Grey relational analysis-based fast parameter imaging method in dynamic pet
Patent Information
- Application Number
- PCT/CN2025/079920
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2025-02-28
- Publication Date
- 2026-09-03
Smart Images
Figure CN2025079920_03092026_PF_FP_ABST
Abstract
Description
A rapid parametric imaging method in dynamic PET based on grey relational analysis Technical Field
[0001] This invention relates to the field of medical PET imaging technology, and in particular to a rapid parametric imaging method in dynamic PET based on grey relational analysis. Background Technology
[0002] Positron emission tomography (PET) is an imaging technique used in nuclear medicine clinical examinations. It is widely used in the diagnosis of oncology, neuroscience, and cardiovascular diseases, and is often combined with other imaging techniques such as CT or MRI to provide more comprehensive diagnostic information. Semi-quantitative normalized uptake values (SUVs) in PET are easily affected by individual physiological differences and cannot provide absolute metabolic information, limiting the comprehensive assessment of lesion status. Kinetic parameters provide more refined physiological information than SUVs; however, dynamic PET scans often require continuous scanning times exceeding 60 minutes, which can cause patient discomfort or anxiety, and patient movement can produce artifacts. Most current methods addressing the low image quality of rapid dynamic PET parametric images rely on specific mathematical models. The parameter calculation process is complex, and the small sample size leads to low model generalization ability and the potential for outliers in parameter calculations, resulting in poor algorithm robustness.
[0003] Therefore, researching and developing new rapid quantitative parametric imaging methods can not only ensure the quality of parametric images but also shorten the duration of dynamic scanning and improve clinical efficiency, which is of great scientific significance and application prospects for clinical diagnostic efficiency. Summary of the Invention
[0004] In view of this, embodiments of the present invention provide a rapid parametric imaging method based on grey relational analysis in dynamic PET to solve the above problems.
[0005] This invention provides a rapid parametric imaging method for dynamic PET based on grey relational analysis, comprising: acquiring a complete dynamic PET image and a short-time dynamic PET image; extracting an arterial input function from the complete dynamic PET image; extracting tissue time-activity curves from different tissues in the short-time dynamic PET image; constructing a Logan analysis model and generating a gold standard parametric image based on the arterial input function and the tissue time-activity curves; optimizing the slope estimation of the Logan analysis model using grey relational analysis; and generating a short-time parametric image based on the optimized slope estimation.
[0006] In another implementation of the present invention, the Logan analysis model is represented as:
[0007] Among them, CA It is the arterial input function; C T It is the tissue time-activity curve for each voxel; t * Tracer equilibrium time; V T These are the estimated parameters of interest.
[0008] In another implementation of the present invention, the optimization of the slope estimation of the Logan analysis model using the grey relational analysis method includes: constructing a grey relational analysis correlation sequence based on the horizontal and vertical variables of the Logan analysis model; normalizing the sequence using the min-max normalization method; calculating the grey relational coefficient between each frame node and the lower correlation sequence based on the new sequence after normalization; and removing weakly correlated parts of the signal according to the magnitude of the grey relational coefficient, retaining the strongly correlated signals for linear fitting to obtain the optimized slope estimate.
[0009] In another implementation of the present invention, the construction of the grey relational analysis sequence based on the horizontal and vertical variables of the Logan analysis model includes: converting the horizontal and vertical variables of the Logan analysis model... and As the reference sequence and comparison sequence in grey relational analysis, respectively, they are represented as: x(t)=[x(1),x(2),…,x(n)] y(t)=[y(1),y(2),…,y(n)].
[0010] In another implementation of the present invention, the normalization calculation formula is expressed as:
[0011] Where 'a' is the reference sequence or comparison sequence, a min It is the minimum value of the sequence, a max It is the maximum value of the sequence, a new It is a new sequence after the sequence has been normalized.
[0012] In another implementation of the present invention, the formula for calculating the grey relational coefficient is expressed as:
[0013] Where |x(t)-y(t)| is the difference between the reference sequence and the comparison sequence in the Logan analysis model at time t; ρ is the resolution coefficient; max|x(t)-y(t)| is the maximum absolute value of the difference between the reference sequence and the comparison sequence; and min|x(t)-y(t)| is the minimum absolute value of the difference between the reference sequence and the comparison sequence.
