Method and apparatus for correcting projected images

Abstract

This invention provides a method and apparatus for correcting projected images. The method includes: a. acquiring multiple first projected images of a scanned object; b. acquiring multiple reference images, wherein each reference image corresponds to at least one of the multiple first projected images; and c. correcting the first projected images based on the grayscale relationship between the first projected images and their corresponding reference images. The method and apparatus for correcting projected images of this invention are characterized by low cost and high correction accuracy.

Description

Technical Field

[0001] This invention relates primarily to computed tomography (CT), and more particularly to a method and apparatus for correcting projected images. Background Technology

[0002] CT scans primarily involve rotating an X-ray source and detector mounted on a gantry to scan the object from different gantry angles, obtaining multiple projected images corresponding to those angles. During a CT scan, the X-ray dose inevitably fluctuates at different angles, causing a difference between the actual and expected grayscale values ​​of the projected images. The principle of CT reconstruction assumes that the logarithmic transformation of the image grayscale values ​​is proportional to the equivalent absorption coefficient of the material. This deviation between the actual and expected values ​​leads to CT values ​​in the reconstructed results deviating from the true values, potentially resulting in incorrect medical diagnoses or inaccurate treatment localization.

[0003] With the development of CT technology, the industry's requirements for reducing patient dose are becoming increasingly stringent. After reducing the dose per image, the relative error of the control system's dose control of the X-ray source will increase, and the readout error of the ionization chamber (or reference ionization chamber) cannot be ignored. Conventional mitigation measures mainly include: (1) minimizing the difference between the actual and expected values ​​of the X-ray source in the hardware and control system, using the average value to replace the true value; (2) introducing a reference ionization chamber to record the true dose corresponding to all projected images and using this value to normalize the projected images. However, the above mitigation measures require high-precision X-ray sources and control systems, or the introduction of a high-precision reference ionization chamber, which will increase the cost of CT equipment. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and apparatus for correcting projected images, which has the characteristics of low cost and high correction accuracy.

[0005] To address the aforementioned technical problems, the present invention provides a method for correcting projected images, comprising:

[0006] a. Acquire multiple first projected images of the scanned object; b. Acquire multiple reference images, wherein each reference image corresponds to at least one of the multiple first projected images; and c. Correct the first projected image according to the grayscale relationship between the first projected image and its corresponding reference image.

[0007] In one embodiment of the present invention, step b includes: acquiring a first three-dimensional image of the scanned object; and generating the plurality of reference images based on the first three-dimensional image.

[0008] In one embodiment of the present invention, step a further includes scattering correction of the plurality of first projection images.

[0009] In one embodiment of the present invention, step b involves generating multiple reference images containing scattering amounts.

[0010] In one embodiment of the present invention, in step c, the grayscale relationship is determined based on the same region of interest in the first projected image and its corresponding reference image.

[0011] In one embodiment of the present invention, step c includes: c11. registering the first projected image and its corresponding reference image to obtain the positional relationship between the first projected image and the reference image; c12. calculating the grayscale distribution difference by selecting the same region of interest in the first projected image and the reference image according to the positional relationship; c13. generating a correction coefficient according to the grayscale distribution difference; and c14. correcting the first projected image according to the correction coefficient.

[0012] In one embodiment of the present invention, in step c12, the grayscale distribution difference is determined by calculating the grayscale ratio of the same region of interest in the first projected image and the reference image.

[0013] In one embodiment of the present invention, step b includes: b1. reconstructing the scanned object based on the plurality of first projection images to obtain a second three-dimensional image; b2. registering the first three-dimensional image and the second three-dimensional image to obtain the positional relationship between the first three-dimensional image and the second three-dimensional image; and b3. generating the plurality of reference images based on the positional relationship and the first three-dimensional image.

[0014] In one embodiment of the present invention, step c includes: c21. Selecting the same region of interest in the first projected image and the reference image and calculating the gray-level distribution difference; c22. Generating a correction coefficient based on the gray-level distribution difference; and c23. Correcting the first projected image based on the correction coefficient.

[0015] In one embodiment of the present invention, in step c21, the grayscale distribution difference is determined by calculating the grayscale ratio of the same region of interest in the first projected image and the reference image.

[0016] In one embodiment of the present invention, step a further includes performing one or more of the following on the plurality of first projected images: bad pixel correction, dark field correction, gain correction, and geometric correction.

[0017] In one embodiment of the present invention, if the first three-dimensional image is a non-CT image, the step of generating the plurality of reference images based on the first three-dimensional image includes: converting the first three-dimensional image into a CT image; and generating the plurality of reference images based on the CT image.

[0018] In one embodiment of the present invention, the plurality of reference images are obtained by one or more of the following methods: analytical method, Monte Carlo method, method of solving Boltzmann equations and convolution superposition algorithm.

