A method and system of image reconstruction

Abstract

This specification provides an image reconstruction method comprising acquiring at least one positron emission tomography (PET) image of an object, wherein at least one PET image is generated based on PET data acquired during an examination, during which a tracer is injected into the object. The method further comprises determining an input function based on the at least one PET image, the input function reflecting changes in tracer concentration within the object during the examination. The method further comprises generating a parameter image based on the at least one PET image and the input function according to a nonlinear parameter estimation algorithm, wherein the parameter image reflects kinetic parameters of the tracer within the object.

Description

[0001] Cross-referencing

[0002] This application claims priority to U.S. Application 17 / 446,299, filed August 29, 2021, the contents of which are incorporated herein by reference. Technical Field

[0003] This application generally relates to methods and systems for image reconstruction, and more specifically, to methods and systems for parametric imaging. Background Technology

[0004] PET technology has been widely used in clinical examinations and medical diagnostics. Compared to standardized uptake value (SUV) imaging, parametric imaging in PET provides more accurate quantitative measurements. For example, parametric imaging can provide voxel-level kinetics of tracer absorption by applying kinetic modeling to each voxel. However, compared to SUV imaging, parametric imaging typically requires longer scan times and more complex protocols, thus limiting its clinical application. Therefore, there is a desire to develop methods and systems for parametric imaging to improve its efficiency. Summary of the Invention

[0005] One embodiment of this specification provides an image reconstruction method. The method is implemented on a computing device having at least one processor and at least one storage device. The method includes acquiring at least one positron emission tomography (PET) image of a subject, wherein at least one PET image is generated based on PET data acquired during an examination, during which a tracer is injected into the subject. The method may further include determining an input function based on the at least one PET image, the input function reflecting changes in tracer concentration within the subject during the examination. The method further includes generating a parameter image based on the at least one PET image and the input function according to a nonlinear parameter estimation algorithm, wherein the parameter image reflects the kinetic parameters of the tracer within the subject.

[0006] In some embodiments, at least one PET image comprises multiple PET images. The method includes acquiring multiple PET images by performing multi-point scanning on an object. To perform multi-point scanning, a tracer may be injected into the object at an initial time point during the examination, and multiple PET scans may be performed on the object over multiple scan periods following the initial time point. Each PET scan in the multiple PET scans is performed within one scan period of the multiple scan periods, and there is a time interval between each pair of adjacent PET scans in the multiple PET scans.

[0007] In some embodiments, the method includes obtaining a reference input function related to the object. The method further includes, in each of a plurality of scan periods, determining candidate input functions based on PET images corresponding to the scan period, the candidate input functions reflecting changes in tracer concentration within the object during the scan period. The method further includes generating an input function by transforming the reference input function based on the plurality of candidate input functions.

[0008] In some embodiments, at least one PET image includes a PET image of an object, and the method includes acquiring the PET image by performing a double-injection scan on the object. The double-injection scan is performed on the object, wherein a first portion of a tracer may be injected into the object at a first time point during the examination, and a second portion of the tracer may be injected into the object at a second time point after the first time point during the examination. The PET scan may be performed within a single scan period, which begins after the first time point and before the second time point, and ends after the second time point.

[0009] In some embodiments, the method includes acquiring a reference input function related to the object. The method further includes determining a first candidate input function based on a PET image, the first candidate input function reflecting changes in tracer concentration within the object during the scan. The method includes determining a second candidate input function based on the first candidate input function, a first portion of the tracer, and a second portion of the tracer, the second candidate input function reflecting changes in tracer concentration within the object over a period of time after a first time point. The method further includes generating an input function by transforming the reference input function based on the first candidate input function and the second candidate input function.

[0010] In some embodiments, the method includes generating a compartment model for simulating tracer dynamics within an object. The method also includes generating a parameter image based on the compartment model and an input function using a nonlinear parameter estimation algorithm.

[0011] In some embodiments, the compartment model can be used to simulate forward transport of the tracer from the subject's plasma to the subject's tissue, backward transport of the tracer from the plasma to the tissue, phosphorylation in the subject's tissue, or dephosphorylation in the subject's tissue.

[0012] In some embodiments, the method includes generating a compartment model, an input function, and a relationship function between at least one PET image. The method further includes generating a parametric image based on the relationship function according to a nonlinear parameter estimation algorithm.

[0013] In some embodiments, the nonlinear parameter estimation algorithm includes the maximum likelihood estimation (MLE) algorithm.

[0014] In some embodiments, the tracer comprises 18F fluorodeoxyglucose (FDG).

[0015] In some embodiments, the parameter image includes a Ki image.

[0016] One embodiment of this specification provides an image reconstruction system, the system including at least one storage device for storing executable instructions, and at least one processor communicating with the at least one storage device. When the executable instructions are executed, the at least one processor causes the system to perform operations. The system includes acquiring at least one positron emission tomography (PET) image of an object, wherein at least one PET image is generated based on PET data acquired during an examination, during which a tracer is injected into the object. The system further includes determining an input function based on the at least one PET image, the input function reflecting changes in tracer concentration within the object during the examination. The system further includes generating a parametric image based on the at least one PET image and the input function according to a nonlinear parameter estimation algorithm, wherein the parametric image reflects kinetic parameters of the tracer within the object.

[0017] One embodiment of this specification provides a computer-readable storage medium storing computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes an image reconstruction method as described in any of the above embodiments. The method includes acquiring at least one positron emission tomography (PET) image of an object, wherein at least one PET image is generated based on PET data acquired during an examination, during which a tracer is injected into the object. The method may further include determining an input function based on the at least one PET image, the input function reflecting changes in tracer concentration within the object during the examination. The method further includes generating a parameter image based on the at least one PET image and the input function according to a nonlinear parameter estimation algorithm, wherein the parameter image reflects kinetic parameters of the tracer within the object.

[0018] One embodiment of this specification provides an image reconstruction method. The method is implemented on a computing device having at least one processor and at least one storage device. The method includes acquiring at least one positron emission tomography (PET) image of a subject, wherein at least one PET image is generated based on PET data acquired during the examination, during which a tracer is injected into the subject. Multi-point scanning or dual-injection scanning is performed on the subject, and the total time during one or more of the multi-point scanning or dual-injection scanning is less than or equal to 10 minutes. The method further includes generating a parametric image based on at least one PET image according to a nonlinear parameter estimation algorithm, wherein the parametric image reflects the kinetic parameters of the tracer within the subject.

[0019] In some embodiments, at least one PET image includes multiple PET images. The method includes acquiring multiple PET images by performing multi-point scanning on the object. A tracer may be injected into the object at an initial time point during the examination. Multiple PET scans may be sequentially performed on the object over multiple scan periods following the initial time point. Each PET scan in the multiple PET scans is performed within one scan period of the multiple scan periods, and there is a time interval between each pair of adjacent PET scans in the multiple PET scans.

[0020] In some embodiments, the method includes registering multiple PET images.

[0021] In some embodiments, at least one PET image includes a PET image of the object. The method includes acquiring PET images by performing a double-injection scan on the object. A first portion of the tracer may be injected into the object at a first time point during the examination, and a second portion of the tracer may be injected into the object at a second time point after the first time point during the examination. The PET scan is performed within a single scan period, which begins after the first time point and before the second time point, and ends after the second time point.

[0022] In some embodiments, the method includes determining an input function based on at least one PET image, the input function being used to reflect changes in tracer concentration in the subject's body during the examination.

[0023] In some embodiments, the parameter image includes a Ki image.

[0024] In traditional parametric imaging, parametric images of the subject are typically acquired through long (e.g., tens of minutes) consecutive PET scans, and Patlak models can be used to determine these images (e.g., Ki images). Patlak models are linear models, and Ki corresponds to the slope of a linear model. However, if the imaging time is insufficient (e.g., less than 10 minutes), there may not be enough PET data to determine parametric images. According to some embodiments of this specification, parametric images can be generated based on PET data acquired within a relatively short imaging time (e.g., less than or equal to 10 minutes) using a nonlinear parameter estimation algorithm (e.g., maximum likelihood estimation). Compared to traditional parametric imaging methods (e.g., parametric imaging using Patlak models), the methods and systems disclosed in this specification can generate parametric images within a relatively short imaging time (e.g., less than or equal to 10 minutes), thereby improving imaging efficiency and promoting the clinical application of parametric imaging.

