Image point spread function prediction methods and computer equipment

Abstract

This specification provides a method and computer device for estimating the point spread function of an image, relating to the field of medical device technology. The method includes: selecting at least one feature point in the reconstructed image space; establishing an orthographic projection model based on the geometric parameters of the CT system and the current scanning parameters to simulate the diffusion distribution of the feature point as an ideal point source in the projection data domain, thereby obtaining the orthographic projection point spread function; performing a virtual backprojection operation on the feature point based on the current reconstruction method and analyzing its response distribution to obtain the backprojection point spread function; and synthesizing the orthographic projection point spread function and the backprojection point spread function to obtain the overall point spread function for iterative reconstruction or post-processing of CT images. Thus, the point spread function corresponding to the CT system can be adaptively and quickly estimated based on the scanning and reconstruction parameters, facilitating improved image quality in iterative reconstruction or post-processing of CT images.

Description

Technical Field

[0001] The embodiments described in this specification relate to the field of medical device technology, specifically to an image point spread function prediction method and a computer device. Background Technology

[0002] X-ray computed tomography (CT) imaging technology has become one of the core tools of modern medical diagnosis. Its image quality, especially spatial resolution, directly affects the detection and characterization of lesions. Ideally, a CT system should be able to clearly image an infinitesimally small point object (i.e., an ideal point source) as a single point. However, in actual physical imaging processes, limited by the physical characteristics of the system hardware and the approximation of reconstruction algorithms, this point source will always appear as a blurred distribution in the final image. This blurring effect is mathematically described by the point spread function (PSF). The PSF is essentially a direct manifestation of the spatial transfer characteristics of the imaging system in image space; its width and shape determine the CT system's ability to restore object details (i.e., high-frequency information). Therefore, accurately knowing the PSF of a CT system under specific operating conditions is crucial for achieving high-fidelity image reconstruction and post-processing, thereby improving image resolution.

[0003] In related technologies, acquiring the post-convergence strength (PSF) of a CT system typically involves using a tiny sphere of known precise size and location, made of high-density materials such as tungsten or ceramic, as a phantom. This phantom is placed within the scanning field of view, and data is acquired according to a specific scanning protocol. Subsequently, the acquired projection data is reconstructed. In the resulting reconstructed image, the sphere is not an ideal bright spot, but rather a diffuse spot formed due to system blurring. By measuring and fitting the two-dimensional or three-dimensional intensity distribution of this diffuse spot in the image, the PSF of the system under the specific scanning and reconstruction conditions can be deduced. However, this method is only effective for the specific parameter combination used in the measurement. Once any parameter changes in clinical application, the previously measured PSF becomes invalid and cannot accurately reflect the system performance under the new conditions. In other words, the PSF acquisition methods in related technologies rely on repetitive, offline physical measurements, lacking adaptability and being inefficient.

[0004] Therefore, there is an urgent need to provide a method for predicting the point spread function of an image, which can be free from dependence on physical phantoms and can adaptively and quickly predict the point spread function of the CT system based on any given combination of scanning and reconstruction parameters. Summary of the Invention

[0005] In view of this, this specification provides a method for predicting the point spread function of an image, which adaptively and quickly predicts the point spread function of a CT system based on scanning parameters and reconstruction parameters, thereby improving image quality in iterative reconstruction or post-processing of CT images.

[0006] This specification provides a method for estimating the point spread function of an image. The method includes: selecting at least one feature point in the reconstructed image space; establishing an orthographic projection model based on the geometric parameters of the CT system and the current scanning parameters to simulate the diffusion distribution of the feature point as an ideal point source in the projection data domain, thereby obtaining the orthographic projection point spread function; performing a virtual backprojection operation on the feature point based on the current reconstruction method, analyzing the response distribution during the backprojection process, thereby obtaining the backprojection point spread function; and synthesizing the orthographic projection point spread function and the backprojection point spread function to obtain the overall point spread function; wherein the overall point spread function can characterize the overall link ambiguity effect from CT system projection acquisition to image reconstruction.