[0014] In another aspect, the present invention provides a rapid parametric imaging system for dynamic PET based on grey relational analysis, comprising: an image acquisition module for acquiring complete dynamic PET images and short-time dynamic PET images; an image processing module for extracting an arterial input function from the complete dynamic PET image and extracting tissue time-activity curves from different tissues in the short-time dynamic PET image; a model building module for constructing a Logan analysis model and generating a gold standard parametric image based on the arterial input function and the tissue time-activity curves; a parameter optimization module for optimizing the slope estimate of the Logan analysis model using grey relational analysis; and an image generation module for generating a short-time parametric image based on the optimized slope estimate.
[0015] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of a rapid parametric imaging method in dynamic PET based on grey relational analysis as described in any of the preceding claims.
[0016] In another aspect, the present invention provides a computer storage medium, characterized in that the computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a rapid parametric imaging method in dynamic PET based on grey relational analysis as described in any of the preceding claims.
[0017] The present invention provides a rapid parametric imaging method for dynamic PET based on grey relational analysis. This method introduces grey relational analysis into the parametric imaging calculation. By comparing the grey relational coefficients between two variables in the graph model, the linear fitting scatter points in low-quality parametric images are denoised. This method does not require complex parameter estimation and model building, and can simultaneously consider the relationship between multiple variables. It is more sensitive to data changes and obtains higher-quality parametric images with a shorter dynamic scanning time. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.
[0019] Figure 1 is a schematic flowchart of a rapid parametric imaging method based on grey relational analysis in dynamic PET according to an embodiment of the present invention.
[0020] Figure 2 is a schematic diagram of the Logan analysis model construction process according to an embodiment of the present invention.
[0021] Figure 3 is a 60-minute standard parameter image of an embodiment of the present invention.
[0022] Figure 4 shows an unprocessed 30-minute short-time parameter image of an embodiment of the present invention.
[0023] Figure 5 is a schematic diagram of the restoration result according to an embodiment of the present invention. Detailed Implementation
[0024] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.
[0025] Figure 1 is a schematic flowchart of a rapid parametric imaging method in dynamic PET based on grey relational analysis provided by an embodiment of the present invention. As shown in Figure 1, this embodiment mainly includes:
[0026] S101. Obtain complete dynamic PET images and short-time dynamic PET images.
[0027] S102. Extract the arterial input function from the complete dynamic PET image.
[0028] S103. Extract tissue time-activity curves from different tissues in the short-time dynamic PET images.
[0029] For example, regions of interest are plotted on pathological lesions, which are classified as primary tumors (PT), lymph node metastases (LNM), and other metastases (OM). The volumes of interest (VOI) of these lesions are plotted on 3-5 consecutive slices of dynamic PET images, and the tissue time-activity curves of each frame are extracted.
[0030] As shown in Table 1, this application includes 20 lesions as an example: 10 PTs, 5 LNMs, and 5 OMs (including 3 peritoneal metastases, 1 bone metastasis, and 1 lung metastasis).
[0031] Table 1. Relevant information about the lesions
[0032] S104. Based on the arterial input function and the tissue time-activity curve, construct a Logan analysis model and generate a gold standard parameter image.
[0033] S105. The slope estimation of the Logan analysis model is optimized using the grey relational analysis method.
[0034] S106. Generate short-time parameter images based on the optimized slope estimation.
[0035] The present invention provides a rapid parametric imaging method for dynamic PET based on grey relational analysis. This method introduces grey relational analysis into the parametric imaging calculation. By comparing the grey relational coefficients between two variables in the graph model, the linear fitting scatter points in low-quality parametric images are denoised. This method does not require complex parameter estimation and model building, and can simultaneously consider the relationship between multiple variables. It is more sensitive to data changes and obtains higher-quality parametric images with a shorter dynamic scanning time.
[0036] In another implementation of the present invention, the Logan analysis model is represented as:
[0037] Among them, C A It is the arterial input function; C T It is the tissue time-activity curve for each voxel; t * Tracer equilibrium time; V T These are the estimated parameters of interest.
[0038] For example, as shown in Figure 2, considering the computational complexity of the room model, the Logan diagram becomes the preferred computational parameter V due to its simplicity. T An effective approach is to obtain the whole-body parameter image of the reversible tracer through linear fitting of the Logan plot, when the tracer reaches equilibrium time t. * The logan graph shows a linear trend, and the slope is the estimated parameter of interest, V. T .
[0039] As shown in Figure 3, the gold standard V T The parameter calculation setting is t * =20min, that is, the last 40 minutes of the entire 60-minute dynamic scanning protocol are used as the linear fitting part of the gold standard Logan analysis.