[0025] Some of the additional features of this application will be described in the following description. Some of these additional features will be apparent to those skilled in the art from the study of the following description and the accompanying drawings, or from an understanding of the production or operation of the embodiments. The features of this application can be implemented and achieved through the practice or use of various methods, means, and combinations of the specific embodiments described below. Attached Figure Description

[0026] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting exemplary embodiments, and in these embodiments, the same reference numerals denote similar structures, wherein:

[0027] Figure 1 These are schematic diagrams of exemplary imaging systems according to some embodiments of this specification;

[0028] Figure 2 These are schematic diagrams of exemplary hardware and / or software components of a computing device according to some embodiments of this specification;

[0029] Figure 3 These are schematic diagrams of exemplary hardware and / or software components of a mobile device according to some embodiments of this specification;

[0030] Figure 4 These are schematic diagrams of exemplary processing devices according to some embodiments of this specification;

[0031] Figure 5 This is an exemplary flowchart illustrating the generation of parameter images according to some embodiments of this specification;

[0032] Figure 6This is an exemplary flowchart illustrating the generation of parameter images according to some embodiments of this specification;

[0033] Figure 7A This is a schematic diagram illustrating an exemplary multi-point scan according to some embodiments of this specification;

[0034] Figure 7B This is a schematic diagram of an exemplary dual-injection scan according to some embodiments of this specification;

[0035] Figure 8 This is a schematic diagram of an exemplary object Ki image shown according to some embodiments of this specification. Detailed Implementation

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. However, those skilled in the art should understand that this application can be implemented without these details. In other instances, to avoid unnecessarily obscuring various aspects of this application, well-known methods, processes, systems, components, and / or circuits have been described at a higher level. It will be apparent to those skilled in the art that various changes can be made to the disclosed embodiments, and the general principles defined in this application can be applied to other embodiments and application scenarios without departing from the principles and scope of this application. Therefore, this application is not limited to the embodiments shown, but conforms to the broadest scope consistent with the claims.

[0037] The terminology used in this specification is for the purpose of describing specific exemplary embodiments only and does not limit the scope of this specification. The singular forms “a,” “an,” and “the” used herein may also include the plural forms, unless the context clearly indicates otherwise. The terms “and / or” and “at least one of” as used herein include any and all combinations of one or more of the associated listed items. It should also be understood that, as in this specification, the terms “comprising” and / or “including” indicate only the presence of the stated features, integrals, steps, operations, components, and / or parts, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, components, parts, and / or combinations thereof. Furthermore, “exemplary” means example or illustration.

[0038] It should be understood that the terms “system,” “unit,” “module,” and / or “block” used in this specification are methods of distinguishing different components, elements, parts, sections, or assemblies at different levels in ascending order. However, if these terms serve the same purpose, they may be replaced by another term.

[0039] Generally, the terms "module," "unit," or "block" as used herein refer to logic embodied in hardware or firmware, or a collection of software instructions. The modules, units, or blocks described herein can be implemented as software and / or hardware and can be stored on any type of non-transitory computer-readable medium or other storage device. In some embodiments, software modules / units / blocks can be compiled and linked into an executable program. It should be understood that software modules can be invoked from other modules / units / blocks or from themselves, and / or can be invoked in response to detected events or interrupts. Software modules / units / blocks configured for execution on a computing device can be provided on computer-readable media, such as optical discs, digital video discs, flash drives, disks, or any other tangible media, or as digital downloads (and may initially be stored in a compressed or installable format, requiring installation, decompression, or decryption before execution). The software code herein can be stored, in part or in whole, in the storage device of the computing device performing the operation and applied in the operation of the computing device. Software instructions can be embedded in firmware, such as EPROM. It should also be understood that hardware modules / units / blocks may include connected logical components, such as gates and flip-flops, and / or may include programmable units, such as programmable gate arrays or processors. The modules / units / blocks or computing device functions described herein can be implemented as software modules / units / blocks, but can be represented in hardware or firmware. Generally, the modules / units / blocks described herein refer to logical modules / units / blocks, which can be combined with other modules / units / blocks or divided into sub-modules / sub-units / sub-blocks, regardless of their physical organization or storage method. This application may be applied to systems, engines, or parts thereof.

[0040] It should be understood that although the terms "first," "second," "third," etc., may be used herein to describe various elements, these elements should not be limited by the terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element without departing from the scope of the exemplary embodiments of this application.

[0041] Various terms are used to describe spatial and functional relationships between elements, including “connection,” “attachment,” and “installation.” Unless explicitly described as “direct” when describing a relationship between the first and second elements in this application, the relationship includes a direct relationship where no other intervening element exists between the first and second elements, and may also include an indirect relationship where one or more intervening elements exist between the first and second elements (spatially or functionally). Conversely, when an element is referred to as being “directly connected,” attached, or positioned to another element, no intervening element exists. Other words used to describe relationships between elements should be interpreted in a similar manner (e.g., “between both” vs. “directly between both,” “adjacent” vs. “directly adjacent”).

[0042] These and other features, characteristics, functions and operating methods of related structural elements, as well as component assembly and manufacturing economics, will become more apparent from the following description of the accompanying drawings, which form part of this application specification. However, it should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of this application. It should also be understood that the drawings are not drawn to scale.

[0043] The term “image” in this specification is used collectively to refer to image data (e.g., scanned data, projected data) and / or images of various forms, including two-dimensional (2D) images, three-dimensional (3D) images, four-dimensional (4D) images, etc. The terms “pixel” and “voxel” in this specification are used interchangeably to refer to an element of an image. The term “anatomical structure” in this specification can refer to a gas (e.g., air), liquid (e.g., water), solid (e.g., stone), cell, tissue, organ, or any combination thereof that can be displayed in an image and actually exist in or on the body of an object. The terms “region,” “location,” and “area” in this specification can refer to the location of an anatomical structure displayed in an image, or the actual location of an anatomical structure existing within or on the body of an object, because an image can indicate the actual location of a certain anatomical structure existing within or on the body of an object.

[0044] This specification provides imaging systems and components that can be used. In some embodiments, the imaging system may include a single-modal imaging system and / or a multimodal imaging system. A single-modal imaging system may include, for example, a PET system, a SPECT system, or any combination thereof. A multimodal imaging system may include a positron emission tomography-X-ray (PET-X-ray) system, a single-photon emission computed tomography-magnetic resonance imaging (SPECT-MRI) system, a positron emission tomography-computed tomography (PET-CT) system, a digital subtraction angiography-magnetic resonance imaging (DSA-MRI) system, etc. It should be noted that the imaging systems described below are provided for illustrative purposes only and are not intended to limit the scope of this specification.

[0045] This specification relates, in one aspect, to methods and systems for generating parametric images. According to some embodiments of the specification, the processing apparatus can acquire at least one PET image of an object. At least one PET image can be generated from PET data acquired by injecting a tracer into the object during inspection. For example, at least one PET image can be acquired by performing a multi-point scan or a double-injection scan on the object. A multi-point scan can be achieved by performing multiple consecutive PET scans on the object after injecting the tracer into the object at an initial time point during inspection. A double-injection scan can be achieved by injecting the tracer into the object twice and performing a single PET scan, wherein the PET scan can begin between the first and second injections and end after the second injection.

[0046] The processing device can determine an input function based on at least one PET image, which reflects changes in tracer concentration within the subject during examination. For example, the input function can be determined based on an image-based input function (also called a candidate input function) and a population-based input function (also called a reference input function). As described in this specification, an image-based input function refers to an input function determined based on one or more PET images of the subject. A population-based input function refers to an input function for the subject that is determined based on multiple sample input functions corresponding to multiple sample subjects other than the subject itself.

[0047] The processing device can further generate parametric images (e.g., Ki images) based on the input function and at least one PET image. The parametric images can reflect the kinetic parameters of the tracer within the organism. For example, the parametric images can be generated based on a compartment model, the input function, and at least one PET image according to a nonlinear parameter estimation algorithm (e.g., maximum likelihood estimation algorithm).

[0048] Traditionally, parametric images of a subject are acquired through long (e.g., tens of minutes) consecutive PET scans, and Patlak models can be used to determine these images (such as Ki images). Patlak models are linear models, and Ki corresponds to the slope of a linear model. However, if the imaging time is insufficient (e.g., less than 10 minutes), there may not be enough PET data to determine parametric images. According to some embodiments of this specification, parametric images can be generated based on PET data acquired within a relatively short imaging time (e.g., less than or equal to 10 minutes) using a nonlinear parameter estimation algorithm (e.g., maximum likelihood estimation algorithm). Compared to conventional methods (e.g., parametric imaging using Patlak models), the methods and systems disclosed in this specification can generate parametric images within a relatively short imaging time (e.g., less than or equal to 10 minutes), thereby improving imaging efficiency and promoting the clinical application of parametric imaging.

[0049] Figure 1 This is a schematic diagram of an exemplary imaging system according to some embodiments of this specification. As shown, the imaging system 100 may include an imaging device 110, a processing device 120, a storage device 130, a terminal 140, and a network 150. The components of the imaging system 100 may be connected in one or more ways. This is merely an example. Figure 1 As shown, imaging device 110 can be directly connected to processing device 120, as indicated by the dashed double-headed arrow connecting imaging device 110 and processing device 120 in the figure, or connected to processing device 120 via network 150. As another example, storage device 130 can be directly connected to imaging device 110, as indicated by the dashed double-headed arrow connecting imaging device 110 and storage device 130 in the figure, or connected to storage device 130 via network 150. As another example, terminal 140 can be directly connected to processing device 120, as indicated by the dashed double-headed arrow connecting terminal 140 and processing device 120, or connected to processing device 120 via network 150.