[0007] In some embodiments, selecting at least one feature point in the reconstructed image space includes: selecting the center point of the image matrix and / or at least one discrete point or key point located within the reconstructed field of view as the feature point, based on the image matrix size; wherein the discrete point or key point selected within the reconstructed field of view is selected based on a preset rule.

[0008] In some embodiments, the geometric parameters of the CT system include at least one of the following: X-ray source focal spot size, distance from focal spot to rotation center, detector unit size and arrangement, and distance from detector to rotation center; the current scanning parameters include at least one of the following: scanning mode, helical scanning pitch, and X-ray cone angle.

[0009] In some implementations, simulating the diffusion distribution of the feature points as ideal point sources in the projection data domain using the orthographic projection model to obtain the orthographic projection point diffusion function includes: simulating the projection path of X-rays emitted from the focal point, passing through the feature points, and reaching the detector unit based on the geometric parameters; calculating the diffusion distribution of the feature points as ideal point sources in the projection data domain along the channel direction, row direction, and viewing angle direction by combining the current scanning parameters and the projection path; and calculating the orthographic projection point diffusion function based on the diffusion distribution and the local response characteristics of the system's orthographic projection operator.

[0010] In some implementations, the virtual back-projection operation on the feature points based on the current reconstruction method includes: performing a virtual back-projection operation on the feature points based on the back-projection strategy, interpolation method, and reconstruction parameters used by the current image reconstruction algorithm.

[0011] In some implementations, analyzing the response distribution during the backprojection process includes: after constructing a virtual backprojection path based on the current image reconstruction algorithm, the interpolation method, and the reconstruction parameters, calculating the response distribution of the feature points in the image space after backprojection on the virtual backprojection path according to the weight allocation rules of weighted backprojection and the selected interpolation method; quantizing the half-width at half-maximum (WHM) of the response distribution to obtain the backprojection point spread function.

[0012] In some embodiments, the synthesis of the orthographic point spread function and the back-projection point spread function includes: performing a convolution operation on the orthographic point spread function and the back-projection point spread function in the image space, or converting to the frequency domain, performing a multiplication operation, and then converting back to the image space to obtain a matrix representation of the overall point spread function.

[0013] In some implementations, the point spread function prediction method further includes: integrating the overall point spread function into the system matrix of the iterative reconstruction algorithm as part of the system response model; or, introducing the overall point spread function as prior information of the regularization term into the objective function of the iterative reconstruction to guide the updating of CT images.

[0014] In some embodiments, the point spread function estimation method further includes: for CT images not reconstructed by backprojection based on the global point spread function, using the global point spread function as the blur kernel for deconvolution operation to perform deconvolution operation on the reconstructed CT images in order to recover suppressed high-frequency details in the CT images.

[0015] This specification provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the point spread function prediction method described in any of the above embodiments.

[0016] In the various implementations provided in this specification, firstly, at least one feature point is selected in the reconstructed image space. Next, based on the geometric parameters of the CT system and the current scanning parameters, an orthographic projection model is established to simulate the diffusion distribution of the feature point as an ideal point source in the projection data domain, yielding an orthographic projection point diffusion function. This orthographic projection point diffusion function describes the blurring effect exhibited by the orthographic projection model in response to the feature point. Then, based on the current reconstruction method, a virtual backprojection operation is performed on the feature point, and the response distribution during the backprojection process is analyzed to obtain a backprojection point diffusion function. This backprojection point diffusion function describes the blurring or smoothing effect introduced by the backprojection operation. Finally, the orthographic projection point diffusion function and the backprojection point diffusion function are synthesized to obtain a global point diffusion function that characterizes the overall link blurring effect from CT system projection acquisition to image reconstruction. This global point diffusion function can then be used for iterative reconstruction or post-processing of CT images. In this way, the point diffusion function corresponding to the CT system can be adaptively and quickly predicted based on scanning parameters, geometric parameters, and reconstruction parameters, facilitating improved image quality in iterative reconstruction or post-processing of CT images. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the image point spread function prediction method provided in the embodiments of this specification; Figure 2 A schematic diagram of an image point spread function prediction device provided in the embodiments of this specification; Figure 3 A schematic diagram of a computer device provided for an embodiment of this specification. Detailed Implementation