[0019] In one embodiment of the present invention, the method further includes: d. reconstructing the corrected plurality of first projection images to obtain a three-dimensional image of the scanned object.

[0020] In one embodiment of the present invention, the reference image is generated based on a second projected image of the scanned object, the second projected image being different from the first projected image.

[0021] In one embodiment of the present invention, the first projected image and the second projected image are generated by different imaging devices.

[0022] In one embodiment of the present invention, the imaging device generates multiple projected images at each gantry corner, wherein the first projected image is formed from a portion of the multiple projected images, and the second projected image is formed from another portion of the multiple projected images.

[0023] Another aspect of the present invention provides a projection image correction apparatus, comprising: a projection image acquisition module adapted to acquire a plurality of first projection images of a scanned object; a reference image acquisition module adapted to acquire a plurality of reference images, wherein each reference image corresponds to at least one of the plurality of first projection images; and a correction module adapted to correct the first projection image according to the grayscale relationship between the first projection image and its corresponding reference image.

[0024] Another aspect of the present invention provides a projection image correction apparatus, comprising: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the method described above.

[0025] Another aspect of the present invention provides a computer-readable storage medium having computer instructions stored thereon, wherein when the computer instructions are executed by a processor, the method described above is performed.

[0026] Compared with the prior art, the present invention has the following advantages:

[0027] The projection image correction method / apparatus of the present invention corrects projection images, reducing the impact of uneven radiation dose and improving the image quality of the reconstructed image. The projection image correction method / apparatus of the present invention can significantly improve the quality of reconstructed images under low dose conditions. Using the projection image correction method of the present invention eliminates the need for a high-precision reference ionization chamber or a high-precision radiation source and control system, significantly reducing costs. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of an imaging system according to some embodiments of the present invention.

[0029] Figure 2 These are schematic diagrams of exemplary hardware and / or software components of a computing device according to some embodiments of the present invention.

[0030] Figure 3 These are schematic diagrams of exemplary hardware and / or software components of a mobile device according to some embodiments of the present invention.

[0031] Figure 4 This is a schematic diagram of a processing device according to some embodiments of the present invention.

[0032] Figure 5 This is a basic flowchart of a projection image correction method according to some embodiments of the present invention.

[0033] Figure 6 This is a schematic diagram of a reference image generator based on an analytical method according to some embodiments of the present invention.

[0034] Figure 7 This is a schematic diagram of the reference image generation and correction steps in some embodiments of the present invention.

[0035] Figure 8 This is a schematic diagram of the reference image generation and correction steps in some other embodiments of the present invention.

[0036] Figure 9 This is a basic flowchart of a projection image correction method according to other embodiments of the present invention.

[0037] Figure 10 This is a basic flowchart of a projection image correction method according to some embodiments of the present invention. Detailed Implementation

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the invention is not limited to the specific embodiments disclosed below.

[0040] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0041] It is understood that the terms "system," "module," "unit," and / or "subunit" used in this invention are a method of distinguishing hierarchical relationships between different structures. However, these terms can be replaced by other expressions if the same purpose can be achieved.

[0042] Generally, the terms "module," "unit," and / or "subunit" as used in this invention refer to logic or a set of software instructions stored in hardware or firmware. The "module," "unit," and / or "subunit" described in this invention can be executed by software and / or hardware modules and can also be stored in any non-transitory computer-readable storage medium or other storage device. In some embodiments, a software module can be compiled and linked into an executable program. The software module here can respond to information passed by itself or other modules, and / or can respond upon detection of certain events or interruptions. A computer-readable storage medium configured to be used on a computing device (e.g., Figure 2 The processor 210 shown, Figure 3The software modules / units / subunits that perform operations on the central processing unit (CPU) 340 shown herein may be computer-readable storage media such as optical discs, digital optical discs, flash drives, magnetic disks, or any other type of tangible media; software modules may also be obtained through digital download (digital download here also includes data stored in compressed or installation packages, which need to be decompressed or decoded before execution). The software code may be stored partially or entirely in the storage device of the computing device performing the operations and applied in the operation of the computing device. Software instructions may be embedded in firmware, such as erasable programmable read-only memory (EPROM). It should also be understood that hardware modules / units / subunits may contain logical units connected together, such as gates, flip-flops, and / or programmable units, such as programmable gate arrays or processors. The functionality of the modules / units / subunits or computing devices described herein is preferably performed by software modules / units / subunits, but may also be represented in hardware or firmware. Generally, the modules / units / subunits described herein are logical modules, not limited by their specific physical form or memory. A module, unit, and / or subunit can be combined with other modules, units, and / or subunits, or can be separated into a series of submodules and / or subunits.

