A method and system for direct reconstruction of parametric images

Abstract

The embodiment of the present specification provides a direct reconstruction method of a parameter image, which comprises reconstructing the parameter image by iteration based on scanning data; wherein in each iteration, an iterative input function is determined based on a starting image of the iteration; a parameter analysis is performed based on the iterative input function to determine an iterative parameter image; and the starting image of the next iteration is updated based on the iterative parameter image.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of image reconstruction, and in particular, to a method and system for direct reconstruction of parametric images. BACKGROUND

[0002] Positron emission tomography (PET) is a high-level functional molecular imaging technique that combines gamma photon detection, signal processing, and image reconstruction to visualize the biochemical metabolic processes of radionuclides in living organisms. It is an important component of modern nuclear medicine diagnosis. However, the reconstruction process of parametric images usually requires multiple steps to obtain the parametric images, which limits the clinical application of parametric imaging technology.

[0003] Therefore, it is desirable to provide a method for parametric image reconstruction that can improve the efficiency of image reconstruction. SUMMARY

[0004] One of the embodiments of the present specification provides a method for direct reconstruction of parametric images, the method comprising: reconstructing a parametric image by iteration based on scan data; wherein in each iteration, an iterative input function is determined based on a starting image of the iteration; parametric analysis is performed based on the iterative input function to determine an iterative parametric image; the starting image of the next iteration is updated based on the iterative parametric image.

[0005] In some embodiments, reconstructing a parametric image by iteration further comprises: stopping iteration and obtaining a parametric image when iteration meets a preset iteration condition, the preset iteration condition comprising iteration convergence or reaching a preset iteration number.

[0006] In some embodiments, determining an iterative input function comprises: obtaining a region of interest; determining the iterative input function based on the starting image and the region of interest, the region of interest being obtained based on a CT image or a PET image.

[0007] In some embodiments, determining an iterative input function further comprises correcting the iterative input function, the correction comprising supplementing the iterative input function based on a population input function.

[0008] One of the embodiments of the present specification provides a system for direct reconstruction of parametric images, the system comprising a processing module configured to perform the following operations: reconstructing a parametric image by iteration based on scan data; wherein in each iteration, an iterative input function is determined based on a starting image of the iteration; parametric analysis is performed based on the iterative input function to determine an iterative parametric image; the starting image of the next iteration is updated based on the iterative parametric image.

[0009] In some embodiments, the processing module is further configured to stop the iteration and obtain the parametric image when a preset iteration condition is met, the preset iteration condition comprising iteration convergence or reaching a preset iteration number.

[0010] One of the embodiments of the present specification provides a device for direct reconstruction of a parametric image, comprising a processor configured to execute the direct reconstruction method of the parametric image.

[0011] One of the embodiments of the present specification provides a computer readable storage medium, the storage medium storing computer instructions, when the computer reads the computer instructions in the storage medium, the computer executes the direct reconstruction method of the parametric image. BRIEF DESCRIPTION OF DRAWINGS

[0012] The present specification will be further described in the manner of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0013] Figure 1 is a schematic diagram of an application scenario of an image reconstruction system according to some embodiments of the present specification;

[0014] Figure 2 is an exemplary flowchart of obtaining a parametric image according to some embodiments of the present specification;

[0015] Figure 3 is an exemplary flowchart of determining an iteration input function according to some embodiments of the present specification;

[0016] Figure 4 is an exemplary schematic diagram of correcting an iteration input function according to some embodiments of the present specification;

[0017] Figure 5 is an exemplary schematic diagram of correction based on a population input function according to some embodiments of the present specification. DETAILED DESCRIPTION

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, without paying creative labor, the present specification can also be applied to other similar scenarios according to these drawings. Unless it is obvious from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0019] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0020] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0021] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0022] Figure 1 This is a schematic diagram illustrating an application scenario of a parametric image reconstruction system according to some embodiments of this specification. In some embodiments, the application scenario 100 involved in the embodiments of this specification can be implemented by carrying out the methods and / or processes disclosed in this specification to achieve parametric image reconstruction.