[0050] In some embodiments, the imaging device 110 may scan an object to obtain object-related data. In some embodiments, the imaging device 110 may be an emission computed tomography (ECT) device, a positron emission tomography (PET) device, a single-photon emission computed tomography (SPECT) device, a multimodal device, or any combination thereof. Exemplary multimodal devices may include CT-PET devices, MR-PET devices, etc. In some embodiments, the multimodal imaging device may include modules and / or components for performing PET imaging and / or related analyses.

[0051] In some embodiments, the imaging device 110 may be a PET scanner, including a frame 111, a detector 112, a detection area 113, and a scanning bed 114. The frame 111 may support the detector 112. Figure 1 As shown, an object can be placed on the scanning bed 114 and moved to the detection area 113 for scanning along the Z-axis. The detector 112 can detect radiation events (e.g., gamma photons) emitted from the detection area 113. In some embodiments, the detector 112 may include one or more detector elements. The detector 112 may include a scintillation detector (e.g., a cesium iodide detector), a gas detector, etc. The detector 112 may be and / or include a single-row detector arranging multiple detector elements in a single row and / or a multi-row detector arranging multiple detector elements in multiple rows.

[0052] The object can be biological or non-biological. For example, an object can include a patient, a man-made object, etc. As another example, an object can include a specific part, organ, and / or tissue of a patient. Specifically, an object can include the head, neck, chest, heart, stomach, blood vessels, soft tissue, tumor, etc., or any combination thereof. In this specification, the terms "object" and "object" are used interchangeably.

[0053] Processing device 120 can process data and / or information acquired from imaging device 110, storage device 130, and / or terminal 140. For example, processing device 120 can acquire at least one PET image of an object. As another example, processing device 120 can determine an input function based on at least one PET image. As another example, processing device 120 can generate a parametric image based on the input function and at least one PET image.

[0054] In some embodiments, processing device 120 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processing device 120 may be local or remote. For example, processing device 120 may access information and / or data from imaging device 110, storage device 130, and / or terminal 140 via network 150. As another example, processing device 120 may be directly connected to imaging device 110, terminal 140, and / or storage device 130 to access information and / or data. In some embodiments, processing device 120 may be implemented on a cloud platform. For example, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, or any combination thereof. In some embodiments, processing device 120 may be part of terminal 140. In some embodiments, processing device 120 may be part of imaging device 110.

[0055] Storage device 130 may store data, instructions, and / or any other information. In some embodiments, storage device 130 may store data acquired from imaging device 110, processing device 120, and / or terminal 140. This data may include image data acquired by processing device 120, algorithms and / or models for processing the image data, etc. For example, storage device 130 may store PET images of objects acquired from a PET device (e.g., imaging device 110). As another example, storage device 130 may store an input function determined by processing device 120. As yet another example, storage device 130 may store a parametric image generated by processing device 120.

[0056] In some embodiments, storage device 130 may store data and / or instructions that processing device 120 and / or terminal 140 may execute or be used to execute the exemplary methods described herein. In some embodiments, storage device 130 may include mass storage devices, removable storage devices, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage devices may include disks, optical disks, solid-state drives, etc. Exemplary removable storage devices may include flash drives, floppy disks, optical disks, memory cards, compressed disks, magnetic tapes, etc. Exemplary volatile read-write memory may include random access memory (RAM). Exemplary RAM may include dynamic RAM (DRAM), double data rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and zero-capacitance RAM (Z-RAM), etc. Exemplary ROM may include mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically erasable programmable ROM (EEPROM), optical disk ROM, or digital multifunction disk ROM, etc. In some embodiments, storage device 130 may be implemented on a cloud platform. As an example only, a cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, etc., or any combination thereof.

[0057] In some embodiments, storage device 130 may be connected to network 150 to communicate with one or more other components in imaging system 100 (e.g., processing device 120, terminal 140). One or more components in imaging system 100 may access data or instructions stored in storage device 130 via network 150. In some embodiments, storage device 130 may be integrated into imaging device 110.

[0058] Terminal 140 can connect to and / or communicate with imaging device 110, processing device 120, and / or storage device 130. In some embodiments, terminal 140 may include mobile device 141, tablet computer 142, laptop computer 143, or any similar combination. For example, mobile device 141 may include mobile phone, personal digital assistant (PDA), gaming device, navigation device, point-of-sale (POS) device, laptop computer, tablet computer, desktop computer, or any combination thereof. In some embodiments, terminal 140 may include input devices, output devices, etc. Input devices may include alphanumeric keys and other keys that can be input via a keyboard, touchscreen (e.g., with haptic or haptic feedback), voice input, eye-tracking input, brain monitoring system, or any other similar input mechanism. Other types of input devices may include cursor control devices, such as a mouse, trackball, or cursor arrow keys. Output devices may include a display, printer, or any combination thereof.

[0059] Network 150 may include any suitable network that can facilitate the exchange of information and / or data between imaging system 100. In some embodiments, one or more components of imaging system 100 (e.g., imaging device 110, processing device 120, storage device 130, terminal 140, etc.) may communicate information and / or data with one or more other components of imaging system 100 via network 150. For example, processing device 120 and / or terminal 140 may acquire PET images from imaging device 110 via network 150. As another example, processing device 120 and / or terminal 140 may acquire information stored in storage device 130 via network 150. Network 150 may be and / or include public networks (e.g., the Internet), private networks (e.g., local area networks (LANs), wide area networks (WANs), etc.), wired networks (e.g., Ethernet networks), wireless networks (e.g., I1 networks, Wi-Fi networks, etc.), cellular networks (e.g., LTE networks), Frame Relay networks, virtual private networks (“VPNs”), satellite networks, telephone networks, routers, hubs, switches, server computers, and / or any combination thereof. By way of example only, network 150 may include cable networks, wired networks, fiber optic networks, telecommunications networks, intranets, wireless local area networks (WLANs), metropolitan area networks (MANs), public switched telephone networks (PSTNs), Bluetooth networks, ZigBee networks, near field communication (NFC) networks, and any combination thereof. In some embodiments, network 150 may include one or more network access points. For example, network 150 may include wired and / or wireless network access points, such as base stations and / or internet switching points, through which one or more components of imaging system 100 may connect to network 150 to exchange data and / or information.

[0060] This description is illustrative and does not limit the scope of this specification. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other features of the exemplary embodiments described herein can be combined in various ways to obtain other and / or alternative exemplary embodiments. However, these variations and modifications do not exceed the scope of this disclosure. In some embodiments, the imaging system 100 may include one or more additional components and / or one or more components of the imaging system 100 may be omitted. Additionally, two or more components of the imaging system 100 may be integrated into a single component. Components of the imaging system 100 may be implemented on two or more sub-components.

[0061] Figure 2 This is a schematic diagram of exemplary hardware and / or software components of a computing device 200 according to some embodiments of this specification. In some embodiments, one or more components of the imaging system 100 (e.g., processing device 120, terminal 140) may be implemented on the computing device 200. Figure 2 As shown, computing device 200 may include processor 210, storage device 220, input / output (I / O) 230 and communication port 240.

[0062] Processor 210 can execute computer instructions (program code) and perform the functions of processing device 120 according to the techniques described herein. Computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform specific functions described herein. For example, processor 210 can process imaging data obtained from imaging device 110, terminal 140, storage device 130, and / or any other component of imaging system 100. In some embodiments, processor 210 may include one or more hardware processors, such as microcontrollers, microprocessors, reduced instruction set computers (RISC), application-specific integrated circuits (ASICs), application-specific instruction set processors (ASIPs), central processing units (CPUs), graphics processing units (GPUs), physical processing units (PPUs), microcontroller units, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), advanced RISC machines (ARMs), programmable logic devices (PLDs), and any circuits and processors capable of performing one or more functions, or any combination thereof.

[0063] For illustrative purposes only, only one processor is described in computing device 200. However, it should be noted that computing device 200 in this specification may also include multiple processors, and therefore the operations and / or method steps described herein, which are performed by one processor, may also be performed jointly or separately by multiple processors. For example, if, in this application, the processors of computing device 200 simultaneously execute operation A and operation B, it should be understood that operation A and operation B may also be performed jointly or separately by two or more different processors in computing device 200 (e.g., the first processor executes operation A, the second processor executes operation B, or the first and second processors jointly execute operation A and B).

[0064] Storage device 220 can store data / information obtained from imaging device 110, terminal 140, storage device 130, and / or any other component of imaging system 100. Storage device 220 can be connected to... Figure 1 Similar to the storage device 130 described herein, a detailed description is not repeated here.

[0065] Input / output 230 can input and / or output signals, data, information, etc. In some embodiments, input / output 230 enables a user to interact with processing device 120. In some embodiments, input / output 230 may include input devices and output devices. Exemplary input devices may include a keyboard, mouse, touchscreen, microphone, trackball, etc., or any combination thereof. Exemplary output devices may include a display device, speaker, printer, projector, etc., or any combination thereof. Exemplary display devices may include a liquid crystal display (LCD), a light-emitting diode (LED) based display, a flat panel display, a curved display, a television device, a cathode ray tube (CRT), etc., or any combination thereof.