[0018] To enable those skilled in the art to better understand the solutions described in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0019] In related technologies, PSF acquisition methods rely on repetitive, offline physical measurements and lack adaptability. Therefore, it is necessary to provide a method that can get rid of dependence on physical phantoms, and can adaptively and quickly predict the point spread function of the CT system based on any given combination of scanning and reconstruction parameters. By predicting the PSF in real time under the current scanning and reconstruction configuration, a data foundation can be provided to improve CT image quality, especially image resolution.

[0020] This specification provides a method for estimating the point spread function of an image. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart illustrating an image point spread function prediction method provided in this specification. This embodiment provides the method operation steps as shown in the flowchart, but based on conventional or non-inventive methods, more or fewer operation steps may be included. The order of steps listed in the embodiment is merely one possible execution order among many, and does not represent the only possible execution order. In actual system or server product execution, the method can be executed sequentially as shown in the embodiment or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as follows... Figure 1 As shown, the image point spread function prediction method may include the following steps.

[0021] Step S110: Select at least one feature point in the reconstructed image space.

[0022] In some cases, to reduce reliance on physical phantoms and improve the adaptability and real-time performance of spatial transfer characteristic detection during CT imaging, the imaging blur characteristics can be dynamically predicted based on the real-time scanning and reconstruction parameters of the CT system. Specifically, by virtually simulating the imaging process of the CT system, the blurring effect in the CT imaging chain is decomposed into two stages: forward projection and back projection. The contribution of each stage to the point source response is modeled, thereby predicting the blur performance of the CT system under specific parameters, i.e., synthesizing a complete system-level overall PSF.

[0023] Specifically, at least one feature point can be selected in the reconstructed image space based on the preset reconstruction field of view and image matrix size.

[0024] The preset field of view (FOV) can refer to parameters that describe the physical spatial extent of the image to be reconstructed. The FOV determines the size of the anatomical region of the human body covered by the final CT image. For example, a circular region with a diameter of 300 mm or a square region of 400 mm × 400 mm.

[0025] Image matrix size refers to a parameter that describes the degree of discretization of the reconstructed field of view. It can define the number of pixels or voxels in the image along the length, width, and height directions, as well as the number of pixels or voxels in the image along the row, column, and layer directions. As an example, image matrix size can refer to a preset image resolution parameter. For instance, the image matrix size could be 512 (rows) × 512 (columns) × 300 (layers). Based on the reconstructed field of view and the image matrix size, the spatial sampling rate of the image can be determined together.

[0026] The reconstructed image space can refer to a three-dimensional discrete coordinate space defined by the reconstructed field of view and the image matrix size. Each smallest unit is a voxel.

[0027] Feature points can refer to the spatial coordinates of an ideal point source, which are artificially defined within the reconstructed image space. These feature points can be used as probes to test the system's response, providing a clear and idealized input source for subsequent simulation calculations.

[0028] Step S120: Based on the geometric parameters of the CT system and the current scanning parameters, establish an orthographic projection model to simulate the diffusion distribution of feature points as ideal point sources in the projection data domain, and obtain the orthographic projection point diffusion function; wherein, the orthographic projection point diffusion function can describe the fuzzing effect presented by the orthographic projection model in response to feature points.

[0029] Among them, the geometric parameters of the CT system are the parameters that describe the fixed physical structure of the CT system. They do not change with routine scanning and can be obtained through a one-time system calibration.

[0030] Current scan parameters refer to the variable parameters in the scan protocol that the user has set or the CT system is about to execute, which are currently being used or are being estimated.