[0023] like Figure 1 As shown, application scenario 100 may include processing device 110, network 120, terminal 130, storage device 140, and data acquisition device 150. The various components in application scenario 100 can be connected in multiple ways. For example, data acquisition device 150 and processing device 110 can be connected via network 120 or directly.

[0024] Processing device 110 can process data and / or information acquired from terminal 130, storage device 140, and / or data acquisition device 150. For example, processing device 120 can generate a parametric image by iteratively reconstructing parametric images based on scan data (e.g., raw PET scan data) acquired by data acquisition device 150. As another example, processing device 110 can correct the iterative input function during the reconstruction process. In some embodiments, processing device 110 can be a single server or a group of servers.

[0025] The network 120 can include any suitable network capable of facilitating the exchange of information and / or data for the application scenario 100. In some embodiments, one or more components of the application scenario 100 (e.g., the processing device 110, the terminal 130, the storage device 140, the data acquisition device 150, etc.) can exchange information and / or data via the network 120.

[0026] The terminal 130 can include one or any combination of a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a desktop computer 130-4, etc. In some embodiments, the terminal 130 can interact with other components in the application scenario 100 via the network 120. For example, the terminal 130 can send one or more control instructions to the imaging device 150 to control the data acquisition device 150 to scan a to-be-scanned object according to the instructions to acquire scan data. In some embodiments, the terminal 130 can be part of the processing device 110. In some embodiments, the terminal 130 can be integrated with the processing device 110 as an operation station of the data acquisition device 150. For example, a user / operator (e.g., a doctor) of the parameter reconstruction system 100 can control the data acquisition device 150 to acquire scan data of a to-be-scanned object via the operation station. In some embodiments, the terminal 130 can serve as a receiving terminal and display terminal for receiving and displaying the parameter image determined by the processing device 110. The above examples are merely used to illustrate the broadness of the terminal 130 and not to limit the scope thereof.

[0027] The storage device 140 can be configured to store data (e.g., scan data) and / or instructions. In some embodiments, the storage device 140 can store data acquired from the processing device 110, the terminal 130, and / or the data acquisition device 150, for example, the storage device 140 can store scan data of a to-be-scanned object acquired from the data acquisition device 150, etc. In some embodiments, the storage device 140 can store data and / or instructions used by the processing device 110 to perform or use to perform the exemplary methods described in the present application.

[0028] The data acquisition device 150 can be configured to acquire scan data (e.g., PET raw scan data) of a to-be-scanned object. The to-be-scanned object can include a biological object (e.g., a human body, an animal, etc.), a non-biological object (e.g., a phantom), etc. In some embodiments, the data acquisition device 150 can be a positron emission computed tomography (PET) imaging device, a PET-CT imaging device, a PET-MRI imaging device, etc., where the CT device is a computed tomography scanning imaging device and the MRI device is a magnetic resonance imaging device.

[0029] Figure 2is an exemplary flowchart of acquiring a parametric image according to some embodiments of the present specification. As shown in Figure 2 Flow 200 includes the following steps. In some embodiments, flow 200 can be performed by processing device 110.

[0030] In some embodiments, the direct reconstruction method of the parametric image can be based on the scan data, and the reconstruction of the parametric image is performed by iteration. Wherein, the direct reconstruction refers to the reconstruction of the parametric image by using a non-indirect method, and the indirect reconstruction of the parametric image refers to that the input function is obtained by first performing complete reconstruction, and then the parametric image is obtained by secondary reconstruction based on the input function.

[0031] Scan data refers to the original data collected by the scanning device, for example, PET scan data obtained by scanning the tracer signal of PET. In some embodiments, the scan data refers to the data collected by the scanning device, such as the PET scan data obtained after PET scanning. In some embodiments, the scan data can include multiple angles of data, that is, it can include projection data of different angles at a time point. In some embodiments, the scan data can include multiple frames of original data. When the PET data acquisition device performs PET scanning, it can be a continuous scanning, that is, scanning data for several time periods, and the scanning result of each time period is called a frame.