[0066] Communication port 240 can be connected to a network (e.g., network 150) to facilitate data communication. Communication port 240 can establish a connection between processing device 120 and imaging device 110, terminal 140, or storage device 130. The connection can be a wired connection, a wireless connection, any other communication connection that enables data transmission and / or reception, and / or any combination thereof. Wired connections may include cables, optical fibers, telephone lines, etc., or any combination thereof. Wireless connections may include Bluetooth connections, Wi-Fi connections, WiMax connections, WLAN connections, ZigBee connections, mobile network connections (e.g., 3G, 4G, 5G), etc., or any combination thereof. In some embodiments, communication port 240 can be a standardized communication port, such as RS232, RS485, etc. In some embodiments, communication port 240 can be a specially designed communication port. For example, communication port 240 can be designed according to the Medical Digital Imaging and Communication (DICOM) protocol.

[0067] Figure 3 This is a schematic diagram of exemplary hardware and / or software components of a mobile device 300 according to some embodiments of this specification. In some embodiments, a terminal 140 and / or a processing device 120 may be implemented on the mobile device 300, respectively.

[0068] like Figure 3 As shown, the mobile device 300 may include a communication platform 310, a display 320, a graphics processing unit (GPU) 330, a central processing unit (CPU) 340, an input / output 350, a memory 360, and a storage 390. In some embodiments, any other suitable components, including but not limited to a system bus or controller (not shown), may also be included in the mobile device 300.

[0069] In some embodiments, the communication platform 310 may be configured to establish a connection between the mobile device 300 and other components of the imaging system 100, and to enable the transmission of data and / or signals between the mobile device 300 and other components of the imaging system 100. For example, the communication platform 310 may establish a wireless connection between the mobile device 300 and the imaging device 110, and / or the processing device 120. The wireless connection may include, for example, a Bluetooth connection, a Wi-Fi connection, a WiMax connection, a WLAN connection, a ZigBee connection, a mobile network connection (e.g., 3G, 4G, 5G, etc.), or any combination thereof. The communication platform 310 may also enable the transmission of data and / or signals between the mobile device 300 and other components of the imaging system 100. For example, the communication platform 310 may transmit user-inputted data and / or signals to other components of the imaging system 100. The input data and / or signals may include user instructions. As another example, the communication platform 310 may receive data and / or signals transmitted from the processing device 120. The received data and / or signals may include imaging data acquired by the detector of the imaging device 110.

[0070] In some embodiments, a mobile operating system 370 (e.g., iOS, Android, Windows Phone, etc.) and at least one or more applications 380 may be loaded from storage 390 into memory 360 for execution by CPU 340. Applications 380 may include a browser or any other suitable mobile application for receiving and presenting information relating to the imaging system 100. User interaction with the information stream may be achieved via input / output 350 and provided to the processing device 120 and / or other components of the imaging system 100 via network 150.

[0071] To implement the various modules, units, and functions described in this specification, a computer hardware platform may be used as the hardware platform for one or more of the elements described in this specification. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of workstation or terminal. If properly programmed, the computer may also be used as a server. Those skilled in the art will be familiar with the structure, programming, and general operation of this computer device; therefore, the accompanying drawings should be self-evident.

[0072] Figure 4 This is a schematic diagram of an exemplary processing device according to some embodiments of this specification. In some embodiments, the processing device 120 may include an acquisition module 410, a determination module 420, and a generation module 430.

[0073] The acquisition module 410 can be configured to acquire data and / or information related to the imaging system 100. This data and / or information may include PET images of the object, input functions of the object (e.g., reference input function, candidate input function), parametric images, compartment models, relational functions, etc., or any combination thereof. For example, the acquisition module 410 can acquire at least one PET image of the object. At least one PET image can be acquired by performing a multi-point scan or a dual-injection scan on the object. Further description of acquiring at least one PET image can be found in other parts of this specification (e.g., Figure 5 , Figure 7A , Figure 7B (and its description). As another example, the acquisition module 410 can acquire a reference input function of the object. In some embodiments, the acquisition module 410 can obtain data and / or information related to the imaging system 100 from one or more components of the imaging system 100 (e.g., terminal 140, storage device 130, imaging device 110) via network 150.

[0074] The determining module 420 can be configured to determine an input function. The input function can reflect changes in tracer concentration within the subject during the examination. In some embodiments, the determining module 420 can determine the input function based on at least one PET image of the subject. For example, the determining module 420 can determine candidate input functions based on PET images corresponding to the scanning period, the candidate input functions reflecting changes in tracer concentration within the subject during the scanning period. As another example, the determining module 420 can determine the input function by transforming a reference input function based on multiple candidate input functions. Further description of determining the input function can be found in other parts of this specification (e.g., Figure 5 , 7A 7B and its description).

[0075] The generation module 430 can be configured to generate a parametric image based on an input function and at least one PET image. The parametric image can reflect the kinetic parameters of the tracer within the object. In some embodiments, the generation module 430 can generate a compartment model for simulating tracer kinetics within the object. In some embodiments, the generation module 430 can generate a parametric image based on the compartment model, the input function, and at least one PET image. For example, the generation module 430 can generate a relationship function between the compartment model, the input function, and at least one PET image. The generation module 430 can generate the parametric image based on the relationship function according to a maximum likelihood estimation algorithm. Further description of generating the parametric image can be found in other parts of this specification (e.g., Figure 5 , Figure 6 (and its description).

[0076] It should be noted that the above description of the processing device 120 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this specification. In some embodiments, one or more modules may be combined into a single module. For example, the determining module 420 and the generating module 430 may be combined into a single module that can simultaneously determine the input function and the parameter graph. In some embodiments, one or more modules may be added to or omitted from the processing device 120. For example, the processing device 120 may further include a storage module (not included in the description). Figure 4 As shown in the figure, the storage module is configured to store data and / or information (e.g., PET data, input functions, parametric images) related to the imaging system 100.

[0077] Figure 5 This is an exemplary flowchart illustrating the generation of parameter images according to some embodiments of this specification. In some embodiments, process 500 can be... Figure 1 This is implemented in the imaging system 100 shown. For example, process 500 can be stored as instructions in storage device 130 and / or storage devices (e.g., storage device 220, memory 390) and processed by processing device 120 (e.g., such as...). Figure 2 The processor 210 of the computing device 200 shown, such as Figure 3 The CPU 340 of the mobile device 300 shown is as follows: Figure 4 One or more modules shown may be invoked and / or executed. The operations of the procedures shown below are for illustrative purposes only. In some embodiments, process 500 may be accomplished using one or more additional operations not described and / or by omitting one or more operations discussed. Furthermore, Figure 5 The sequence of operations in process 500 shown and the following description are not intended to be limiting.

[0078] In step 510, the processing device 120 (e.g., acquisition module 410) may acquire at least one PET image of the object.

[0079] At least one PET image may include 2D images, 3D images, 4D images (also known as dynamic images) (e.g., a series of 3D images that change over time) and / or any associated image data (e.g., scan data, projection data), etc.

[0080] In some embodiments, at least one PET image may be generated based on PET data acquired during the examination, during which a tracer (also referred to as a "PET tracer molecule" or "PET tracer") is injected into the object. The tracer may undergo positron emission decay and emit positrons. Positrons and electrons have the same mass and opposite charge, and when the two particles collide, the positron and electron (which are abundant in the object) can annihilate (also referred to as an "annihilation event" or "coincidence event"). Electron-positron annihilation can cause the two particles (e.g., two 511 keV gamma photons) to begin traveling in opposite directions. During the PET scan of the object, the particles produced by the annihilation event can reach and be detected by the detector unit of the PET scanner. The detector unit can obtain information about the particles (e.g., time information, trajectory information) (also referred to as "PET data"). In some embodiments, PET data may include list pattern data or sine wave data.

[0081] The distribution of tracers can indicate bioactivity information within a target organism. For example, one or more atoms of the tracer can chemically bind to the bioactive molecules of the target organism. The active molecules may be concentrated in the tissue of interest of the target organism. Tracers may include [15O]H₂O, [15O]butanol, [11C]butanol, [18F]fluorodeoxyglucose (FDG), [64Cu]diacetyl-diacetyl (64Cu-ATSM), [18F]fluoride, 3'-deoxy-3'-[18F]fluorothymidine (FLT), [18F]-fluoronitroimidazole (FMISO), gallium, thallium, etc., or any combination thereof.

[0082] In some embodiments, processing device 120 may acquire PET data from one or more components of imaging system 100 (e.g., imaging device 110, terminal 140, and / or storage device 130) or an external storage device via network 150. For example, imaging device 110 may transmit the acquired PET data (e.g., projection data) to a storage device (e.g., storage device 130 or an external storage device) for storage. Processing device 120 may acquire PET data from the storage device. As another example, processing device 120 may acquire PET data directly from imaging device 110. In some embodiments, imaging device 110 may acquire PET data and transmit PET data to processing device 120 substantially in real time. Alternatively, processing device 120 may acquire PET data (e.g., from a storage device) after a period of time has elapsed since the PET data was collected.

