Scattering correction method, system and readable storage medium

Abstract

Embodiments of this specification provide a scatter correction method, system, and computer-readable storage medium. The method includes: acquiring sizing scan data of a scanned object; acquiring tomographic scan data of the scanned object; determining scatter estimation data of the scanned object based on the tomographic scan data; and determining target data based on the scatter estimation data and a target matching condition, wherein the target matching condition is related to the sizing scan data.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a scatter correction method, system, and computer-readable storage medium. Background Art

[0002] Computed tomography (CT) equipment uses high-energy radiation (such as X-rays and gamma rays) to scan an object and generate scanned images. However, during the scanning process, the radiation may scatter as it passes through the object. This can cause scattering noise in the resulting image. Specifically, the generated image may exhibit darkened strips, bands, or uneven cup-shaped artifacts, reducing the soft tissue contrast of the scanned object and the accuracy of the CT values, thereby affecting image quality. Therefore, it is necessary to provide a scatter correction method to improve the effectiveness of scatter correction. Summary of the Invention

[0003] One embodiment of the present specification provides a scatter correction method, the method comprising: acquiring positioning scan data of a scanned object; acquiring tomographic scan data of the scanned object; determining scatter estimation data of the scanned object based on the tomographic scan data; and determining target data based on the scatter estimation data and a target matching condition, wherein the target matching condition is related to the positioning scan data.

[0004] In some embodiments, determining the target data based on the scatter estimation data and the target matching condition includes: determining scatter correction data based on the scatter estimation data; comparing at least a portion of the scatter correction data with positioning data within a preset range, and if the comparison result meets a threshold condition, determining the scatter correction data as the target data.

[0005] In some embodiments, determining the target data based on the scatter estimation data and the target matching condition includes: iteratively updating the scatter estimation data according to the tomographic data until the target matching condition is met, and determining the target data based on the updated scatter estimation data.

[0006] In some embodiments, determining the scattering estimation data of the scanned object based on the tomographic data includes: determining the scattering estimation data based on the tomographic data using at least one of a fast Monte Carlo simulation algorithm, a scattering kernel superposition algorithm, and a deep learning network model.

[0007] In some embodiments, determining the scatter estimation data of the scanned object based on the tomographic scan data further includes: verifying or correcting the scatter estimation data based on a set judgment condition.

[0008] In some embodiments, the judgment condition is that the scatter estimation data is greater than 0 and less than the product of the tomographic data and a proportional coefficient.

[0009] In some embodiments, iteratively updating the scatter estimation data until the target matching condition is satisfied includes: determining scatter correction data of the scanned object based on the scatter estimation data; determining, from the scatter correction data and the positioning scan data, scatter correction projection data and positioning scan projection data corresponding to the scatter correction data and the positioning scan data, respectively, within a combined projection having the same acquisition angle and bed code position range; and determining whether the scatter correction projection data and the positioning scan projection data satisfy the target matching condition.

[0010] In some embodiments, determining whether the scatter-corrected projection data and the positioning scan projection data meet the target matching condition includes: determining whether the average value of the difference between the scatter-corrected projection data and the positioning scan projection data is less than a first threshold, and / or determining whether the sum of the difference between the scatter-corrected projection data and the positioning scan projection data is less than a second threshold.

[0011] One embodiment of the present specification provides a scatter correction system, comprising: a positioning scan data acquisition module for acquiring positioning scan data of a scanned object; a tomography scan data determination module for acquiring tomography scan data of the scanned object; a scatter estimation data determination module for determining scatter estimation data of the scanned object based on the tomography scan data; and a correction module for determining target data based on the scatter estimation data and a target matching condition, wherein the target matching condition is related to the positioning scan data.

[0012] One embodiment of this specification provides a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes the method described in any one of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same numbers represent the same structures, wherein:

[0014] Figure 1 is a schematic diagram of an application scenario of a scatter correction system according to some embodiments of this specification;

[0015] Figure 2 is an exemplary flow chart of a scatter correction method according to some embodiments of this specification;

[0016] Figure 3 is an exemplary flow chart of a process for determining scatter estimation data according to some embodiments of this specification;

[0017] Figure 4 is an exemplary flow chart of iteratively updating scatter estimation data or scatter correction data according to some embodiments of this specification;

[0018] Figure 5 is a schematic diagram of the correspondence between positioning scan data and tomographic scan data at the same scanning angle according to some embodiments of this specification;

[0019] Figure 6 is an exemplary module diagram of a scatter correction system according to some embodiments of this specification. DETAILED DESCRIPTION

[0020] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0021] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0022] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0023] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0024] Due to the interaction between radiation and the object during CT scanning, the scanned image will generate a certain amount of scatter noise, which directly affects the image quality. Improving the accuracy and optimizing the correction effect of the scatter correction process for the scanned image is a key issue. During the positioning scan phase, the positioning scan data obtained for the scanned object is typically acquired under an extremely narrow scanning beam, and the impact of scatter noise is minimal, or even negligible. Based on the aforementioned characteristics of the positioning scan data, by setting target matching conditions associated with the positioning scan data, during the scatter correction process based on the tomographic scan data of the scanned object, the optimized target matching conditions are utilized to iteratively update the scatter estimation data determined from the tomographic scan data until the optimized target matching conditions are met. The resulting scatter estimation data or scatter correction data is more accurate, which improves the scatter correction efficiency and optimizes the correction effect. As a result, the scatter correction processed image is closer to the ideal state image with less scatter noise, significantly reducing the impact of scatter noise on the scanned image and effectively improving image quality.