[0032] Iteration refers to a process of repeating multiple rounds, wherein at least part of the output of each round is used as part of the input of the next round. For example, in the process of reconstructing the parametric image based on the scan data, the initial starting image is obtained based on the scan data, the initial input function is determined based on the starting image, and the initial input function is the iterative input function of the first iteration. In each iteration, the iterative input function is analyzed to obtain an iterative parametric image, which can be used to update the starting image of the next iteration. For specific operation of iteration, please refer to the content of Figure 2 .

[0033] Parametric image refers to an image with parameter values that can reflect the analysis results. In some embodiments, the parametric image can be a two-dimensional or three-dimensional image, and the value of each pixel or voxel thereof reflects the parameter value of the corresponding position of the scanned object. The parameter value contained in the parametric image can be a pharmacokinetic parameter value, which can include local blood flow, metabolic rate, and material transport rate.

[0034] In some embodiments, the parameter image can be obtained based on the kinetic parameters. For example, based on the obtained kinetic parameters of each pixel or voxel in multiple frames, the parameter image can be reconstructed. In some embodiments, the kinetic parameters can be obtained based on the reconstructed images of multiple time frames (e.g., PET reconstructed images of multiple time frames), for example, by processing the reconstructed images of multiple time frames, the parameter value of each pixel or voxel corresponding to each time frame obtained as the kinetic parameter, and then reconstructing the parameter image based on the obtained parameter value. In some embodiments, the kinetic parameters can be obtained based on the analysis and processing of the scan data.

[0035] Reconstruction refers to a process of generating data in one format by processing data in another format, for example, a process of reconstructing original PET data into dynamic multi-frame image data.

[0036] The following will specifically illustrate how to reconstruct the parameter image based on the scan data by iteration through steps 210-230. In some embodiments, steps 210-230 can be performed by the processing module.

[0037] Step 210, in each iteration, the iteration input function is determined based on the starting image of the iteration.

[0038] In the first iteration, the processing module can correct the scan data, and reconstruct the corrected scan data to obtain the starting image of the first iteration. In some embodiments, the starting image in the first iteration can also be determined based on other manners, such as a random image, etc.

[0039] In each iteration, the processing module can determine the iteration input function based on the starting image of the iteration. In subsequent iterations, the starting image can be updated based on the iteration parameter image obtained in the previous iteration, for details, see step 230.

[0040] The iteration input function refers to the input function obtained based on the starting image of each iteration.

[0041] The input function is a curve of the activity concentration of human plasma over time. In some embodiments, the input function can be obtained by blood sampling, for example, blood samples of the human are collected at different time points during the scanning process, and the input function is obtained based on the data of the blood samples. In some embodiments, the input function can also be obtained from dynamic images, for example, the dynamic images are obtained first, the blood pool VOI is selected, and the time activity curve (TAC) inside the blood pool VOI is obtained for related correction (for example, plasma / whole blood ratio correction, metabolize rate correction, partial volume correction, etc.) to serve as the input function. In some embodiments, the input function can also be corrected based on a population input function, for example, the input function of part of the population is used to complete the complete input function. For specific details of the correction, see Figure 5 .

[0042] The iteration input function of the first iteration is the initial input function. In subsequent iterations, the iteration input function of each iteration can be determined based on the starting image of the iteration. For more details of determining the iteration input function, see Figure 3 . The iteration input function obtained in the last iteration can be used as the input function output when the iteration is stopped.

[0043] Step 220, performing parameter analysis based on the iteration input function to determine the iteration parameter image.

[0044] The iteration parameter image refers to the parameter image obtained in each iteration in the dynamic reconstruction of the iteration input function. In some embodiments, the processing module can obtain the iteration parameter image by performing corresponding parameter analysis on the iteration input function. In some embodiments, the iteration parameter image obtained in the previous iteration can be used to update the starting image of the next iteration, and the starting image in the subsequent iterations can be referred to as the iteration dynamic image. The iteration parameter image obtained in the last iteration can be used as the parameter image output when the iteration is stopped.