[0083] After obtaining PET data, the processing device 120 can generate at least one PET image based on the PET data according to one or more image reconstruction algorithms. The at least one PET image can show the process of an object taking up tracer. Exemplary image reconstruction algorithms may include iterative algorithms, analytical algorithms, etc. Iterative algorithms may include maximum likelihood estimation (MLE) algorithms, ordered subset expectation maximization (OSEM) algorithms, and 3D reconstruction algorithms, etc. Analytical algorithms may include filtered back projection (FBP) algorithms.

[0084] In some embodiments, at least one PET image may include multiple PET images. Multiple PET images can be obtained by performing multi-point scanning of the object during the examination. As an example, all tracers may be injected into the object at an initial time point during the examination. Multiple PET scans may be performed consecutively on the object during multiple scan periods following the initial time point. Each PET scan in the multiple PET scans is performed within one scan period of the multiple scan periods to obtain a set of PET data for the object. The set of PET data obtained in a single PET scan can be used to reconstruct one of the multiple PET images. In some embodiments, the total time of the multiple scan periods of the multi-point scanning may be less than or equal to 10 minutes.

[0085] In some embodiments, the number (or number of) PET scans in a multi-point scan, the duration of each PET scan, and / or the time interval between adjacent PET scans can be manually set by the user of the imaging system 100, or determined by one or more components of the imaging system 100 (e.g., processing device 120) depending on the circumstances. In some embodiments, the duration of each PET scan can be the same or different. For example, each PET scan can last 5 minutes. Further description of multi-point scanning can be found in other parts of this specification, such as... Figure 7A And its related descriptions.

[0086] In some embodiments, at least one PET image may comprise a single PET image. A single PET image can be obtained by performing a double-injection scan on the subject. For example, a first portion of the tracer may be injected into the subject at a first time point during the examination, and a second portion of the tracer may be injected into the subject at a second time point after the first time point during the examination. The first and second portions of the tracer may be the same or different. For example, the ratio of the first and second portions of the tracer may be equal to 0.8, 0.9, 1, 1.1, 1.2, etc. The PET scan is performed within a single scan period, which begins after the first time point and before the second time point, and ends after the second time point. In some embodiments, the scan period for a double-injection scan may be less than or equal to 10 minutes.

[0087] In some embodiments, the time interval between the first and second time points and / or the duration of the PET scan can be manually set by the user of the imaging system 100, or determined by one or more components of the imaging system 100 (e.g., processing device 120) depending on the circumstances. Further description of multi-point scanning can be found in other parts of this specification, for example, Figure 7B And its related descriptions.

[0088] In step 520, the processing device 120 (e.g., determination module 420) may determine an input function based on at least one PET image, which reflects the concentration change of tracer in the subject during the examination.

[0089] In some embodiments, the input function may be represented as a time-activity curve (TAC) associated with the tracer. For example, the input function may be represented as a plasma TAC and / or a blood TAC, where the plasma TAC represents the concentration change of the tracer in plasma and the blood TAC represents the concentration change of the tracer in blood.

[0090] In some embodiments, the input functions determined in step 520 may include multiple input functions corresponding to different parts of the object. As an example only, the processing device 120 may determine the input function for each physical point of the object. A physical point of the object refers to a portion of the object that corresponds to an element (e.g., a pixel or voxel) in at least one PET image. The input functions corresponding to different parts of the object may differ due to dispersion effects, time delay effects, etc. Dispersion effects and time delay effects may be caused by blood circulation. Specifically, dispersion effects may be caused by factors such as uneven blood flow velocity in different blood vessels of the object. Time delay effects may be caused by the distance between the blood collection site (e.g., injection site) and a specific organ or tissue of the object. Due to dispersion effects and time delay effects, different parts of the object may have different tracer concentrations at the same time, resulting in different input functions. A further description of the time delay effect can be found in Chinese Patent Application No. 201910383290.5, filed June 6, 2019, entitled "Image Reconstruction Method, Apparatus, Medical Imaging Device, and Storage Medium," the contents of which are incorporated herein by reference.

[0091] In some embodiments, an input function (e.g., plasma TAC) can be obtained using arterial sampling techniques, image-based input function techniques, population-based input function techniques, venous blood sampling methods, or any combination thereof. Using arterial sampling techniques, arterial blood of an object can be sampled to measure the object's input function. Using image-based input function techniques, the object's input function can be determined based on one or more PET images (e.g., at least one PET image determined in step 510). For example, processing device 120 can determine a Region of Interest (ROI) (e.g., a region associated with the heart or arterial blood) from each of the one or more PET images. Processing device 120 can determine a blood TAC based on the ROI identified from each PET image and designate the blood TAC as a plasma TAC. A plasma TAC determined based on one or more PET images can also be referred to as an image-based input function. Using population-based input function techniques, the object's input function can be determined based on multiple sample input functions of multiple sample objects (e.g., patients). The sample input function can be determined based on arterial sampling techniques. For example, the object's input function can be obtained by normalizing and / or averaging multiple sample input functions of multiple sample objects. Venous blood sampling technology can be used to extract venous blood samples at a certain time after injection and to determine the correct scaling factor for population-based input functions or image-generated input functions.

[0092] In some embodiments, the similarity between each sample object and the object may be higher than a similarity threshold (e.g., 90%, 95%). The similarity between sample objects can be determined based on the feature information of the sample object and the feature information of the object. The feature information may include gender, age, body size (e.g., thickness, height, width), physiological state (e.g., cardiac output), or any other feature that may affect the tracer's metabolic rate. For example, the processing device 120 may determine the average input function of the sample input function of the sample object as the population-based input function of the object. As another example, the processing device 120 may select the sample input function of the sample object with the highest similarity to the object from a plurality of sample input functions. The processing device 120 may then designate the selected sample input function as the population-based input function of the object. As another example, the processing device 120 may modify the selected sample input function (e.g., modify the shape of the selected sample input function) based on the sample object corresponding to the selected sample input function and the feature information of the object, such as the difference in cardiac output between sample objects and objects. The processing device 120 may further designate the modified sample input function as the population-based input function of the object. Therefore, determining the crowd-based input function for an object based on its characteristic information can improve the accuracy of the crowd-based input function.

[0093] In some embodiments, the at least one PET image acquired in step 510 may include multiple PET images acquired through multi-point scanning. Further description of the input function for determining the multi-point scanning can be found elsewhere in this specification, for example, Figure 7A And related descriptions. Alternatively, at least one PET image acquired in step 510 may include a single PET image acquired via a dual-injection scan. Further description of the input function for determining the dual-injection scan can be found elsewhere in this specification, for example, Figure 7B And its related descriptions.

[0094] In step 530, the processing device 120 (e.g., generation module 430) can generate a parametric image based on at least one PET image and an input function.

[0095] In some embodiments, parametric images can reflect the kinetic parameters of the tracer in the subject's body. In this specification, "kinetic parameters" refers to physiological parameters related to tracer kinetics after the tracer is injected into the subject's body. For example, kinetic parameters may include the tracer transport rate from plasma to tissues (or the tracer's K1 parameter), the tracer transport rate from tissues to plasma (or the tracer's K2 parameter), the plasma concentration in the tissue, the tracer perfusion rate, the tracer's receptor binding potential, the tracer's Ki parameter, or any other combination thereof. Parametric images can help assess the physiology (function) and / or anatomy (structure) of the subject's organ and / or tissue.

[0096] In some embodiments, the parameter image may represent the values ​​of a kinetic parameter corresponding to one or more time points during the inspection. For example, the parameter image may include one or more static images corresponding to one or more time points. As another example, the parameter image may include a dynamic parameter image, such as a Graphical Interchange Format (GIF) image reflecting the change of kinetic parameters over time.

[0097] In some embodiments, the processing device 120 may generate a compartmental model (e.g., a two-tissue compartmental model) for simulating tracer kinetics within a subject. Further, the processing device 120 may generate a parametric image based on the compartmental model, an input function, and at least one PET image. For details on the generation of the parametric image, please refer to other descriptions in this specification (e.g., Figure 6 (and related descriptions). In some embodiments, the processing device 120 can generate a parametric image based on an input function and at least one PET image according to a nonlinear parameter estimation algorithm. Further description of the nonlinear parameter estimation algorithm can be found elsewhere in this specification, such as step 620 and its related description.

[0098] According to some embodiments of this specification, at least one PET image can be acquired by performing multi-point scanning or dual-injection scanning on the object. An input function can then be generated based on an image-based input function and a population-based input function. Further, a parametric image (e.g., a Ki image) can be generated based on the input function and at least one PET image. Compared to conventional methods (e.g., parametric imaging using the Patlak model), the methods and systems of this specification can generate parametric images in a relatively short imaging time (e.g., less than 10 minutes), which can improve imaging efficiency and promote the clinical application of parametric imaging.