[0025] Figure 1 1 is a schematic diagram of an application scenario of the scatter correction system 100 according to some embodiments of this specification.

[0026] The scatter correction system 100 may include a CT device 110, a network 120, one or more terminals 130, a processing device 140, and a storage device 150. The connections between the components in the scatter correction system 100 may be variable. Figure 1 As shown, CT device 110 can be connected to processing device 140 via network 120. For another example, CT device 110 can be directly connected to processing device 140, as indicated by the dashed double-headed arrow connecting CT device 110 and processing device 140. For another example, storage device 150 can be connected to processing device 140 directly or via network 120. As an example, terminal 130 can be directly connected to processing device 140 (as indicated by the dashed double-headed arrow connecting terminal 130 and processing device 140) or connected to processing device 140 via network 120.

[0027] The CT device 110 can be configured to scan a scan object using high-energy rays (such as X-rays, gamma rays, etc.) to collect scan data related to the scan object. The scan data can be used to generate one or more images of the scan object. In some embodiments, the CT device 110 may include a computed tomography (CT) scanner, a digital radiography (DR) scanner (e.g., mobile digital radiography), a digital subtraction angiography (DSA) scanner, a dynamic spatial reconstruction (DSR) scanner, an X-ray microscope scanner, a multimodal scanner, or the like, or a combination thereof. Exemplary multimodal scanners may include a computed tomography-positron emission tomography (CT-PET) scanner, a computed tomography-magnetic resonance imaging (CT-MRI) scanner, or the like. The scan object may be biological or non-biological. By way of example only, the scan object may include a patient, an artificial object (e.g., a phantom), or the like. For another example, the scan object may include a specific part, organ, and / or tissue of a patient.

[0028] like Figure 1 As shown, the CT device 110 may include a gantry 111, a detector 112, a detection area 113, a workbench 114, and a radiation source 115. The gantry 111 may support the detector 112 and the radiation source 115. A scanned object may be placed on the workbench 114 for scanning. The radiation source 115 may emit radiation toward the scanned object. The detector 112 may detect radiation (e.g., X-rays) emitted from the radiation source 115. In some embodiments, the detector 112 may include one or more detector units. The detector units may include scintillation detectors (e.g., cesium iodide detectors), gas detectors, etc. The detector units may include single-row detectors and / or multiple-row detectors.

[0029] Network 120 may include any suitable network that can facilitate the exchange of information and / or data for scatter correction system 100. In some embodiments, one or more components of scatter correction system 100 (e.g., CT device 110, terminal 130, processing device 140, storage device 150) can exchange information and / or data with each other via network 120. For example, processing device 140 can obtain image data from CT device 110 via network 120. For another example, processing device 140 can obtain user instructions from terminal 130 via network 120.

[0030] The network 120 may be and / or include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN), a wide area network (WAN), etc.), a wired network (e.g., an Ethernet network), a wireless network (e.g., an 802.11 network, a Wi-Fi network, etc.), a cellular network (e.g., a Long Term Evolution (LTE) network), a frame relay network, a virtual private network (“VPN”), a satellite network, a telephone network, a router, a hub, a switch, a server computer, and / or any combination thereof. By way of example only, the network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth TM Network, ZigBee TM network, a near field communication (NFC) network, etc., or any combination thereof. In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include wired and / or wireless network access points such as base stations and / or Internet exchange points, through which one or more components of the scatter correction system 100 can connect to the network 120 to exchange data and / or information.

[0031] The terminal 130 may include a mobile device 131, a tablet computer 132, a laptop computer 133, etc., or any combination thereof. In some embodiments, the mobile device 131 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, etc., or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a control device for a smart electrical device, a smart monitoring device, a smart TV, a smart camera, an intercom, etc., or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a personal digital assistant (PDA), a gaming device, a navigation device, a point of sale (POS) device, a laptop computer, a tablet computer, a desktop computer, etc., or any combination thereof. In some embodiments, the virtual reality device and / or the augmented reality device include a virtual reality helmet, virtual reality glasses, virtual reality goggles, an augmented reality helmet, augmented reality glasses, augmented reality goggles, etc., or any combination thereof. For example, the virtual reality device and / or the augmented reality device may include Google Glass TM , Oculus Rift TM , Hololens TM , Gear VR TM In some embodiments, the terminal 130 may be part of the processing device 140 .

[0032] The processing device 140 can process data and / or information obtained from the CT device 110, the terminal 130, and / or the storage device 150. For example, the processing device 140 can obtain data acquired by the CT device 110, use the data to perform imaging, and perform scatter estimation and / or scatter correction on the image. For another example, the processing device 140 can obtain a scanning protocol for a scout scan and / or a tomography scan from the terminal 130.

[0033] In some embodiments, processing device 140 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processing device 140 may be local or remote. For example, processing device 140 may access information and / or data stored in CT device 110, terminal 130, and / or storage device 150 via network 120. For another example, processing device 140 may be directly connected to CT device 110, terminal 130, and / or storage device 150 to access stored information and / or data. In some embodiments, processing device 140 may be implemented on a cloud platform. By way of example only, a cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or any combination thereof.