[0045] The iteration dynamic image can be determined and updated based on the iteration parameter image of the iteration and the pharmacokinetic model (for example, the Patlak model). For more details of determining the iteration dynamic image, see step 230.

[0046] The iteration parameter image of the iteration can be determined based on the iteration dynamic image (or the starting image) output in the previous iteration. For example, the processing module can determine the iteration input function based on the iteration dynamic image output in the previous iteration, and determine the iteration parameter image of the iteration based on the iteration input function.

[0047] The parameter analysis can be a process of obtaining the iteration parameter image of the iteration based on the iteration input function and the starting image in the iteration.

[0048] In some embodiments, the processing module can obtain the iterative parameter image of the iteration based on the iterative input function and the iterative dynamic image in the iteration. For example, the iterative parameter image can be determined using the following iterative formula (1):

[0049]

[0050] Where m and n are positive integers, S represents the nth frame image in the multi-frame iterative dynamic image of the (m-1)th iteration; n C n Characterizes the dynamic model matrix obtained by calculating the iterative input function; κ l+1 ,b l+1 It can characterize the iterative parameter image and determine k. l+1 ,b l+1 This allows for the determination of iterative parameter images.

[0051] Step 230: Update the starting image for the next iteration based on the iterative parameter image.

[0052] As described in step 220, the starting image for the next iteration is the iterative dynamic image.

[0053] In some embodiments, the processing module can update and determine the iterative dynamic image based on the iterative parameter image, the iterative input function, the pharmacokinetic model, etc.

[0054] For example, the processing module can use iterative formula (2) to obtain iterative dynamic images based on iterative parameter images, iterative input functions, and pharmacokinetic models.

[0055]

[0056] Where m and n are positive integers, f represents the nth frame image in the multi-frame iterative dynamic image of the m-th iteration; m (K l ,b l ) represents the Patlak model, and f m (K l ,b l ) = S n κ l +C n (b l ); K l ,b l Characterizing the iterative parameter image, S n C n The dynamic model matrix R is represented by the input function calculated using the iterative method. ncharacterizing the random projection estimate and the scattered projection estimate, P is a system matrix, y n characterizing a 4D sinogram.

[0057] In each iteration, the processing module can determine an iteration input function for the next iteration based on the iteration dynamic image obtained in the current iteration. For example, the processing module can determine a pharmacokinetic parameter value corresponding to each iteration dynamic image based on processing of the multiple frames of iteration dynamic images by a pharmacokinetic model, and determine an input function for each frame based on the parameter value, where one frame corresponds to one time point. In some embodiments, the determination of the iteration input function is also related to the selection of the region of interest. For more details about determining the iteration input function, see Figure 3 .

[0058] In some embodiments, the processing module can stop the iteration and obtain the parameter image when the iteration satisfies a preset iteration condition. The preset iteration condition can include that the iteration converges or reaches a preset number of iterations.

[0059] The parameter image obtained after stopping the iteration can be used as a parameter image for evaluating the sample condition. In some embodiments, the processing module can determine an iteration input function based on the iteration dynamic image in the last iteration, and obtain a parameter image based on the iteration dynamic image output in the last iteration, where the parameter image is the parameter image obtained after stopping the iteration.

[0060] In the reconstruction process of the parameter image, the image reconstruction is first performed to obtain dynamic image data, then the input function is obtained based on the reconstructed dynamic image, and the parameter image is obtained based on the input function or by applying the input function to the reconstructed dynamic image data. Therefore, in the entire reconstruction process, not only the parameter image is obtained, but also the input function is obtained, which can save the additional reconstruction estimation and effectively improve the reconstruction efficiency.