[0043] Unless otherwise expressly indicated, it should be understood that when a unit, engine, module, or subunit is “located,” “connected to,” or “coupled to” another unit, engine, module, or subunit, said unit, engine, module, or subunit may be directly located, connected to, coupled to, or communicated with the other unit, engine, module, or subunit, or there may be intermediate units, engines, modules, or subunits. The term “and / or” as used in this invention includes any and all combinations of one or more of the associated listed terms.

[0044] These and other features and characteristics of the invention, the operation and function of the relevant elements of the structure, and the economy of the combination and manufacture of the components will become more apparent from the following detailed description with reference to the accompanying drawings, all of which are part of the invention. However, it should be clearly understood that the drawings are for illustrative and descriptive purposes only and are not intended to limit the scope of the invention. It should be understood that all drawings are not drawn to scale.

[0045] One aspect of the present invention relates to a method and apparatus for correcting multiple projected images. The projected images are generated based on scan data acquired by an imaging device at multiple gantry angles, with each projected image corresponding to one gantry angle. It is understood that the imaging device can acquire scan data multiple times at a single gantry angle, thereby generating multiple projected images corresponding to the same gantry angle. To correct the projected images corresponding to the gantry angles, the apparatus can perform the method to generate a reference image corresponding to the projected image using a reconstructed three-dimensional image, and then correct the projected image based on the grayscale relationship between the projected image and its corresponding reference image, thereby normalizing the multiple projected images acquired by the imaging device. It should be noted that, in the present invention, "projected image and reference image correspond" means that the reference image and the projected image have pixels corresponding to the same spatial point. In some embodiments, the method and apparatus can correct multiple projected images corresponding to multiple gantry angles. The method and apparatus can also reconstruct a three-dimensional image of the scanned object based on the multiple corrected projected images corresponding to multiple gantry angles.

[0046] Figure 1 This is a schematic diagram of an imaging system 100 according to some embodiments of the present invention. In some embodiments, the imaging system 100 may include a conventional CT system, a cone-beam CT (CBCT) system, a spiral CT system, a multi-slice CT system, a digital subtraction angiography (DSA) system, a radiotherapy system with imaging modes, or any combination thereof. In some embodiments, the imaging beam used by the imaging system 100 may be X-rays, gamma rays, ultrasound, or any combination thereof.

[0047] like Figure 1 As shown, the imaging system 100 may include a CT scanner 110, a network 120, a terminal 130, a processing device 140, and a storage device 150. The components in the imaging system 100 can be connected to each other in various ways. For example, the CT scanner 110 may be connected to the processing device 140 via the network 120. Alternatively, the CT scanner 110 may be directly connected to the processing device 140. Another example is that the storage device 150 may be directly connected to the processing device 140 or via the network 120. Yet another example is that the terminal 130 may be directly connected to the processing device 140 or via the network 120.

[0048] The CT scanner 110 may include a gantry 111, a detector 112, a radiation source 113, and a scanning table 114. The detector 112 and the radiation source 113 may be mounted relative to each other on the gantry 111. The object to be scanned may be placed on the scanning table 114 and moved into the detection channel of the CT scanner 110. For illustrative purposes, references to... Figure 1The reference coordinate system shown may include an X-axis, a Y-axis, and a Z-axis. The Z-axis may indicate the direction in which the scanned object is moved into and / or out of the detection channel of the CT scanner 110. The X-axis and Y-axis may form a plane perpendicular to the Z-axis.

[0049] Radiation source 113 can emit X-rays, gamma rays, ultrasound, etc., to scan an object placed on a scanning bed 114. The scanned object can be a living organism (e.g., a patient, animal) or a non-living organism (e.g., a man-made object). Detector 112 can detect radiation (e.g., X-rays, gamma rays, ultrasound, etc.) emitted from radiation source 113. In some embodiments, detector 112 may include multiple detector units. The detector units may include scintillation detectors (e.g., cesium iodide detectors), gas detectors, and / or ultrasound detectors, etc. The detector units may be arranged in a single row or multiple rows.

[0050] In some embodiments, the CT scanner 110 may include one or more components for preventing or reducing beam hardening and / or radiation scattering during scanning. For example, the CT scanner 110 may include a grid (e.g., an anti-scatter grid) and / or other components that can prevent or reduce beam hardening. As another example, the CT scanner 110 may include an X-ray collimator, a metal grid, a slit, a beam scattering correction plate (BSA), a beam attenuation grid (BAG), and / or other components that can prevent or reduce radiation scattering.