[0034] The storage device 150 can store data, instructions, and / or any other information. In some embodiments, the storage device 150 can store data obtained from the CT device 110, the terminal 130, and / or the processing device 140. For example, the storage device 150 can store scout scan data and / or tomographic data obtained from the CT device 110. For another example, the storage device 150 can store a scan protocol input from the terminal 130. For another example, the storage device 150 can store data generated by the processing device 140 (e.g., scout images, tomographic images, scatter estimation data, and scatter correction results).

[0035] In some embodiments, storage device 150 may store data and / or instructions that processing device 140 may execute or use to execute the exemplary methods described herein. In some embodiments, storage device 150 includes a mass storage device, a removable storage device, volatile read-write memory, read-only memory (ROM), or the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid-state drives, or the like. Exemplary removable storage devices may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, or the like. Exemplary volatile read-write memory may include random access memory (RAM). Exemplary RAM may include dynamic random access memory (DRAM), double data rate synchronous dynamic access memory (DDR SDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero capacitance random access memory (Z-RAM). Exemplary ROM may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disk read-only memory (CD-ROM), and digital versatile disk reallocation memory. In some embodiments, the storage device 150 can be implemented on a cloud platform. By way of example only, a cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or any combination thereof.

[0036] In some embodiments, storage device 150 can be connected to network 120 to communicate with one or more other components in scatter correction system 100 (e.g., processing device 140, terminal 130). One or more components in scatter correction system 100 can access data or instructions stored in storage device 150 via network 120. In some embodiments, storage device 150 can be directly connected to or communicate with one or more other components in scatter correction system 100 (e.g., processing device 140, terminal 130). In some embodiments, storage device 150 can be part of processing device 140.

[0037] The description of the scatter correction system 100 is intended to be illustrative and not to limit the scope of the present application. Many alternatives, modifications, and variations will be apparent to those skilled in the art. It is understood that, after understanding the principles of the system, those skilled in the art may arbitrarily combine the modules or form subsystems connected to other modules without departing from the principles. In some embodiments, Figure 6The positioning scan data acquisition module 310, tomography data acquisition module 320, scatter estimation data determination module 330, and correction module 340 disclosed herein may be different modules within a system, or a single module may implement the functionality of two or more of the aforementioned modules. For example, each module may share a storage module, or each module may have its own storage module. The features, structures, methods, and other characteristics of the exemplary embodiments described herein may be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the processing device 140 and the CT device 110 may be integrated into a single device. Such variations are within the scope of this specification.

[0038] Figure 2 is an exemplary flow chart of the scatter correction method 200 according to some embodiments of the present specification. In some embodiments, the process of the scatter correction method 200 can be performed by Figure 1 The scatter correction system 100 shown, or Figure 6 For example, the scatter correction method 200 may be stored in a storage device (e.g., a storage unit of the storage device 150 or the processing device 140) in the form of a program or instruction. Figure 6 When the modules shown execute the program or instructions, the scatter correction method 200 can be implemented. In some embodiments, the scatter correction method 200 can be completed using one or more additional operations not described below, and / or not by one or more operations discussed below. Figure 2 The order in which the operations are presented is not limiting.

[0039] In step 210 , positioning scan data of the scan object may be acquired. In some embodiments, step 210 may be performed by the positioning scan data acquisition module 310 .

[0040] The scanned object may be a living or non-living object. In some embodiments, the scanned object may include a patient, an organ of the patient, a tissue of the patient, or any body part of the patient, or any combination thereof. In some embodiments, the scanned object may be an artificial scanned object, such as a water phantom or a phantom of a human organ or tissue.

[0041] The positioning scan data is scan data obtained by performing a positioning scan before performing a tomographic scan on the scanned object. In some embodiments, the positioning scan data may be positioning slice scan data. In some embodiments, the positioning slice scan data may be obtained by positioning slice scanning. In some embodiments, during the positioning slice scanning process, the scanning beam may be set to a narrow range, such as an extremely narrow slit width of 4*0.5mm. In some embodiments, the slit width may be a width parameter of the scanning beam (such as an X-ray beam) transmitted by the collimator in the CT device 110. In some embodiments, appropriate scanning protocol parameters for the positioning slice scan may be set based on the narrow slit width. In some embodiments, in combination with the scanning power parameters, the scanning protocol parameters for the positioning slice scan may be set to 120kV, 30mAs, and 4*0.5mm. The positioning scan setting method of the narrower scanning beam can minimize the scattering effect generated by the scanning beam during the scanning process. It can be considered that there is no scattering in this relatively ideal state, or the scattering effect can be negligible. The positioning scan data thus obtained can be used as standard data or reference data for scatter correction processing.

[0042] In some embodiments, multiple positioning film scanning angles can be set to obtain positioning scan data. The embodiment of the present application does not impose any special restrictions on the number of positioning film scanning angles and their specific values. For example, according to the set number of positioning film scanning angles (such as 0°, 90°, 180°, 270°, etc.), 1 to 2 groups of positioning scan data can be obtained, and so on. In some embodiments, the positioning film scanning angle can be set to 0° and 270°, and the bed code position range (i.e., bed code position range) can be 1000mm-1400mm. In some embodiments, the positioning film scanning data may include anteroposterior film scanning data and / or lateral film scanning data. In some embodiments, the positioning film scanning angle of the anteroposterior film scanning data can be set to: the tube is 0° directly above or 180° directly below the scanning object. In some embodiments, the lateral film scanning data can be set to: the tube is 90° or 270° on both lateral sides of the scanning object. In some embodiments, the positioning scan data can be expressed as I topo .