[0061] Figure 3 is an exemplary flowchart of determining an iteration input function according to some embodiments of the present specification. As shown in Figure 3 , the flowchart 300 includes the following steps. In some embodiments, the flowchart 300 can be executed by the processing device 110.

[0062] Step 310, obtaining a region of interest.

[0063] The region of interest (VOI) refers to a region of interest in a scan image, which can be an image obtained by CT scanning or PET scanning. In some embodiments, the region of interest can be a region associated with the heart or the artery. For example, the heart blood pool, the artery blood pool, etc.

[0064] The region of interest can be a two-dimensional or three-dimensional region, and the value of each pixel or voxel thereof reflects the activity value of the corresponding position of the object to be scanned. The region of interest can be a fixed region in each frame of the scan image, and the fixed region in multiple frames of the scan image can provide a dynamic change in the region data.

[0065] In some embodiments, the region of interest can be determined based on a CT image obtained by CT scanning or a PET image obtained by PET scanning. For example, from a CT image of a heart, a region corresponding to a blood pool can be taken as the region of interest. In some embodiments, the region of interest can also be obtained by mapping the region of interest determined based on the CT image to the PET image.

[0066] In step 320, an iterative input function is determined based on the initial image and the region of interest.

[0067] The iterative input function is related to a time activity curve (TAC curve), and the abscissa of the TAC curve corresponds to the time point corresponding to each frame of the iterative image, and the ordinate represents the activity concentration, which is obtained by averaging all pixel values or voxel values of the region of interest in the initial image. After the TAC curve is determined, the iterative input function can be determined. For example, all pixel values or voxel values of the region of interest in multiple frames of the initial image can be averaged, and multiple pixel averages or voxel averages can be obtained from the multiple frames of the initial image. Then, the pixel averages or voxel averages are taken as the ordinate, and the frame numbers corresponding to the pixel averages or voxel averages are taken as the abscissa, and then the TAC curve is obtained and the iterative input function is determined.

[0068] In some embodiments, the iterative input function can also be corrected in order to optimize the iterative input function. For more details of correcting the iterative input function, see Figure 4 .

[0069] Determining the iterative input function based on the initial image and the region of interest can avoid cumbersome steps such as arterial blood sampling, making the method of obtaining the input function simple and easy to operate. The input function determined by the method can reflect the specificity of the object to be scanned, making the determined input function more accurate.

[0070] It should be noted that the above description of the process 300 is only for example and illustration, and does not limit the scope of the present specification. Those skilled in the art can make various modifications and changes to the process 300 under the guidance of the present specification. However, these modifications and changes are still within the scope of the present specification.

[0071] Figure 4 Fig. 4 is an exemplary schematic diagram 400 of correcting the iterative input function according to some embodiments of the present specification.

[0072] Due to the presence of noise or data missing in the input function, in order to improve the accuracy of the input function, in some embodiments, the obtained iterative input function can be further corrected to obtain a corrected iterative input function with higher accuracy.

[0073] The iterative input function before correction is the iterative input function to be corrected. The iterative input function to be corrected can be any input function obtained in the entire parameter image reconstruction process. For example, the initial input function obtained in the first iteration or the iterative input function generated in the subsequent iteration.

[0074] In some embodiments, the processing module can correct the iterative input function. In some embodiments, the correction includes supplementing the iterative input function based on the population input function. The population input function refers to the plasma TAC determined based on the plasma TAC of multiple people, which can be used as a template, also known as standard arterial input function (SAIF). For more details about the correction based on the population input function, see Figure 5 .

[0075] In some embodiments, the way to correct the iterative input function to be corrected can include whole blood / plasma drug ratio correction, metabolize rate correction, partial volume correction (PVC) correction, model correction (for example, using Feng model multi-exponential model for correction).

[0076] Starting iteration after correcting the initial input function or continuing iteration with the corrected iterative input function as the iterative input function of the next iteration can effectively improve the accuracy of the parameter image obtained based on the iterative input function.

[0077] Figure 5 is an exemplary schematic diagram 500 of correction based on the population input function according to some embodiments of the present specification.