[0031] An orthographic projection model is a mathematical model based on the geometric parameters of a CT system and the current scanning parameters. It accurately describes the physical process of X-rays emitted from a finite-sized focal point, passing through a feature point (an ideal point source), and being received by a finite-sized detector unit. The projection data domain refers to the data space composed of the X-ray attenuation data received by each detector unit at different projection angles. Under this orthographic projection model, the signal of a feature point diffuses across multiple adjacent detector units in the projection data, not just affecting one unit along the theoretical geometric path. That is, the finite size of the focal point and the finite size of the detector units jointly cause the diffusion distribution, from which the orthographic projection point spread function (PSF) corresponding to the orthographic projection model can be calculated. The orthographic projection PSF quantitatively describes the ambiguity effect exhibited by the orthographic projection model in response to the ideal point source located at the feature point during the data acquisition phase; in other words, it defines the distribution pattern of the ideal point source signal in the projection data. Thus, it is possible to accurately quantify and separate the ambiguity components introduced by physical limitations at the imaging chain front end. Here, the imaging chain front end refers to the data acquisition hardware.

[0032] Step S130: Based on the current reconstruction method, perform virtual back projection on the feature points, analyze the response distribution during the back projection process, and obtain the back projection point diffusion function; wherein, the back projection point diffusion function can describe the blurring effect or smoothing effect introduced by the back projection operation.

[0033] The current reconstruction method can be the configuration parameters used for the current image reconstruction.

[0034] Virtual backprojection operation can refer to the process of simulating the formation of a result in image space after processing by reconstruction parameters, using feature points as reconstruction targets based on reconstruction parameters, under ideal conditions without orthographic blur.

[0035] During the backprojection process or stage, the signals of feature points are distributed across multiple voxels in the reconstructed image space along the backprojection path. This distribution, influenced by the reconstruction parameters, introduces blurring or smoothing effects, causing the point to diffuse in the output image even if the input is an ideal point. This leads to the backprojection point spread function (PSF) corresponding to the virtual backprojection operation. The backprojection PSF can quantitatively describe the blurring or smoothing effects introduced during the image reconstruction stage, specifically the virtual backprojection operation itself. Thus, it is possible to accurately quantify and separate the blurring components introduced at the back end of the imaging chain due to algorithmic approximation and discretization. Here, the back end of the imaging chain refers to the reconstruction algorithm and software.

[0036] Step S140: Combine the orthogonal projection point spread function and the back projection point spread function to obtain the overall point spread function; wherein, the overall point spread function can characterize the overall link blurring effect from CT system projection acquisition to image reconstruction.

[0037] The global point spread function, also known as the global PSF, can be used for iterative reconstruction or post-processing of CT images.

[0038] By synthesizing the orthographic projection PSF and backprojection PSF, which respectively characterize the blurring effects at the front and back ends, into a complete system-level overall PSF, it is possible to comprehensively characterize the overall link blurring effect from CT system projection acquisition to image reconstruction. This comprehensively reflects the total blurring effect caused by all stages on the ideal point source throughout the entire process, from X-ray emission, through the object, and acquisition by the detector, to the final reconstruction of the CT image by the algorithm. In this way, it is possible to integrate the front-end and back-end models to generate a high-fidelity system-level blurring effect characterization tool that strictly corresponds to the current scanning and reconstruction parameters. This provides accurate input for subsequent iterative reconstruction or post-processing of CT images, ultimately achieving the goal of improving the spatial resolution of CT images.