[0099] It should be noted that the above description of process 500 is for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this specification. For example, process 500 may include additional steps for transmitting the determined parameter image to a terminal device (e.g., terminal 140) for display. As another example, process 500 may also include additional steps for storing information and / or data (e.g., at least one PET image, input function, parameter image) in a storage device (e.g., storage device 130) disclosed elsewhere in this specification.

[0100] In some embodiments, at least one PET image of an object may include at least one gated PET image of the object. As an example only, the processing device 120 may gate PET data of an object acquired during an examination. For example, the processing device 120 may gate the PET data into multiple sets. Different sets may correspond to different time periods or phases of motion (e.g., respiratory motion, cardiac motion). For example, different sets may correspond to different respiratory phases of the object. The processing device 120 may reconstruct multiple gated PET images using multiple sets of gated PET data. For illustrative purposes, a first set of gated PET data may correspond to the end of inhalation, and a second set of gated PET data may correspond to the end of exhalation. The processing device 120 may reconstruct a first gated PET image using the first set of gated PET data and a second gated PET image using the second set of gated PET data.

[0101] Figure 6 This is an exemplary flowchart illustrating the generation of parameter images according to some embodiments of this specification. In some embodiments, process 600 can be... Figure 1 This is implemented in the imaging system 100 shown. For example, process 600 can be stored as instructions in storage device 130 and / or storage (e.g., storage device 220, memory 390) and processed by processing device 120 (e.g., such as...). Figure 2 The processor 210 of the computing device 200 shown, such as Figure 3 The CPU 340 of the mobile device 300 shown is as follows: Figure 4 One or more modules shown may be invoked and / or executed. The steps of the procedure shown below are for illustrative purposes only. In some embodiments, procedure 600 may be accomplished using one or more additional operations not described and / or by omitting one or more operations discussed. Figure 6 The sequence of operations of process 600 shown and the following description are not intended to be limiting. In some embodiments, one or more steps of process 600 may be performed to achieve the same result as... Figure 5 At least a portion of step 530.

[0102] In step 610, the processing device 120 (e.g., generation module 430) can generate a compartment model for simulating tracer dynamics within a target body.

[0103] In some embodiments, the compartment model may be a two-compartment model. A two-compartment model may include a first compartment model and a second compartment model, the first compartment model representing the transport of the tracer between blood / plasma and tissue, and the second compartment model representing the phosphorylation of the tracer (e.g., FDG) in cells. For example, the first compartment model may be used to simulate forward transport of the tracer from plasma to tissue and backward transport from plasma to tissue. The second compartment model may be used to simulate the phosphorylation and dephosphorylation of the tracer in the target tissue. As used herein, phosphorylation refers to the chemical addition of a phosphate group (PO3-) to an organic molecule, and dephosphorylation refers to the removal of the phosphate group. For example, phosphorylation of FDG by hexokinase can produce phosphorylated FDG (e.g., FDG-6 phosphate), and the phosphorylated FDG can be metabolically captured and isolated in the cells of the tissue.

[0104] As an example only, the dual-compartment model can be represented by the following formulas (1) and (2):

[0105]

[0106]

[0107] in, This indicates the rate of change in the concentration of the tracer in the first compartment (which may be tissue for some tracers); C1 represents the rate of change of tracer concentration in the second compartment (which may be the tracer cell for some tracers, or the phosphorylation process when FDG is used as a tracer); C2 represents the tracer concentration in the first compartment; C3 represents the tracer concentration in the second compartment; C4 represents the tracer concentration in the second compartment. p The input function represents the concentration of the tracer in the plasma; K1 represents the forward transport rate of the tracer from the plasma to the first compartment; k2 represents the reverse transport rate of the tracer from the first compartment to the plasma; k3 represents the phosphorylation rate of the tracer (for FDG); k4 represents the dephosphorylation rate of the tracer (for FDG); and t represents the time elapsed since the tracer was injected.

[0108] In some embodiments, a dual-compartment model can be constructed for each physical point of the object to simulate physical point tracer dynamics.

[0109] In step 620, the processing device 120 (e.g., generation module 430) can generate a parametric image based on the compartment model, the input function, and at least one PET image.

[0110] In some embodiments, the processing device 120 can generate a relationship function between the compartment model, the input function, and at least one PET image. As an example only, assuming that most phosphorylated FDG is typically captured metabolically and isolated in cells without undergoing dephosphorylation (i.e., k4 = 0), the relationship function between the compartment model, the input function, and at least one PET image can be expressed by the following formula (3):

[0111]

[0112] Where t represents a time point, and X(t) represents the PET image corresponding to time point t; v b Indicates the concentration of plasma in the tissue; This represents the convolution operation.

[0113] In some embodiments, the relational function can be simplified to the following formula (4):

[0114]

[0115] Where K′1, k′2, K i and C i (t) can be expressed by formulas (5)-(8) respectively, as follows:

[0116]

[0117] k′2=k2+k3, (6)

[0118]

[0119] C i (t)=∫C p dt, (8)

[0120] Furthermore, the processing device 120 can generate a parameter image based on the relational function. In some embodiments, the processing device 120 can generate a parameter image based on the relational function according to a nonlinear parameter estimation algorithm. A nonlinear parameter estimation algorithm refers to a nonlinear algorithm used to determine image parameters. A nonlinear algorithm is an algorithm that includes one or more nonlinear operations, such as logarithmic operations, square root operations, exponential operations, integral operations, etc., or any combination thereof. For example, a nonlinear algorithm may include an iterative algorithm. Exemplarily, an iterative algorithm may include the maximum likelihood estimation (MLE) algorithm, the least squares algorithm, the ordered subset expectation maximization (OSEM) algorithm, the maximum a posteriori probability (MAP) algorithm, the weighted least squares (WLS) algorithm, etc., or any combination thereof.

[0121] For example, formula (5) can be determined for each physical point of an object. As an example only, for a physical point, X(t) represents the element value (e.g., pixel or voxel value) corresponding to that physical point in each PET image, and K... i K represents the physical point i The processing device 120 can determine the dynamic parameter value of the physical point according to the formula (4) corresponding to the physical point. For illustrative purposes only, K is used as an example. i As an example of dynamic parameters for a physical point to be determined, assuming that the element values ​​of a physical point in different PET images roughly follow a Gaussian distribution, the K of that physical point can be estimated using a least squares algorithm. i As another example, assuming that the element values ​​in the PET image roughly follow a Poisson distribution, the maximum likelihood estimation algorithm can be used to estimate the K of the physical point according to the following formulas (9)-(12). i :

[0122]

[0123]

[0124]

[0125]

[0126] in, The element value of the physical point in the estimated PET image corresponding to time point t is represented by p; the iteration number is represented by p. Represents convolution operation; t d Indicates the time required for the tracer to reach the physical point; v b Indicates the concentration of plasma in the tissue; K i K represents the physical point i Value; X(t) represents the PET image at time point t; C pK1 represents the forward transport rate of the tracer from the plasma to the first compartment, and k2 represents the reverse transport rate of the tracer from the first compartment to the plasma. It can be determined by the following formula (13):

[0127]

[0128] In formulas (9)-(12), X(t) can have a known measured value, and the parameter K can be updated iteratively. i v b The values ​​of K′1 and k′2. In some embodiments, the values ​​in formulas (9)-(12) It can measure the difference between the measured element values ​​and the predicted element values ​​of a physical point. It can be based on... Update parameter K i v b K′1 and k′2 are used to minimize the difference between the measured element values ​​and the predicted element values ​​of the physical point.

[0129] In some embodiments, an alternative update method can be used to estimate the parameter K. i v b The values ​​of K′1 and k′2. For example, multiple iterations can be performed to update the parameter K. i v b K′1 and k′2. In each iteration of multiple iterations, formulas (9)-(12) can be executed sequentially to update the parameter K. i v b Let K′1 and k2′ be the parameters. Multiple iterations can be performed to update the parameters until the termination condition is met.

[0130] If the current iteration meets the termination condition, the processing device 120 can process the K obtained from the current iteration. i The final K designated as the physical point i For example, if the current iteration yields K... i If the difference between the value and the preset value is less than the threshold, the termination condition can be considered met. For example, if K is obtained in two or more consecutive iterations... i If the change is less than a threshold (e.g., a constant), the termination condition can be considered met. For example, the termination condition can be considered met when a specified number (or number of) iterations are performed.

[0131] In some embodiments, the processing device 120 may determine one or more kinetic parameters for each physical point of the object and generate at least one parametric image corresponding to the kinetic parameters. For example, the parametric image may include a Ki image, which may reflect the transport rate of the tracer from plasma to the object's tissue. The Ki image may be used to identify and / or assess tumors in the object.

[0132] It should be noted that the above description of process 600 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can make various changes and modifications based on the description herein. However, these changes and modifications do not depart from the scope of this specification.