[0078] In some embodiments, the iterative input function to be corrected 520 can be supplemented based on the population input function 510 to determine the corrected iterative input function 530. In some embodiments, the correction method of supplementing the iterative input function based on the population input function can be used alternatively or in combination with other correction methods, for example, the iterative input function to be corrected 520 can be corrected first, such as whole blood / plasma drug ratio correction, and then the corrected iterative input function 520 can be supplemented based on the population input function 510.

[0079] The supplementing of the to-be-corrected iterative input function 520 by the population-based input function 510 can be supplementing missing data (e.g., missing data of the first few minutes) in the to-be-corrected input function 520 with the population-based input function 510. In some embodiments, the TAC curve shape of the population-based input function 510 can be used as a template to supplement the missing data in the to-be-corrected iterative input function 520 based on the curve shape of the template. For example, a portion corresponding to the missing portion in the to-be-corrected input function 520 can be determined in the population-based input function 510 corresponding to the same block of interest, and the x-y coordinate values of this portion can be used as the supplement data. In some embodiments, the supplement data can be directly supplemented into the to-be-corrected input function 520 to determine the corrected input function 530. In some embodiments, the supplement data can also be differentially processed, e.g., adjusted according to the characteristic parameters (e.g., height, weight, disease, etc.) of the to-be-scanned object, so as to determine the corrected iterative input function 530.

[0080] By correcting the iterative input function with the population-based input function having a similar curve shape, the missing portion in the input function can be efficiently supplemented. At the same time, since the population-based input function has overall commonality and cannot reflect the specificity of different to-be-scanned objects, by differentially processing the supplement data, the corrected iterative input function can take into account the specificity of the to-be-scanned object, so as to obtain a more accurate parameter image.

[0081] The embodiments of the present specification also provide a direct reconstruction device of an image, comprising a processor and a memory; the memory is used to store computer instructions; the processor is used to execute at least part of the computer instructions to implement the direct reconstruction method of an image as described above.

[0082] The embodiments of the present specification also provide a computer readable storage medium, which stores computer instructions, when the computer instructions are executed by a processor, the direct reconstruction method of an image as described above is implemented.

[0083] The above has described the basic concepts, and it is obvious that the above detailed disclosure is only used as an example and does not limit the present specification. Although the present specification does not explicitly describe it, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0084] Also, the use of "a" or "an" or "the" are intended to include both singular and plural, unless the context clearly indicates otherwise. For example, the phrases "a member" as well as "members" can refer to one or more members. Similarly, the term "an element" as well as "the element" can refer to one or more elements. Also, the terms "comprising", "having", "including", and "containing" are to be construed open-ended terms (i.e., meaning "including, but not limited to") unless otherwise noted. The use of "consisting essentially of" means that the composition or process can include additional elements so long as these additional elements do not materially alter the basic and novel characteristics of the claimed composition or process. The use of "consisting of means that the composition or process can include only the elements specifically listed.

[0085] Furthermore, the order of presentation of the processes and methods of the present description is not intended to be limiting unless otherwise specifically noted. Although the above disclosure discusses some presently preferred embodiments of the application, the present application can be practiced with modifications and alterations, within the spirit and scope of the appended claims. For example, although the above-described system components can be implemented by hardware devices, they can also be implemented by software solutions, such as installing the described system on an existing server or mobile device.

[0086] Similarly, it is noted that the foregoing description of embodiments of the present description is intended to be illustrative only rather than limiting. For example, while the above description discusses some presently preferred embodiments of the application, the present application can be practiced with modifications and alterations, within the spirit and scope of the appended claims. For example, although the above-described system components can be implemented by hardware devices, they can also be implemented by software solutions, such as installing the described system on an existing server or mobile device.