[0043] In some embodiments, step 210 may be implemented as follows: after the medical staff positions the scan subject on the bed, they perform a corresponding positioning scan according to a pre-set positioning scan protocol to obtain positioning scan data. In some embodiments, step 210 may be implemented as follows: performing a positioning scan according to a pre-set positioning scan protocol to obtain initial positioning scan data; and pre-processing the initial positioning scan data to obtain positioning scan data.

[0044] Step 220 , acquiring tomographic data of the scanned object. In some embodiments, step 220 may be performed by the tomographic data acquisition module 320 .

[0045] Tomographic scan data is scan data acquired during a tomographic scan phase in order to obtain a tomographic image. In some embodiments, the scanning protocol parameters of the tomographic scan can be determined based on the positioning scan data. As an example only, the scanning range of the scanning part can be selected or determined on the image corresponding to the positioning scan data, and used as the scanning range of the tomographic scan. In some embodiments, the scanning range of the positioning scan data can be larger than the range of the area where the scanning part is located. In some embodiments, the slit width in the scanning protocol parameters of the tomographic scan can be larger than the slit width of the positioning film scan. In some embodiments, the slit width of the tomographic scan can be set to 320*0.5mm. In some embodiments, combined with the scanning power parameters, the scanning protocol parameters of the tomographic scan can be set to 120kv, 200mAs, and a slit width of 320*0.5mm.

[0046] In some embodiments, multiple tomographic scanning angles can be set to acquire tomographic data. The number of tomographic scanning angles and their specific values ​​are not particularly limited. For example, a number of tomographic scanning angles (e.g., 0°, 90°, 180°, 270°, etc.) can be set to acquire 200 sets of tomographic data, and so on. In some embodiments, all or a portion of the total tomographic data acquired at the multiple tomographic scanning angles can be selected as the tomographic data to be used for scatter correction. For example, 1 / 10 of the tomographic data can be selected, for example, 10 sets of 100 sets of tomographic data can be selected. In some embodiments, the tomographic scanning angle value range can be set to 0°-360°, and the bed code position range, i.e., the bed code position range, can be 1000 mm-1400 mm. In some embodiments, at least a portion of the positioning scan data and at least a portion of the tomography scan data have the same scanning angle and / or the same bed code position range. As for the number of the same scanning angles and the specific number and size of the same bed code position range, the embodiments of the present application do not impose any special restrictions.

[0047] In some embodiments, step 220 can be implemented as follows: performing a tomography scan according to a set tomography scanning protocol to obtain tomography data. In some embodiments, step 220 can be implemented as follows: performing a tomography scan according to a set tomography scanning protocol to obtain initial tomography data; and preprocessing the initial tomography data to obtain the tomography data.

[0048] In some embodiments, the preprocessing of the positioning scan data may include at least one of air correction, bad channel correction, etc. In some embodiments, the preprocessing of the tomography data may include at least one of air correction, bad channel correction, etc. In some embodiments, the preprocessing of the positioning scan data and the preprocessing of the tomography data may perform the same or similar operations. In some embodiments, the tomography data (or the preprocessed tomography data) may be projection domain data in the form of raw data. In some embodiments, the tomography data (or the preprocessed tomography data) may be two-dimensional CT image data or three-dimensional CT image data, which may be represented in matrix form as an example only. In some embodiments, the tomography data (or the preprocessed tomography data) may be represented as I t , which may include scattered noise.

[0049] In some embodiments, air correction can generate an air correction table by air scanning. In some embodiments, the air correction table can include one or more air correction parameters. In some embodiments, the air correction table can be updated based on multiple reference values. In some embodiments, the data type of the reference value can be the same as the data type of one or more air correction parameters in the correction table. In some embodiments, bad channel correction can be performed by obtaining the measurement values ​​of the available channels around the damaged channel, interpolating or taking the average value, and estimating the actual radiation attenuation value of the bad channel. In some embodiments, a repair operation can be performed on the bad channel to be repaired area of ​​the initial positioning scan data or the initial tomography scan data.

[0050] Step 230 : Determine scatter estimation data of the scanned object based on the tomographic scan data (or pre-processed tomographic scan data).

[0051] The scatter estimation data refers to the estimated parameters of the scatter magnitude generated during the tomography process. In some embodiments, the scatter estimation data can be represented in a matrix form, for example, the scatter signal distribution I s In some embodiments, the positioning scan data I topo The size (eg, matrix size) of the tomographic data (or pre-processed tomographic data) may be larger than that of the tomographic data. t In some embodiments, the tomographic data (or pre-processed tomographic data) is t The size can be estimated with the scattering data I s The same size.

[0052] Figure 3 is an exemplary flow chart of a process for determining scatter estimation data according to some embodiments of the present specification.

[0053] In some embodiments, step 230 may include the following two sub-steps:

[0054] Step 231, determining scattering estimation data using a preset scattering estimation method;

[0055] Step 232: Verify or correct the scattering estimation data.

[0056] In some embodiments, the preset scattering estimation method may be a projection domain scattering estimation method (i.e., scattering correction is performed on projection domain data). In some embodiments, the preset scattering estimation method may include a fast Monte Carlo simulation algorithm, a scattering kernel superposition algorithm, a deep learning network model, or any other feasible scattering estimation algorithm, such as a wavelet Fourier transform method.