[0133] Figure 7A This is a schematic diagram illustrating exemplary multi-point scanning according to some embodiments of this specification. For example... Figure 7A As shown, the horizontal axis represents time t during the inspection period 700A, and the vertical axis represents the tracer concentration in the object during the inspection period 700A. The inspection period 700A starts from the initial time point t. a0 Continue until the end time t a3 At the initial time point t of 700A during the inspection. a0 The tracer is injected into the object. As described in step 510, the object can be scanned at multiple points to obtain at least one PET image. The at least one PET image may include a first PET image and a second PET image of the object. This can be achieved by... a0 to t a1 The first PET image is obtained by performing a first PET scan on the object during the first scan period. This can be achieved by... a2 to t a3 A second PET scan is performed on the object during the second scan period to obtain a second PET image. During the first scan period, from t... a0 to t a1 Duration and the second scan period from t a2 to t a3 The duration of each scan can be 5 minutes. For example, if the 700A scan lasts for 60 minutes, and the tracer is injected into the subject at minute 0, then the first scan period can last from minute 0 to minute 5, and the second scan period can last from minute 55 to minute 60. The total time for the first and second scan periods can be 10 minutes.

[0134] In some embodiments, processing device 120 may determine an input function I1 that reflects the concentration change of tracer in the subject during examination period 700A. For example, for each of the multiple scan periods in a multi-point scan, processing device 120 may determine a candidate input function (also referred to as an image-based input function) based on the PET image corresponding to the scan period. The candidate input function may reflect the concentration change of tracer in the subject during the scan period. Processing device 120 may acquire a reference input function related to the subject. The reference input function may reflect the predicted concentration change of tracer in the subject during time periods other than the scan period in examination period 700A. For example, the reference input function may be a population-based input function related to the subject. Further, processing device 120 may generate an input function by transforming the reference input function based on multiple candidate input functions. For example, processing device 120 may modify (e.g., scale) the reference input function so that one end of the modified reference input function coincides (e.g., overlaps) with the corresponding end of the candidate input function. If one end of the modified reference input function and one end of the candidate input function correspond to the same (or substantially the same) time point, they can be considered to be corresponding. The processing device 120 can generate the input function I1 by combining the modified reference input function and multiple candidate input functions.

[0135] For example only, for reference. Figure 7A The processing device 120 can determine a candidate input function F1 based on the first PET image, and the input function F1 can reflect the changes from t during the first scan. a0 to t a1 The concentration change of the tracer in the subject's body. Processing device 120 can determine a candidate input function F2 based on the second PET image, which can reflect the change from t during the second scan. a2 to t a3 The concentration change of the tracer within the object. Processing device 120 can acquire a reference input function R1, which reflects the change from t... a1 to t a2 The predicted concentration change of the tracer in the subject's body during the period. Processing device 120 can scale the reference input function R1 so that at time point t... a1 The scaled value of the reference input function is equal to the value at time t. a1 The value of the candidate input function F1, and at time point t a2 The scaled value of the reference input function is equal to the value at time t. a2 The value of the candidate input function F2. Processing device 120 can combine values ​​corresponding to t. a0 to t a1 The candidate input function F1 during the first scan period corresponds to the input function from ta1 to t a2 The scaled reference function F1 during the time period and corresponding to the time from t a2 to t a3 The candidate input function F2 during the second scan period is used to determine the input from t. a0 to t a3 The input function I1 corresponding to 700A during the inspection period.

[0136] For illustrative purposes, the input function I1 can be expressed by formula (14):

[0137]

[0138] Where t represents a specific point in time within the 700A inspection period; C p (t) represents the value of the input function I1 at time t; C image (t) represents the value of candidate input function F1 or candidate input function F2 at time point t; C p0 (t) represents the reference input function R1; and γ and μ represent scaling constants, satisfying μC p0 (t a1 ) = C image (t a1 )as well as

[0139] In some embodiments, before determining the input function using the first PET image and the second PET image, the processing device 120 may perform an image registration operation on the first PET image and the second PET image. For example, the processing device 120 may register the first PET image and the second PET image based on image features of the first PET image and the second PET image according to one or more image registration algorithms. Image features may include grayscale features, gradient features, edge features, texture features, etc., or any combination thereof. Exemplary image registration algorithms may include intensity-based algorithms, feature-based algorithms, transformation model algorithms (e.g., linear transformation models, non-rigid transformation models), spatial domain algorithms, frequency domain algorithms, single-modal algorithms, multi-modal algorithms, automatic algorithms, and interactive algorithms, etc., or any combination thereof. After performing an image registration operation on the first and second images, the same position in the registered PET image may correspond to the same physical (or spatial) point of the object.

[0140] It should be noted that, Figure 7AThe examples shown are provided for illustrative purposes only and are not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description herein. By way of example only, at least one PET image may further include a third PET image, and multi-point scanning may further include a third PET scan. The first scan period may last from minute 0 to minute 3 of the inspection period 700A, and the second scan period may last from minute 30 to minute 33 of the inspection period 700A. A third PET image can be generated by performing a third PET scan on the object during the third scan period, which may last from minute 57 to minute 60 of the inspection period 700A.

[0141] Figure 7B This is a schematic diagram of an exemplary dual-injection scan according to some embodiments of this specification. Figure 7B As shown, the horizontal axis represents time t during the 700B inspection period, and the vertical axis represents the concentration of the tracer in the subject during the 700B inspection period. The 700B inspection period starts from the initial time point t. b0 Continue until the end time t b3 At the initial time point t b0 The first portion of the tracer (e.g., 50% tracer) is injected into the subject (i.e., at t...). b0 The first injection was administered at time point t. b0 The next time point t b2 The second portion of the tracer (e.g., 50% tracer) is injected into the subject (i.e., at t...). b2 (Perform a second injection). As described in step 510, the object can be double-injected scanned to obtain at least one PET image. The at least one PET image may comprise a single PET image of the object. This can be achieved by scanning the object from t... b1 to t b3 PET scans are performed on the object during the scanning period to acquire PET images. b1 The time point is t b0 After time point and within t b2 Before that point in time. b3 The time point is t b2 After a specific point in time. For example, from t b1 to t b3 The duration of the scan period can be 10 minutes. As an example only, if the examination period 700B lasts for 60 minutes, with 50% tracer injected at minute 0 and 50% tracer injected at minute 55, then the scan period can last from minute 50 to minute 60 of the examination period 700B.

[0142] In some embodiments, the processing device 120 may determine an input function I2 that reflects changes in tracer concentration within the subject during the inspection period 700B. For example, refer to Figure 7B Candidate input function F3 (or first candidate input function) can be determined based on PET images. Candidate input function F3 can reflect the changes in t b1 to t b3 The change in tracer concentration within the subject during the scanning period. Then, the processing device 120 can determine a candidate input function F4 (or a second candidate input function) based on the candidate input function F3, the first portion of the tracer, and the second portion of the tracer. The candidate input function F4 can reflect the change in tracer concentration at time point t. b0 The change in tracer concentration in the subject's body during the subsequent first period.

[0143] Assuming the ratio of the shape of the input function from the first injection to the shape of the input function from the second injection is related to the ratio of the first and second portions of the tracer, the processing device 120 can determine a candidate input function F4 based on the candidate input function F3, the first portion, and the second portion of the tracer. For example, if the ratio of the first and second portions of the tracer is 1, the shape of the input function from the second injection can be the same as the shape of the input function from the first injection. Specifically, "input function from injection" can refer to the input function corresponding to an early period after (e.g., immediately following) the tracer injection into the target body. For example, the input function from the first injection can correspond to the period from t... b0 to t b4 During the period, such as Figure 7B As shown. The input function from the second injection can correspond to t. b2 to t b3 During the period, such as Figure 7B As shown.

[0144] Processing device 120 can acquire a reference input function R2 related to the object. The reference input function R2 can reflect the input from t... b4 to t b1 The processing device 120 can generate the input function I2 by transforming the reference input function R2 based on multiple candidate input functions. For example, the processing device 120 can modify (e.g., scale) the reference input function R2 so that one end of the modified reference input function coincides with the corresponding end of the candidate input function (e.g., overlaps). The processing device 120 can generate the input function I2 by combining the modified reference input function and multiple candidate input functions.

[0145] For example only, for reference. Figure 7BThe processing device 120 can scale the reference input function R2 so that at time point t b4 The value of the scaled reference input function at point t is equal to the value at time t. b4 The value of the candidate input function F4 at time point t b1 The value of the scaled reference input function at point t is equal to the value at time t. b1 The value of the candidate input function F3 at point t. Processing device 120 can combine the corresponding values ​​from t. b0 to t b4 During the period, the candidate input function F4 corresponds to the function from t b4 to t b1 The scaled reference input function during the period, and the corresponding input function from t b1 to t b3 The candidate input function F3 during the period is used to determine the corresponding inspection period 700B from t. b1 to t b3 The input function I2 during this period.