[0087] Some embodiments use numerical designations to describe components, quantities of attributes. It is to be understood that such numerical designations used in the description of embodiments are, in some examples, modified by the adjectives "about", "approximately", or "substantially". Unless otherwise indicated, "about", "approximately", or "substantially" indicates that the described numerical value allows for a ±20% variation. Accordingly, numerical values used in the description and claims are approximations that can vary depending on the desired properties of the individual embodiments. In some embodiments, numerical values should be considered in the context of the number of significant figures used. Although the numerical ranges and parameters setting forth the broadest scope of the embodiments of the present description are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values set forth in the specific examples are provided to be as precise as practicable.

[0088] Each patent, patent application, patent publication, and other material cited in this specification is hereby incorporated by reference in its entirety herein for the teachings relevant to the sentence and / or paragraph in which the reference is presented. Document histories, to the extent not inconsistent with the pertinent U.S. patent application file history, are also incorporated by reference herein. To the extent that material incorporated by reference contradicts or contradicts any portion of this specification, including definition, the portion of the material incorporated by reference prevails. Note, however, that in the event of inconsistencies between any such material and the present specification, including definitions, the present specification, including definitions, will control.

[0089] Finally, it should be understood that the embodiments described herein are merely exemplary of the principles of the present description. Other embodiments can be devised without departing from the scope of the present description. Accordingly, the embodiments described herein are not intended to limit the scope of the present description, but rather are intended to be exemplary thereof.

Claims

1. A direct reconstruction method for parametric images, comprising reconstructing the parametric image iteratively based on scan data; wherein, In each iteration, the iterative input function is determined based on the iterative dynamic image of that iteration; the iterative dynamic image of that iteration is determined based on the iterative parameter image obtained in the previous iteration; the iterative dynamic image includes PET images; The iterative input function refers to the input function obtained based on the iterative dynamic image of the iteration in each iteration. The iterative input function of the first iteration is determined based on the starting image, which is obtained based on the scan data, and the scan data includes raw data from multiple frames. The function for determining the iterative input includes: Get the region of interest; The iterative input function is determined based on the iterative dynamic image and the region of interest; The iterative input function is supplemented based on the population-based input function to determine the corrected iterative input function; Based on the corrected iterative input function and the iterative dynamic image, parameter analysis is performed to determine the iterative parameter image; the iterative parameter image refers to an image with parameter values ​​that can reflect the analysis results. The iteration stops and the parameter image is obtained once the preset iteration conditions are met.

2. The method according to claim 1, The preset iteration conditions include iteration convergence or reaching a preset number of iterations.

3. The method according to claim 1, wherein the region of interest is obtained based on CT images or PET images.

4. A direct reconstruction system for parametric images, comprising a processing module configured to perform the following operations: Based on the scanned data, parametric images are reconstructed iteratively; among them, In each iteration, an iterative input function is determined based on the iterative dynamic image of that iteration; wherein, the iterative dynamic image of that iteration is determined based on the iterative parameter image obtained in the previous iteration; the iterative dynamic image includes PET images; the iterative input function refers to the input function obtained based on the iterative dynamic image of that iteration in each iteration, and the iterative input function of the first iteration is determined based on the starting image, the starting image is obtained based on the scan data, and the scan data includes multiple frames of raw data; The function for determining the iterative input includes: Get the region of interest; The iterative input function is determined based on the iterative dynamic image and the region of interest; The iterative input function is supplemented based on the population-based input function to determine the corrected iterative input function; Based on the corrected iterative input function and the iterative dynamic image, parameter analysis is performed to determine the iterative parameter image; the iterative parameter image refers to an image with parameter values ​​that can reflect the analysis results. The iteration stops and the parameter image is obtained once the preset iteration conditions are met.

5. The system according to claim 4, wherein the preset iteration condition includes iteration convergence or reaching a preset number of iterations.

6. A direct reconstruction apparatus for a parametric image, the apparatus comprising a processor and a memory; the memory for storing instructions, which, when executed by the processor, cause the apparatus to implement the direct reconstruction method for the parametric image as described in any one of claims 1 to 3.

7. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the direct reconstruction method of the parametric image as described in any one of claims 1 to 3.

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