[0057] In some embodiments, step 231, determining scattering estimation data using a preset scattering estimation method, can be implemented as the following process: based on tomography data, determining scattering estimation data using at least one of a fast Monte Carlo simulation algorithm, a scattering kernel superposition algorithm, and a deep learning network model.

[0058] In some embodiments, the use of a fast Monte Carlo simulation algorithm to determine the scattering estimation data can be implemented as follows: the tomography data (or pre-processed tomography data) is t and the first preset system parameters as simulation input, and the scattering signal distribution I is obtained by using the fast Monte Carlo simulation algorithm. s (i.e. scatter estimation data).

[0059] In some embodiments, the first preset system parameters may include slit width, energy spectrum parameters, detector position and response parameters. In some embodiments, the energy spectrum parameters may include the tube energy spectrum and its intensity distribution data. In some embodiments, for scatter estimation applications with a low number of scans and a low computational load, a fast Monte Carlo simulation algorithm may be selected to determine the scatter estimation data, thereby achieving high computational accuracy while keeping the computational time within an expected reasonable range and avoiding excessive computational time.

[0060] In some embodiments, the scatter estimation data is determined by using a scatter kernel superposition (SKS) algorithm, which can be implemented as follows: the tomography data (or pre-processed tomography data) is t Input the preset SKS model, and after model calculation, output the scattered signal distribution I s .

[0061] In some embodiments, multiple sets of simulation fitting parameters, multiple sets of total signal training data and scattered signal distribution label data can be used to perform simulation fitting, and finally obtain multiple sets of kernels, thereby obtaining a preset SKS model.

[0062] In some embodiments, the multiple sets of fitting parameters may include multiple sets of different energy spectrum parameters, slit widths, and scanning area sizes. In some embodiments, the energy spectrum parameters may include the tube energy spectrum and its intensity distribution data. In some embodiments, the tomography data may include scattered signal distribution data. t and remove the scattered signal data Ip.

[0063] In some embodiments, determining the scatter estimation data using a deep learning network model may be implemented as follows: converting the tomography data (or pre-processed tomography data) into t Input the preset deep learning network model for calculation and output the scattered signal distribution I s In some embodiments, multiple sets of tomography training data (which may include scattered signal distribution data) may be used. t And remove the scattered signal data Ip) and its scattered signal distribution label data, and train to obtain a preset deep learning network model.

[0064] In some embodiments, to avoid abnormal results and ensure more accurate scatter estimation data, the scatter estimation data may be verified or corrected in step 232 . This may be implemented as follows: verifying or correcting the scatter estimation data based on a set judgment condition.

[0065] In some embodiments, the judgment condition can be set as the scatter estimation data being greater than 0 and less than the product of the tomographic data and the proportional coefficient, which can more effectively avoid the occurrence of negative values ​​or abnormal results of excessively large values ​​in the scatter estimation data, thereby obtaining more accurate scatter estimation data. In some embodiments, the judgment condition may include: the scatter signal distribution I s Whether the value of satisfies the following preset value range: 0<I s ≤q*It, where q is the proportionality coefficient. In some embodiments, the scattered signal distribution I s Whether the value of satisfies the judgment condition, that is, whether it satisfies the aforementioned preset value range, in order to perform the scattered signal distribution I s In some embodiments, when determining the scattered signal distribution I s When the above preset value range is not satisfied, the preset assignment expression can be used to distribute the scattered signal I s Perform corresponding correction processing, that is, the scattered signal distribution I sThe corresponding assignment is performed to satisfy the aforementioned preset value range so that the scatter estimation data is within the expected value range, thereby ensuring the accuracy of the scatter estimation data. In some embodiments, the preset assignment expression may include the following mathematical expression (1):

[0066] I s =max(0,I s ),I s =min(I s ,q*I t ) (1)

[0067] In some embodiments, the specific value of the proportionality coefficient q can be determined based on a second preset system parameter. In some embodiments, the second preset system parameter can include an energy spectrum parameter and a slit width. In some embodiments, the energy spectrum parameter can include the tube energy spectrum and its intensity distribution data. In some embodiments, the proportionality coefficient q can take a value within a range. In some embodiments, the proportionality coefficient q can take a value of 0.97. At this proportionality coefficient value, the expected range of values ​​for the scatter estimation data is relatively reasonable, avoiding abnormally large values. In some embodiments, the proportionality coefficient q can be a default setting of the scatter correction system 100, or can be manually set by the user, or can be adjusted by the processing device 140 according to actual needs.

[0068] Step 240 : Determine target data based on the scatter estimation data and a target matching condition, wherein the target matching condition is related to the positioning scan data. In some embodiments, step 240 may be performed by the correction module 340 .

[0069] In some embodiments, the target matching condition may include a threshold condition and an iterative convergence condition. In some embodiments, the threshold condition may include positioning data within a preset range. The positioning data within the preset range may be determined based on the positioning scan data of the scanned object obtained in step 210 above. In some embodiments, step 240 may be implemented as the following process:

[0070] Based on the scatter estimation data, the scatter correction data is determined. As for the specific process of determining the scatter correction data based on the scatter estimation data, please refer to the following Figure 4 The description of the relevant content will not be repeated here.