[0040] In another implementation of the present invention, the optimization of the slope estimation of the Logan analysis model using the grey relational analysis method includes: constructing a grey relational analysis correlation sequence based on the horizontal and vertical variables of the Logan analysis model; normalizing the sequence using the min-max normalization method; calculating the grey relational coefficient between each frame node and the lower correlation sequence based on the new sequence after normalization; and removing weakly correlated parts of the signal according to the magnitude of the grey relational coefficient, retaining the strongly correlated signals for linear fitting to obtain the optimized slope estimate.
[0041] For example, grey relational analysis is a quantitative analysis method based on the correlation between sequence data. It can be used to reduce noise and highlight the true signal. Its core idea is to find the most relevant parts by calculating the correlation between different sequences, thereby improving the accuracy of the signal.
[0042] By introducing grey relational analysis, multiple sequences are transformed into a dimensionless form. Grey relational thresholding is used to filter strong and weak signals. By finding the part of the signal that is strongly correlated with the target, the slope estimation is optimized and the influence of noise is reduced.
[0043] In another implementation of the present invention, the construction of the grey relational analysis sequence based on the horizontal and vertical variables of the Logan analysis model includes: converting the horizontal and vertical variables of the Logan analysis model... and As the reference sequence and comparison sequence in grey relational analysis, respectively, they are represented as: x(t)=[x(1),x(2),…,x(n)] y(t)=[y(1),y(2),…,y(n)].
[0044] For example, the horizontal and vertical variables of the Logan graph are used as the reference and comparison sequences in the GRA. These two variables are related to dynamic PET multi-frame data and need to be calculated based on frame-by-frame information.
[0045] In another implementation of the present invention, the normalization calculation formula is expressed as:
[0046] Where 'a' is the reference sequence or comparison sequence, a min It is the minimum value of the sequence, a max It is the maximum value of the sequence, a new It is a new sequence after the sequence has been normalized.
[0047] For example, the calculation of grey relational degree depends on the degree of difference between sequences. If the numerical ranges of the original data differ significantly, the calculation of grey relational degree will be inaccurate because the calculation of difference may be affected by the data scale. In this invention, a min-max normalization method is used to transform all data into a dimensionless standardized form, thereby eliminating the influence of different dimensions and scales, making the calculation results more fair and reliable.
[0048] In another implementation of the present invention, the formula for calculating the grey relational coefficient is expressed as:
[0049] Where |x(t)-y(t)| is the difference between the reference sequence and the comparison sequence in the Logan analysis model at time t; ρ is the resolution coefficient; max|x(t)-y(t)| is the maximum absolute value of the difference between the reference sequence and the comparison sequence; and min|x(t)-y(t)| is the minimum absolute value of the difference between the reference sequence and the comparison sequence.
[0050] For example, for each time point, the gray correlation coefficient between the associated sequences is calculated. Based on the magnitude of the correlation coefficient, weakly correlated parts of the signal are removed, and strongly correlated signals are retained for linear fitting to obtain a new slope estimate. When filtering strong and weak signals, a gray correlation coefficient threshold of 0.5 is set to remove noise points with gray correlation coefficients less than the threshold, as shown in Figures 4 and 5, thus improving the parametric imaging quality.
[0051] This invention considers the variable characteristics of dynamic PET model parametric imaging and innovatively combines GRA with dynamic PET parametric imaging. It utilizes grey relational analysis to extract time-series features of multidimensional data and optimize the similarity between sequences. It does not require any distribution assumptions about the data and directly captures the inherent relationships of the data based on grey relational analysis, ensuring the versatility and wide applicability of the method. It has potential application value for improving the stability and reliability of short-term Logan parametric imaging.
[0052] This invention is applied to PET parametric imaging, and after appropriate deformation, it can also be applied to high-resolution reconstruction of CT and SPECT.
[0053] Another aspect of the present invention provides a rapid parametric imaging system for dynamic PET based on grey relational analysis, comprising:
[0054] Image acquisition module: Acquires complete dynamic PET images and short-time dynamic PET images.
[0055] Image processing module: extracts arterial input function from the complete dynamic PET image; extracts tissue time-activity curves from different tissues in the short-time dynamic PET image.
[0056] Model building module: Based on the arterial input function and the tissue time-activity curve, construct a Logan analysis model and generate a gold standard parameter image.
[0057] Parameter optimization module: Optimizes the slope estimation of the Logan analysis model using grey relational analysis.
[0058] Image generation module: Generates short-time parameter images based on the optimized slope estimate.