[0039] In the above embodiments, by performing orthographic PSF modeling and back-projection PSF modeling for selected feature points respectively, and synthesizing the obtained orthographic PSF and back-projection PSF, the overall PSF used for iterative reconstruction or post-processing of CT images is obtained, which has the following beneficial effects: (1) Real-time adaptive prediction of PSF is realized: without relying on repeated physical phantom scanning, the system point spread function under the current working conditions can be quickly and accurately calculated based on only one system calibration and real-time input scanning and reconstruction parameters, overcoming the defects of fixed parameters and poor adaptability; (2) Improved clinical efficiency and convenience: the cumbersome phantom preparation, scanning and data processing process is eliminated, which greatly reduces the time and cost of system maintenance and image quality optimization. The cost makes the acquisition and application of high-precision PSF simple and efficient; (3) A more complete physical model is established: by decomposing the blurring effect of the imaging chain into two stages, orthographic projection and back-projection and modeling them separately, the overall PSF of the CT system is finally synthesized, which can more comprehensively and accurately reflect the full-link system response characteristics from data acquisition to image reconstruction, and provide a precise basis for the in-depth optimization of image quality; (4) Provide a data foundation for improving CT image quality: the estimated high-precision, adaptive PSF can be directly used as prior knowledge to seamlessly embed the system matrix or regularization term in the iterative reconstruction algorithm, or used to perform deconvolution post-processing on conventional reconstructed images, thereby effectively restoring high-frequency details of the image and improving the spatial resolution and diagnostic value of CT images.

[0040] In some implementations, selecting at least one feature point in the reconstructed image space may include: selecting the center point of the image matrix and / or at least one discrete point or key point located within the reconstructed field of view as feature points, based on the image matrix size. This provides a physical location reference and image space coordinates for subsequent simulation calculations.

[0041] Among them, the discrete points or key points selected within the reconstructed field of view can be selected based on preset rules.

[0042] For example, the discrete points or key points selected within the reconstructed field of view can be uniformly selected within the key area.

[0043] In some implementations, the geometric parameters of the CT system may include at least one of the following: X-ray source focal spot size, distance from the focal spot to the center of rotation, detector unit size and arrangement, and distance from the detector to the center of rotation.

[0044] The focal spot size of the X-ray source is also known as the physical size of the focal spot. The distance from the focal spot to the center of rotation is the distance from the focal spot to the center of rotation of the gantry. The detector unit size and arrangement can refer to the physical size of each detector unit and the gap between adjacent units. The detector arrangement can include fan-shaped or arc-shaped arrangements, which are not limited here.

[0045] In some implementations, the current scan parameters may include at least one of the following: scan mode, helical scan pitch, and X-ray cone angle.

[0046] For example, the scanning mode may include axial scanning or helical scanning.

[0047] For example, the scanning parameters may also include the tube voltage and tube current used for scanning.

[0048] In some implementations, the diffusion distribution of feature points as ideal point sources in the projection data domain is simulated by an orthographic projection model to obtain the orthographic projection point diffusion function, which may include the following steps S210-S230.

[0049] Step S210: Based on geometric parameters, simulate the projection path of X-rays emitted from the focal point, passing through feature points, and reaching the detector unit.

[0050] Step S220: Combine the current scanning parameters and projection path to calculate the diffusion distribution of feature points, which are ideal point sources, in the projection data domain along the channel direction, row direction, and viewing direction.

[0051] Step S230: Calculate the diffusion function of the orthographic projection point based on the diffusion distribution and the local response characteristics of the system's orthographic projection operator.

[0052] In some implementations, the current reconstruction method may include specific configuration parameters such as the image reconstruction algorithm, back projection strategy, interpolation method, and reconstruction parameters used for the current image reconstruction.

[0053] In this embodiment, performing a virtual back-projection operation on feature points based on the current reconstruction method may include: performing a virtual back-projection operation on feature points based on the back-projection strategy, interpolation method, and reconstruction parameters used by the current image reconstruction algorithm.

[0054] The current image reconstruction algorithm can be a filtered backprojection algorithm. The backprojection strategy can include weighted backprojection. The interpolation method can include linear interpolation or sinc interpolation. Reconstruction parameters can include reconstruction layer thickness, reconstruction interval, and convolution kernel type.