[0071] Thereafter, at least a portion of the scatter estimation correction data is compared with a preset range of positioning data. If the comparison result satisfies a threshold condition and the scatter estimation data satisfies the preset positioning data range, the scatter estimation correction data is determined as target data. In some embodiments, the selected scatter estimation correction data may be at least a portion or all of the data. In some embodiments, the determined target data may be output as scatter correction result data. In some embodiments, image reconstruction may be performed based on the output target data, for example, including reconstruction processing of tomographic data. In some embodiments, if the scatter estimation data does not satisfy the preset positioning data range, the scatter estimation data may be iteratively updated based on the tomographic data until the target matching condition is satisfied, and the target data may be determined based on the updated scatter estimation data. This process may be described in detail below. Figure 4 The relevant content description will not be repeated here.

[0072] In some embodiments, in step 240, the scatter estimation data is iteratively updated until a target matching condition is satisfied. This can be implemented in the following manner: based on the scatter estimation data, scatter correction data of the scanned object is determined; from the scatter correction data and the scout scan data, scatter correction projection data and scout scan projection data corresponding to the scatter correction data and the scout scan data are determined within a combined projection set having the same acquisition angle and bed code position range; and whether the scatter correction projection data and the scout scan projection data satisfy the target matching condition is determined.

[0073] Figure 4 is an exemplary flow chart of iteratively updating scatter estimation data or scatter correction data according to some embodiments of the present specification.

[0074] In some embodiments, in step 240, iteratively updating the scatter estimation data until the target matching condition is satisfied may be implemented as the following sub-steps:

[0075] Sub-step 241 : determining scatter correction data of the scanned object based on the scatter estimation data.

[0076] In some embodiments, sub-step 241 can be implemented as the following process: by calculating the following mathematical expression (2), the scatter correction data can be expressed as: p ,

[0077] Ip=I t -I s (2)

[0078] By using tomographic data (or pre-processed tomographic data) t Subtract the scattered signal distribution I s , that is, deducting the scatter distribution data to obtain the scatter correction data after the scatter correction of the scanned object.

[0079] Sub-step 242 : determining, from the scatter correction data and the scout scan data, the scatter correction projection data and the scout scan projection data corresponding to the scatter correction data and the scout scan data in a combined projection within the same scanning angle and bed code position range.

[0080] In some embodiments, the projection set with the same scanning angle and bed code position range can be the minimum projection set of the scatter correction data and the positioning scan data. In some embodiments, the minimum projection set with the same scanning angle and bed code position range can be extracted from the scatter correction data and the positioning scan data. In some embodiments, the scatter correction projection data can be represented as I p ', the positioning scan projection data can be expressed as I topo '.

[0081] Figure 5 Schematic diagram of the correspondence between the positioning scan data and the tomographic scan data at the same scanning angle according to some embodiments of this specification. Where n is an integer greater than 2. For example only, the positioning slice scanning angle is 0° and 270°, the bed code position range (i.e., bed code position range) is 1000mm-1400mm; the tomographic scan angle range is 0°-360°, and the bed code range is 1120mm-1280mm; then in the scatter correction data I p and positioning scan data I topo The scatter correction projection data I with the same scanning angle of 0° and 270° and bed code range of 1120-1280 are taken out respectively. p ' and positioning scan projection data I topo '.like Figure 5 As shown in the figure, there is a one-to-one correspondence between the positioning scan data and the tomographic scan data at the same scanning angle. Taking the 0° positioning film and the 0° tomographic projection as an example, the tomographic projection data with a slit width of 320*0.5 can correspond to multiple positioning films with a slit width of 4*0.5mm spliced ​​at the same bed code position.

[0082] Since the positioning scan uses extremely narrow beam scanning during the acquisition of positioning scan data, its scattering effect can be basically ignored. Taking the positioning scan data as the standard and basis for scattering correction has direct and significant advantages, which can improve the efficiency of scattering correction, especially the correction accuracy.

[0083] Sub-step 243 : determining whether the scatter correction projection data and the positioning scan projection data meet a target matching condition.

[0084] In some embodiments, the target matching condition associated with the scout scan data may employ an iterative convergence condition associated with the scout scan data. In some embodiments, the iterative convergence condition associated with the scout scan data may be set to: whether the average of the differences between the scatter-corrected projection data and the scout scan projection data is less than a first threshold, or whether the sum of the differences between the scatter-corrected projection data and the scout scan projection data is less than a second threshold.

[0085] In some embodiments, sub-step 243 can be implemented as the following process: determining whether the average value of the difference between the scatter-corrected projection data and the positioning scan projection data is less than a first threshold, and / or determining whether the sum of the difference between the scatter-corrected projection data and the positioning scan projection data is less than a second threshold.

[0086] In some embodiments, the first threshold value can be expressed as ε1, the second threshold value can be expressed as ε2, and the iterative convergence condition can be set based on the following mathematical expressions (3) and (4):

[0087] mean|Ip' i -I topo '|≤ε1 (3)

[0088] sum|Ip' i -I topo '|≤ε2 (4)

[0089] Here, i represents the i-th iteration and its value can be an integer greater than 0.

[0090] That is, in some embodiments, sub-step 243 may be implemented as the following process: in an iterative process, the scatter correction projection data I may be determined. p 'With positioning scan projection data I topo ' satisfies the interval range of mathematical expression (3), or the scatter correction projection data I can be traversed to determine whether the average value of the difference satisfies the interval range of mathematical expression (3). p 'With positioning scan projection data I topo 'Whether the sum of the differences satisfies the interval range of mathematical expression (4).