[0059] The present invention relates to a rapid parametric imaging system for dynamic PET based on grey relational analysis. The grey relational analysis method is introduced into the parametric imaging calculation. By comparing the grey relational coefficients between two variables in the graphical model, the linear fitting scatter points in low-quality parametric images are denoised. This method does not require complex parameter estimation and model building, and can simultaneously consider the relationship between multiple variables. It is more sensitive to data changes and obtains higher quality parametric images with a shorter dynamic scanning time.
[0060] In another aspect of the present invention, the electronic device includes: a processor, a memory, and a communication bus and a communication interface.
[0061] in:
[0062] The processor, memory, and communication interface communicate with each other via a communication bus.
[0063] A communication interface is used to communicate with other electronic devices or servers.
[0064] The processor is used to execute programs, specifically, to perform any of the steps of the fast parametric imaging method in dynamic PET based on grey relational analysis in the above embodiments.
[0065] Specifically, the program may include program code, which includes computer operation instructions.
[0066] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.
[0067] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.
[0068] Specifically, the program can be used to cause the processor to execute the steps of any of the rapid parametric imaging methods based on grey relational analysis in dynamic PET described in the embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the rapid parametric imaging methods based on grey relational analysis in dynamic PET described above, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments.
[0069] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods of various embodiments of this application.
[0070] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.
[0071] Specific embodiments of the invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.
[0072] It should be noted that all directional indicators (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.
[0073] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.
[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
[0075] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.
[0076] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.
[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A rapid parametric imaging method in dynamic PET based on grey relational analysis, characterized in that, include: Acquire complete dynamic PET images and short-time dynamic PET images; Extract the arterial input function from the complete dynamic PET image; Tissue time-activity curves were extracted from different tissues in the short-time dynamic PET images; Based on the arterial input function and the tissue time-activity curve, a Logan analysis model is constructed and a gold standard parameter image is generated; The slope estimation of the Logan analysis model was optimized using grey relational analysis. Short-time parameter images are generated based on the optimized slope estimation.
2. The method according to claim 1, characterized in that, The Logan analysis model is represented as follows: Among them, C A It is the arterial input function; C T It is the tissue time-activity curve for each voxel; t * Tracer equilibrium time; V T These are the estimated parameters of interest.
3. The method according to claim 2, characterized in that, The optimization of the slope estimation of the Logan analysis model using grey relational analysis includes: The association sequence of grey relational analysis is constructed based on the horizontal and vertical variables of the Logan analysis model; The sequence is normalized using the min-max normalization method; Based on the new sequence after normalization, calculate the grey correlation coefficient between each frame node and the associated sequence; Based on the magnitude of the gray correlation coefficient, weakly correlated parts of the signal are removed, and highly correlated signals are retained for linear fitting to obtain an optimized slope estimate.
4. The method according to claim 3, characterized in that, The sequence for grey relational analysis constructed from the horizontal and vertical variables based on the Logan analysis model includes: The horizontal and vertical variables of the Logan analysis model and These are used as the reference sequence and comparison sequence in grey relational analysis, respectively, and are represented as follows: x(t) = [x(1), x(2), ..., x(n)] y(t)=[y(1),y(2),…,y(n)].
5. The method according to claim 4, characterized in that, The normalization calculation formula is expressed as follows: Where 'a' is the reference sequence or comparison sequence, a min It is the minimum value of the sequence, a max It is the maximum value of the sequence, a new It is a new sequence after the sequence has been normalized.
6. The method according to claim 4, characterized in that, The formula for calculating the grey relational coefficient is expressed as follows: Where |x(t)-y(t)| is the difference between the reference sequence and the comparison sequence in the Logan analysis model at time t; ρ is the resolution coefficient; max|x(t)-y(t)| is the maximum absolute value of the difference between the reference sequence and the comparison sequence; and min|x(t)-y(t)| is the minimum absolute value of the difference between the reference sequence and the comparison sequence.
7. A rapid parametric imaging system for dynamic PET based on grey relational analysis, characterized in that, include: Image acquisition module: Acquires complete dynamic PET images and short-time dynamic PET images; Image processing module: extracts arterial input function from the complete dynamic PET image; extracts tissue time-activity curves from different tissues in the short-time dynamic PET image; Model building module: Based on the arterial input function and the tissue time-activity curve, construct a Logan analysis model and generate a gold standard parameter image; Parameter optimization module: Optimizes the slope estimation of the Logan analysis model using grey relational analysis; Image generation module: Generates short-time parameter images based on the optimized slope estimate.
8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a fast parametric imaging method in dynamic PET based on grey relational analysis as described in any one of claims 1 to 6.
9. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a rapid parametric imaging method in dynamic PET based on grey relational analysis as described in any one of claims 1 to 6.