[0055] In some implementations, analyzing the response distribution during the back projection process may include the following steps S310-S320.

[0056] Step S310: After constructing a virtual backprojection path based on the current image reconstruction algorithm, interpolation method, and reconstruction parameters, the response distribution of feature points in the image space after backprojection is calculated on the virtual backprojection path according to the weight allocation rules of weighted backprojection and the selected interpolation method.

[0057] Step S320: Quantize the full width at half maximum (FWHM) of the response distribution to obtain the back-projection point spread function.

[0058] Specifically, by analyzing the smoothing effect introduced by weight allocation and interpolation on the back projection path, the half-width at half-maximum (WHM) of the back projection process to the point source response is quantified, and the back projection PSF is obtained.

[0059] In some implementations, synthesizing the orthographic point spread function and the back-projection point spread function may include: performing a convolution operation between the orthographic point spread function and the back-projection point spread function in the image space, or converting to the frequency domain, performing a multiplication operation, and then converting back to the image space to obtain a matrix representation of the overall point spread function.

[0060] In some implementations, the point spread function prediction method may further include the following steps: The global point spread function can be integrated into the system matrix of the iterative reconstruction algorithm as part of the system response model; or, the global point spread function can be used as prior information for regularization and introduced into the objective function of iterative reconstruction to guide the updating of CT images. Alternatively, For CT images that are not back-projected and reconstructed based on the global PSF, the global PSF is used as the blur kernel for the deconvolution operation to perform deconvolution on the reconstructed CT images in order to restore the suppressed high-frequency details in the CT images.

[0061] This specification provides an image point spread function prediction device. Please refer to [link to relevant documentation]. Figure 2 The image point spread function prediction device may include a feature point selection module 410, an orthographic projection PSF modeling module 420, a back projection PSF modeling module 430, and a system overall PSF synthesis module 440.

[0062] The feature point selection module 410 is used to select at least one feature point in the reconstructed image space; The orthographic projection PSF modeling module 420 is used to establish an orthographic projection model based on the geometric parameters of the CT system and the current scanning parameters. The orthographic projection model is used to simulate the diffusion distribution of feature points as ideal point sources in the projection data domain, and obtain the orthographic projection point diffusion function. The orthographic projection point diffusion function can describe the fuzzing effect presented by the orthographic projection model in response to feature points. The back-projection PSF modeling module 430 is used to perform virtual back-projection operations on feature points based on the current reconstruction method, analyze the response distribution during the back-projection process, and obtain the back-projection point spread function; wherein, the back-projection point spread function can describe the blurring effect or smoothing effect introduced by the back-projection operation. The overall PSF synthesis module 440 is used to synthesize the orthographic projection point spread function and the back projection point spread function to obtain the overall point spread function that can be used for iterative reconstruction or post-processing of CT images; wherein, the overall point spread function can characterize the overall link blur effect from CT system projection acquisition to image reconstruction.

[0063] The specific functions and effects of the image point spread function prediction device can be explained by referring to other embodiments in this specification, and will not be repeated here. Each module in the image point spread function prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0064] This specification provides a computer device including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it can implement the point spread function prediction method described in any of the above embodiments.

[0065] This specification also provides a computer-readable storage medium storing a computer program that, when executed by a computer, implements the image point spread function prediction method described in any of the above embodiments.

[0066] This specification also provides a computer program product containing instructions that, when executed by a computer, cause the computer to implement the image point spread function prediction method in any of the above embodiments.

[0067] In some implementations, please refer to Figure 3 The computer device can be a terminal, and its internal structure diagram can be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a communication interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements an image point spread function prediction method.

[0068] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the embodiments described herein, and are not intended to limit the scope of the invention.

[0069] It is understood that in the various embodiments described in this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments described in this specification.

[0070] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and the implementation methods in this specification are not limited in this respect.