[0146] It should be noted that, Figure 7B The examples shown are provided for illustration only and are not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description in this application. For example, the tracer can be injected into the subject through three or more injections, and the subject can be subjected to two or more PET scans.

[0147] In some embodiments, the processing device 120 may determine a candidate input function F5, which may reflect the input function F5 after the scan period from t b3 to t b5 The change in tracer concentration in the subject's body during the third period. t b5 The time point is t b3 After the time point. The processing device 120 can, based on the first part, the second part, and the corresponding portion from t... b4 to t b1 The reference input function R2 during the second period is used to determine the candidate input function F5. In some embodiments, the processing device 120 can determine the candidate input function F5 by scaling the reference input function R2 based on the ratio of the first and second portions of the tracer. For example, the processing device 120 can scale the reference input function R2 (or corresponding to t) according to the ratio of the first and second portions of the tracer. b1 A portion of the reference input function R2 for the next period (with the same duration as the third period) is then transformed and scaled to generate the candidate input function F5.

[0148] Figure 8This is a schematic diagram of exemplary Ki images of an object according to some embodiments of this specification. As described in processes 500 and 600 of this specification, Ki images can be generated based on at least one first PET image of a patient, including Ki image 810 and Ki image 820. Ki images 830 and Ki image 840 can be generated using a Patlak model based on at least one second PET image with a scan time of 40 minutes. At least one first PET image is acquired by performing a 10-minute double-injection scan, and at least one second PET image is acquired by performing a 40-minute continuous PET scan. Ki image 810 and Ki image 830 correspond to the sagittal plane of the patient. Ki image 820 and Ki image 840 correspond to the coronal plane of the patient. It can be seen that Ki image 810 has a similar resolution to Ki image 830, and Ki image 820 has a similar resolution to Ki image 840. Therefore, the methods and systems disclosed in this specification can be used to generate parametric images with desired quality and accuracy in a relatively short imaging time (e.g., less than 10 minutes).

[0149] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this application. Various changes and modifications can be made by those skilled in the art based on the description herein. However, these changes and modifications do not depart from the scope of this application.

[0150] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0151] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0152] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software can be referred to as a “unit,” “module,” or “system.” Furthermore, aspects of this application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0153] Computer-readable signal media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. Such propagated signals can take many forms, including electromagnetic, optical, and any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable signal medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, and any combination of the above.

[0154] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, and Python; general programming languages ​​such as C; Visual Basic, Fortran2103, Perl, COBOL2102, PHP, and ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or can establish a connection with an external computer (e.g., through the network of an internet service provider) or in a cloud computing environment, or provided as a service, such as a software service (SaaS).

[0155] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. Rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0156] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, the method of the present application should not be construed as reflecting an intention that the claimed object to be scanned requires more features than expressly recited in each claim. Rather, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. A method for image reconstruction, implemented on a computing device having at least one processor and at least one storage device, the method comprising: At least one positron emission tomography (PET) image of the object is obtained by performing a multi-point scan on the object, wherein performing the multi-point scan includes: The tracer is injected into the subject at the initial time point during the inspection; Multiple positron emission tomography (PET) scans are performed on the object during multiple scan periods following the initial time point, with each of the multiple PET scans being performed during one of the multiple scan periods; Based on the at least one positron emission tomography (PET) image, an input function is determined, the input function being used to reflect the concentration change of the tracer in the subject during the examination, including: For each of the plurality of scan periods, a candidate input function is determined based on the positron emission tomography image corresponding to the scan period; Obtain a reference input function related to the object, the reference input function reflecting the predicted concentration change of the tracer in the object during time periods other than the plurality of scanning periods, the reference input function being determined based on the sample input function of a plurality of sample objects, and the similarity between the feature information of the sample objects and the feature information of the object being higher than a threshold; The process of transforming the reference input function based on the candidate input function to generate the input function includes: By modifying the reference input function so that one end of the reference input function is consistent with the corresponding end of the candidate input function, and combining the candidate input function with the modified reference input function, the input function is generated. The at least one positron emission tomography (PET) image includes one PET image of the object, and obtaining at least one PET image of the object further includes: The positron emission tomography (PET) image is obtained by performing a dual-injection scan on the object, wherein performing the dual-injection scan includes: A first portion of the tracer is injected into the subject at a first time point during the examination, and a second portion of the tracer is injected into the subject at a second time point after the first time point during the examination. Positron emission tomography (PET) is performed within a scan period that begins after the first time point and before the second time point, and ends after the second time point. Generating the input function further includes: Obtain a reference input function related to the object; A first candidate input function is determined based on the positron emission tomography (PET) image, and the first candidate input function is used to reflect the concentration change of the tracer in the body of the object during the scan; Based on the first candidate input function and the ratio of the first portion and the second portion of the tracer, a second candidate input function is determined. This second candidate input function reflects the concentration change of the tracer in the subject body over a period between the first time point and the second time point. The input function is generated by transforming the reference input function based on the first candidate input function and the second candidate input function; According to a nonlinear parameter estimation algorithm, a parameter image is generated based on the at least one positron emission tomography (PET) image and the input function, wherein the parameter image is used to reflect the kinetic parameters of the tracer in the object.

2. The method of claim 1, wherein, There is a time interval between each pair of adjacent positron emission tomography (PET) scans in the multiple PET scans.

3. The method of claim 2, wherein, The candidate input function is used to reflect the concentration change of the tracer in the body of the object during the scan.

4. The method of claim 1, wherein, The step of generating a parameter image based on the at least one positron emission tomography (PET) image and the input function according to a nonlinear parameter estimation algorithm includes: Generate a compartmentalized model for simulating tracer dynamics within the object; and The parameter image is generated based on the nonlinear parameter estimation algorithm, the compartment model, the input function, and the at least one positron emission tomography (PET) image.

5. The method of claim 4, wherein, The compartment model is used to simulate one of the following: The tracer is transported forward from the subject's plasma to the subject's tissues. The tracer is transported backward from the plasma to the tissue. The phosphorylation process in the target tissue, or The dephosphorylation process in the target tissue.

6. The method of claim 4, wherein, The step of generating the parameter image based on the nonlinear parameter estimation algorithm, the compartment model, the input function, and the at least one positron emission tomography (PET) image includes: Generate the relationship function between the compartment model, the input function, and the at least one positron emission tomography (PET) image; and The parameter image is generated based on the relation function according to the nonlinear parameter estimation algorithm.

7. The method of claim 1, wherein, The nonlinear parameter estimation algorithm includes the maximum likelihood estimation algorithm.

8. The method of claim 1, wherein, The parameter image includes the Ki image.

9. An image reconstruction system, comprising: At least one storage device for storing executable instructions; At least one processor communicating with the at least one storage device, wherein when the executable instructions are executed, the at least one processor causes the system to perform operations, including: At least one positron emission tomography (PET) image of the object is obtained by performing a multi-point scan on the object, wherein performing the multi-point scan includes: The tracer is injected into the subject at the initial time point during the inspection; Multiple positron emission tomography (PET) scans are performed on the object during multiple scan periods following the initial time point, with each of the multiple PET scans being performed during one of the multiple scan periods; Based on the at least one positron emission tomography (PET) image, an input function is determined, the input function being used to reflect the concentration change of the tracer in the subject during the examination, including: For each of the plurality of scan periods, a candidate input function is determined based on the positron emission tomography image corresponding to the scan period; Obtain a reference input function related to the object, the reference input function reflecting the predicted concentration change of the tracer in the object during time periods other than the plurality of scanning periods, the reference input function being determined based on the sample input function of a plurality of sample objects, and the similarity between the feature information of the sample objects and the feature information of the object being higher than a threshold; The process of transforming the reference input function based on the candidate input function to generate the input function includes: By modifying the reference input function so that one end of the reference input function is consistent with the corresponding end of the candidate input function, and combining the candidate input function with the modified reference input function, the input function is generated. The at least one positron emission tomography (PET) image includes one PET image of the object, and obtaining at least one PET image of the object further includes: The positron emission tomography (PET) image is obtained by performing a dual-injection scan on the object, wherein performing the dual-injection scan includes: A first portion of the tracer is injected into the subject at a first time point during the examination, and a second portion of the tracer is injected into the subject at a second time point after the first time point during the examination. Positron emission tomography (PET) is performed within a scan period that begins after the first time point and before the second time point, and ends after the second time point. Generating the input function further includes: Obtain a reference input function related to the object; A first candidate input function is determined based on the positron emission tomography (PET) image, and the first candidate input function is used to reflect the concentration change of the tracer in the body of the object during the scan; Based on the first candidate input function and the ratio of the first portion and the second portion of the tracer, a second candidate input function is determined. This second candidate input function reflects the concentration change of the tracer in the subject body over a period between the first time point and the second time point. The input function is generated by transforming the reference input function based on the first candidate input function and the second candidate input function; According to a nonlinear parameter estimation algorithm, a parameter image is generated based on the at least one positron emission tomography (PET) image and the input function, wherein the parameter image is used to reflect the kinetic parameters of the tracer in the object.

Images