[0091] In some embodiments, after determining whether the target matching condition is met in sub-step 243, if the determination result is yes, proceed to sub-step 244a; if the determination result is no, proceed to sub-step 244b.

[0092] Sub-step 244a, in response to the target matching condition being satisfied, determining the updated scatter estimation data or the updated scatter correction data as target data;

[0093] Sub-step 244b: in response to the target matching condition not being met, assigning the current scatter correction data to the tomographic data of the next iteration, and determining the scatter estimation data of the next iteration based on the tomographic data of the next iteration, and performing the next iteration.

[0094] In some embodiments, sub-step 244b may be implemented as follows: in response to the target matching not being satisfied, the current scatter correction data I p Assigned to the next iteration of the tomographic data I t , the next iteration is carried out, that is, during the iteration process, when the previous iteration does not meet the iteration convergence condition, the next iteration will be based on the updated tomography data I t Iteration is performed to more quickly satisfy the target matching condition, thereby ending the iteration and completing the scatter correction.

[0095] In some embodiments, in step 240, the iterative update of the scatter estimation data may further include the following process: determining whether the number of iterations or the current scatter correction data or the current scatter estimation data meets the target matching condition. In some embodiments, determining whether the number of iterations or the current scatter correction data or the current scatter estimation data meets the iterative convergence condition may include: determining whether the number of iterations meets the iteration threshold, and / or whether the average value of the difference between the current scatter correction data and the scatter correction data generated after the previous iteration is less than a third threshold, and / or whether the sum of the differences between the current scatter correction data and the scatter correction data generated after the previous iteration is less than a fourth threshold, and / or whether the average value of the difference between the current scatter estimation data and the scatter estimation data generated after the previous iteration is less than a fifth threshold, and / or whether the sum of the differences between the current scatter estimation data and the scatter estimation data generated after the previous iteration is less than a sixth threshold. In some embodiments, the third threshold may be expressed as ε3, the fourth threshold may be expressed as ε4, the fifth threshold may be expressed as ε5, and the sixth threshold may be expressed as ε6. In some embodiments, the target matching condition may be set based on the following mathematical expressions (5), (6), (7), and (8):

[0096] mean|I pi -I pi -1|≤ε3 (5)

[0097] sum|I pi -I pi -1|≤ε4 (6)

[0098] mean|I si -I si -1|≤ε5 (7)

[0099] sum|I si -I si-1|≤ε6 (8)

[0100] That is, in some embodiments, iteratively updating the scatter estimation data can be further implemented as follows: in the iterative process, determining whether the target matching condition is satisfied, in addition to the scatter correction projection data I p 'With positioning scan projection data I topo ' (for example, according to mathematical expressions (5) and (6)), it can be further determined whether the target matching condition is satisfied based on the number of iterations, the current scatter correction data and / or the current scatter estimation data (for example, according to the target matching conditions of the above mathematical expressions (5), (6), (7), and (8)); or, in the iterative process, it can be determined whether the target matching condition is satisfied based only on the number of iterations, the current scatter correction data and / or the current scatter estimation data (for example, according to the target matching conditions of the above mathematical expressions (5), (6), (7), and (8)), without the need to determine whether the target matching condition is satisfied based on the scatter correction projection data I p 'With positioning scan projection data I topo ' (For example, according to mathematical expressions (3) and (4)). In some embodiments, whether the target matching condition is satisfied during the iterative process can also be determined based only on the scatter correction projection data I p 'With positioning scan projection data I topo ' (e.g., according to mathematical expressions (3), (4)), without making a judgment based on the number of iterations, the current scatter correction data and / or the current scatter estimation data (e.g., according to mathematical expressions (5), (6), (7), (8)).

[0101] In some embodiments, after the target matching condition is satisfied, the updated scatter estimation data can be used directly as the target data. The updated scatter estimation data can be further used for scatter correction (e.g., removing the updated scatter estimation data from the tomographic data) to obtain scatter correction data. In some embodiments, after completing the iterative update in step 240, the updated scatter correction data can be used as the target data. p , that is, the scattered signal distribution I is removed s The data after this is used as the target data, so that the output scatter correction data I can be directly used. p Reconstruction of tomographic scan data.

[0102] In some embodiments, after step 240 is completed, other correction and reconstruction processes may be continued to obtain a final tomographic image, such as a CT tomographic image.

[0103] It should be noted that the above description of process 200 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may make various modifications and variations to process 200 under the guidance of this specification. However, such modifications and variations are still within the scope of this specification.

[0104] Figure 6 FIG. 3 is an exemplary module diagram of a scatter correction system 300 according to some embodiments of the present specification. Figure 6 As shown, the scatter correction system 300 may include a positioning scan data acquisition module 310, a tomography data acquisition module 320, a scatter estimation data determination module 330, and a correction module 340. In some embodiments, the positioning scan data acquisition module 310, the tomography data acquisition module 320, the scatter estimation data determination module 330, and the correction module 340 may be configured to: Figure 1 The scatter correction system 100 shown is implemented as in a CT device 110 .

