[0152] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory stores a computer program, and the processor implements the steps of any of the method embodiments described above when executing the computer program.

[0153] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program implements the steps of any of the method embodiments described above when executed by a processor.

[0154] In one embodiment, a computer program product is provided, including a computer program, and the computer program implements the steps of any of the method embodiments described above when executed by a processor.

[0155] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0156] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. In the embodiments provided in the present application, any reference to memory, database or other medium can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0157] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, any combination of these technical features is deemed to be within the scope of the present application.

[0158] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An image generation method characterized by, The method comprises: obtaining a tissue-to-blood concentration ratio corresponding to each scanning time within a first scanning duration; the tissue-to-blood concentration ratio is a ratio of a tissue activity value to a blood activity value of a scanning object at each scanning time; generating a target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and a preset mapping relationship; the mapping relationship is used to represent a mapping relationship among a scanning time, a blood activity value corresponding to the scanning time, and a cumulative blood activity value within a scanning time period corresponding to the scanning time.

2. The method of claim 1, wherein, The generating of the target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and the preset mapping relationship comprises: obtaining an initial tissue uptake rate image and an initial distribution volume image according to a preset feature extraction model and the tissue-to-blood concentration ratio; obtaining a standard time corresponding to each scanning time based on the mapping relationship; the standard time is a ratio between a cumulative blood activity value within a scanning time period corresponding to the scanning time and a blood activity value corresponding to the scanning time within the first scanning duration; generating the target tissue uptake rate image according to the initial tissue uptake rate image, the initial distribution volume image, and the standard time.

3. The method of claim 2, wherein, The generating of the target tissue uptake rate image according to the initial tissue uptake rate image, the initial distribution volume image, and the standard time comprises: obtaining an intermediate distribution volume image according to the initial tissue uptake rate image, the tissue-to-blood concentration ratio, and the standard time; correcting the initial distribution volume image according to the intermediate distribution volume image to obtain a target distribution volume image; generating the target tissue uptake rate image based on the initial tissue uptake rate image, the target distribution volume image, and the standard time.

4. The method of claim 3, wherein, The generating of the target tissue uptake rate image based on the initial tissue uptake rate, the target distribution volume, and the standard time comprises: correcting the tissue-to-blood concentration ratio based on the initial tissue uptake rate image, the target distribution volume image, and the standard time to obtain a corrected tissue-to-blood concentration ratio of each scanning time; generating the target tissue uptake rate image according to a difference value between the tissue-to-blood concentration ratio and the corrected tissue-to-blood concentration ratio.

5. The method of claim 4, wherein, The generating of the target tissue uptake rate image according to the difference value between the tissue-to-blood concentration ratio and the corrected tissue-to-blood concentration ratio comprises: in a case where the difference value is greater than a preset difference threshold, updating a parameter of the feature extraction model according to each difference value, taking the updated feature extraction model as the feature extraction model, and returning to execute the step of obtaining the initial tissue uptake rate image and the initial distribution volume image according to the preset feature extraction model and the tissue-to-blood concentration ratio until the difference value is less than or equal to the difference threshold; In a case where the difference value is less than or equal to a preset difference threshold, the corresponding initial tissue uptake rate image is taken as the target tissue uptake rate image.

6. The method of claim 1, wherein, The generating the target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and a preset mapping relationship comprises: obtaining a standard time corresponding to each scanning time based on the mapping relationship; the standard time is a ratio between a cumulative blood activity value in a scanning time period corresponding to the scanning time and a blood activity value corresponding to the scanning time within the first scanning duration; fitting the tissue-to-blood concentration ratio and the standard time corresponding to the scanning time to generate the target tissue uptake rate image.

7. The method according to any one of claims 1 to 6, characterized in that, The obtaining process of the mapping relationship comprises: obtaining a sample standard time of each scanning time; the sample standard time is a ratio between a cumulative blood activity value in a scanning time period corresponding to each scanning time and a blood activity value corresponding to the scanning time within a second scanning duration; the second scanning duration is greater than the first scanning duration; fitting a plurality of the scanning time and the sample standard time corresponding to the scanning time to obtain the mapping relationship.

8. An image generation apparatus characterized by comprising: The apparatus comprises: a first obtaining module configured to obtain a tissue-to-blood concentration ratio corresponding to each scanning time within a first scanning duration; the tissue-to-blood concentration ratio is a ratio between a tissue activity value and a blood activity value of a scanning object at each scanning time; a generating module configured to generate a target tissue uptake rate image of the scanning object according to the tissue-to-blood concentration ratio and a preset mapping relationship; the mapping relationship is used to represent a mapping relationship between a scanning time, a blood activity value corresponding to the scanning time and a cumulative blood activity value in a scanning time period corresponding to the scanning time. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor, when executing the computer program, implements the steps of the method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method in any one of claims 1 to 7.

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