[0071] Unless otherwise stated, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0072] It is understood that the processor in the embodiments of this specification can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly implemented by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0073] It is understood that the memory in the embodiments of this specification may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0076] The above description is merely a specific embodiment of this specification, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for predicting the point spread function of an image, characterized in that, The method includes: In the reconstructed image space, at least one feature point is selected; Based on the geometric parameters of the CT system and the current scanning parameters, an orthographic projection model is established to simulate the diffusion distribution of the feature points as ideal point sources in the projection data domain, thereby obtaining the orthographic projection point diffusion function. Based on the current reconstruction method, a virtual back-projection operation is performed on the feature points, and the response distribution during the back-projection process is analyzed to obtain the back-projection point diffusion function. The positive projection point spread function and the negative projection point spread function are combined to obtain the overall point spread function; wherein, the overall point spread function can characterize the overall link blur effect from CT system projection acquisition to image reconstruction.

2. The point spread function prediction method according to claim 1, characterized in that, The step of selecting at least one feature point in the reconstructed image space includes: In the reconstructed image space, the center point of the image matrix and / or at least one discrete point or key point located within the reconstructed field of view are selected as the feature points, based on the image matrix size; wherein the discrete points or key points selected within the reconstructed field of view are selected based on preset rules.

3. The point spread function prediction method according to claim 1, characterized in that, The geometric parameters of the CT system include at least one of the following: X-ray source focal spot size, distance from focal spot to rotation center, detector unit size and arrangement, and distance from detector to rotation center; The current scanning parameters include at least one of the following: scanning mode, helical scanning pitch, and X-ray cone angle.

4. The point spread function prediction method according to claim 3, characterized in that, The step of simulating the diffusion distribution of the feature points as ideal point sources in the projection data domain using the orthographic projection model to obtain the orthographic projection point diffusion function includes: Based on the geometric parameters, the projection path of X-rays is simulated, which originates from the focal point, passes through the feature point, and reaches the detector unit. Based on the current scanning parameters and the projection path, the diffusion distribution of feature points, which are ideal point sources, in the projection data domain along the channel direction, row direction, and viewing direction is calculated. The diffusion function of the orthographic projection point is calculated based on the diffusion distribution and the local response characteristics of the system's orthographic projection operator.

5. The point spread function prediction method according to claim 1, characterized in that, The virtual back-projection operation on the feature points based on the current reconstruction method includes: performing a virtual back-projection operation on the feature points based on the back-projection strategy, interpolation method, and reconstruction parameters used by the current image reconstruction algorithm.

6. The point spread function prediction method according to claim 5, characterized in that, The analysis of the response distribution during the back projection process includes: After constructing a virtual back-projection path based on the current image reconstruction algorithm, the interpolation method, and the reconstruction parameters, the response distribution of the feature points in the image space after back-projection is calculated on the virtual back-projection path according to the weight allocation rules of weighted back-projection and the selected interpolation method. The full width at half maximum (FWHM) of the response distribution is quantified to obtain the back-projection point spread function.

7. The point spread function prediction method according to claim 1, characterized in that, The step of synthesizing the diffusion function of the orthogonal projection point and the diffusion function of the inverse projection point includes: The positive projection point spread function and the negative projection point spread function are convolved in the image space, or multiplied in the frequency domain and then converted back to the image space to obtain the matrix representation of the overall point spread function.

8. The point spread function prediction method according to claim 1, characterized in that, The method further includes: The global point spread function can be integrated into the system matrix of the iterative reconstruction algorithm as part of the system response model; or, the global point spread function can be used as prior information of the regularization term and introduced into the objective function of the iterative reconstruction to guide the updating of CT images.

9. The point spread function prediction method according to claim 1, characterized in that, The method further includes: For CT images not reconstructed by backprojection based on the global point spread function, the global point spread function is used as the blur kernel for deconvolution operation to perform deconvolution operation on the reconstructed CT image in order to restore the suppressed high-frequency details in the CT image.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the point spread function prediction method according to any one of claims 1 to 9.

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