[0105] The scouting data acquisition module 310 is configured to acquire scouting data of the scanned object. The tomography data acquisition module 320 is configured to acquire tomography data of the scanned object. The scatter estimation data determination module 330 is configured to determine scatter estimation data of the scanned object based on the tomography data. The correction module 340 is configured to determine target data based on the scatter estimation data and a target matching condition, where the target matching condition is related to the scouting data.

[0106] It should be noted that for more technical details on how the positioning scan data acquisition module 310, the tomography data acquisition module 320, the scatter estimation data determination module 330, and the correction module 340 perform corresponding processes or functions to achieve scatter correction, see Figures 1 to 5 The relevant contents of the scatter correction method described in any of the embodiments shown are not repeated here.

[0107] The above description of scatter correction system 300 is for illustrative purposes only and is not intended to limit the scope of this application. It will be apparent to those skilled in the art that various modifications and improvements in form and detail may be made to the application of the above-described method and system without departing from the principles of this application. However, such changes and modifications do not depart from the scope of this application. In some embodiments, scatter correction system 300 may include one or more additional modules. For example, scatter correction system 300 may include a storage module to store data generated by the modules of scatter correction system 300.

[0108] Some embodiments of this specification also provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions, the computer executes the scatter correction method of any of the above embodiments. Figures 1 to 5 The relevant description will not be repeated here.

[0109] The scatter correction method, system, and computer-readable storage medium provided in the embodiments of this specification have at least the following beneficial effects: based on the characteristic that extremely narrow beam scanning makes the influence of scatter noise negligible, positioning scan data is taken into consideration as a factor in optimizing target matching conditions. By setting target matching conditions related to the positioning scan data, during the scatter correction process based on tomographic scan data of the scanned object, the optimized target matching conditions are utilized to iteratively update scatter estimation data determined based on the tomographic scan data until the optimized target matching conditions are satisfied. The resulting scatter estimation data or scatter correction data has higher accuracy, thereby improving the efficiency of scatter correction and optimizing the correction effect. As a result, the image after scatter correction is closer to an ideal state image with less scatter noise, significantly reducing the influence of scatter noise on the scanned image, and effectively improving image quality.

[0110] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0111] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0112] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0113] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0114] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0115] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A scatter correction method, characterized in that: The method comprises: Acquire positioning scan data of the scanned object by scanning a positioning slice, wherein the positioning slice scan is an extremely narrow beam scan; Acquiring tomographic scanning data of the scanned object; determining scatter estimation data of the scanned object based on the tomographic data; determining scatter correction data for the scanned object based on the scatter estimation data; splicing the positioning scan data collected at multiple target bed code positions in the positioning scan data to determine positioning data within a preset range; comparing at least a portion of the scatter correction data with the positioning data within the preset range, and determining the scatter correction data as target data if a comparison result satisfies a threshold condition; At least a portion of the positioning scan data and at least a portion of the tomographic scan data have the same scan angle and / or the same bed code position range.

2. The method according to claim 1, characterized in that If the comparison result does not meet the threshold condition, the method further includes: The scatter estimation data is iteratively updated according to the tomographic data until a target matching condition is satisfied, and target data is determined based on the updated scatter estimation data.

3. The method according to claim 1, characterized in that The determining, based on the tomographic data, scatter estimation data of the scanned object comprises: Based on the tomographic data, the scattering estimation data is determined by using at least one method selected from a fast Monte Carlo simulation algorithm, a scattering kernel superposition algorithm, and a deep learning network model.

4. The method according to claim 3, characterized in that Determining scatter estimation data of the scanned object based on the tomographic scan data further includes: Based on the set judgment conditions, the scattering estimation data is verified or corrected.

5. The method according to claim 4, characterized in that The judgment condition is that the scatter estimation data is greater than 0 and less than the product of the tomographic scan data and a proportional coefficient.

6. The method according to claim 2, characterized in that The iteratively updating the scattering estimation data until the target matching condition is satisfied includes: determining scatter correction data for the scanned object based on the scatter estimation data; Determining, from the scatter correction data and the locator scan data, the scatter correction projection data and the locator scan projection data corresponding to the scatter correction data and the locator scan data within a combined projection having the same acquisition angle and bed code position range; It is determined whether the scatter correction projection data and the positioning scan projection data meet the target matching condition.

7. The method according to claim 6, characterized in that The determining whether the scatter correction projection data and the positioning scan projection data meet the target matching condition includes: It is determined whether an average value of the differences between the scatter-corrected projection data and the scout scan projection data is less than a first threshold, and / or whether a sum of the differences between the scatter-corrected projection data and the scout scan projection data is less than a second threshold.

8. A scatter correction system, characterized in that: The system comprises: A positioning scan data acquisition module, configured to acquire positioning scan data of a scanned object by scanning a positioning slice, wherein the positioning slice scan is an extremely narrow beam scan; a tomographic scan data determination module, configured to obtain tomographic scan data of the scanned object; The scattering estimation data determination module is used to: determining scatter estimation data of the scanned object based on the tomographic data; Correction module for: determining scatter correction data for the scanned object based on the scatter estimation data; splicing the positioning scan data collected at multiple target bed code positions in the positioning scan data to determine positioning data within a preset range; comparing at least a portion of the scatter correction data with the positioning data within the preset range, and determining the scatter correction data as target data if a comparison result satisfies a threshold condition; At least a portion of the positioning scan data and at least a portion of the tomographic scan data have the same scan angle and / or the same bed code position range.

9. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions, the computer executes the method according to any one of claims 1 to 7.

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