[0051] Network 120 can facilitate the exchange of information and / or data. In some embodiments, at least one component of imaging system 100 (e.g., CT scanner 110, terminal 130, processing device 140, or storage device 150) can send information and / or data to another component of imaging system 100 via network 120. For example, the processing device 140 can obtain scan data from the CT scanner 110 via network 120. As another example, the processing device 140 can obtain user instructions from the terminal 130 via network 120. In some embodiments, network 120 can be any type of wired or wireless network, or a combination thereof. Network 120 may include public networks (e.g., the Internet), private networks (e.g., local area networks (LANs), wide area networks (WANs)), wired networks (e.g., Ethernet), wireless networks (e.g., 802.11 networks, Wi-Fi networks), cellular networks (e.g., LTE), Frame Relay networks, virtual private networks (VPNs), satellite networks, telephone networks, routers, hubs, switches, server computers, or combinations thereof. By way of example only, the network 120 may include a cable network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, etc., or any combination thereof. In some embodiments, the network 120 may include at least one network access point. For example, the network 120 may include a wired or wireless network access point, such as a base station and / or an Internet exchange point, through which the components of the imaging system 100 can connect to the network 120 to exchange data and / or information.

[0052] Terminal 130 includes mobile devices 130-1, tablet computers 130-2, laptop computers 130-3, etc., or any combination thereof. In some embodiments, mobile device 130-1 may include smart home devices, wearable devices, smart mobile devices, virtual reality devices, augmented reality devices, etc., or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, smart appliance control devices, smart monitoring devices, smart TVs, smart cameras, walkie-talkies, etc., or any combination thereof. In some embodiments, the wearable devices may include smart bracelets, smart shoes and socks, smart glasses, smart helmets, smartwatches, smart clothing, smart backpacks, smart accessories, etc., or any combination thereof. In some embodiments, the smart mobile devices may include smartphones, personal digital assistants (PDAs), gaming devices, navigation devices, point-of-sale (POS) devices, etc., or any combination thereof. In some embodiments, the virtual reality devices may include virtual reality helmets, virtual reality glasses, virtual reality goggles, augmented reality helmets, augmented reality glasses, augmented reality goggles, etc., or any combination thereof. For example, the virtual reality devices and / or the augmented reality devices may include Google Glass, Oculus Rift, HoloLens, Gear VR, etc. In some embodiments, terminal 130 can remotely operate CT scanner 110. For example, terminal 130 can operate CT scanner 110 via a wireless connection. In some embodiments, terminal 130 can receive information and / or instructions input by a user and transmit the received information and / or instructions to CT scanner 110 or processing device 140 via network 120. In some embodiments, terminal 130 can receive data and / or information from processing device 140. In some embodiments, terminal 130 may be part of processing device 140. In some embodiments, terminal 130 may be omitted.

[0053] In some embodiments, the processing device 140 can process data and / or information obtained from the CT scanner 110, the terminal 130, or the storage device 150. For example, the processing device 140 can acquire multiple projected images corresponding to multiple gantry angles. The processing device 140 can also correct the projected images to generate multiple corrected projected images corresponding to the gantry angles.

[0054] The processing device 140 may be a central processing unit (CPU), a digital signal processor (DSP), a system-on-a-chip (SoC), a microprocessor (MCU), or any combination thereof. In some embodiments, the processing device 140 may be local or remote. For example, the processing device 140 may access information and / or data stored in the CT scanner 110, the terminal 130, and / or the storage device 150 via a network 120. As another example, the processing device 140 may be directly connected to the CT scanner 110, the terminal 130, and / or the storage device 150 to access the information and / or data stored therein. In some embodiments, the processing device 140 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, a cross-cloud, a multi-cloud, or any combination thereof. In some embodiments, the processing device 140 may be implemented in accordance with the present invention. Figure 2 It is implemented on a computing device 200 having at least one component, as shown.

[0055] Storage device 150 can store data and / or instructions. In some embodiments, storage device 150 can store data obtained from terminal 130 and / or processing device 140. In some embodiments, storage device 150 can store data and / or instructions that the processing device 140 can execute or use to execute the exemplary methods described in this invention. In some embodiments, storage device 150 may include mass storage, removable storage, volatile read-write storage, read-only storage (ROM), etc., or any combination thereof. Exemplary mass storage may include disks, optical disks, solid-state drives, etc. Exemplary removable storage may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, etc. Exemplary volatile read-write storage may include random access memory (RAM). Exemplary RAM may include dynamic RAM (DRAM), double data rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitance RAM (Z-RAM), etc. Exemplary ROMs may include mask ROMs (MROMs), programmable ROMs (PROMs), erasable programmable ROMs (EPROMs), electrically erasable programmable ROMs (EEPROMs), optical disc ROMs (CD-ROMs), and digital universal disk ROMs, etc. In some embodiments, the storage device 150 may be implemented on a cloud platform. By way of example only, the cloud platform may include private clouds, public clouds, hybrid clouds, community clouds, distributed clouds, cross-cloud, multi-cloud, etc., or any combination thereof.

[0056] In some embodiments, storage device 150 may be connected to network 120 to communicate with at least one component of imaging system 100 (e.g., terminal 130, processing device 140). At least one component of imaging system 100 may access data or instructions stored in storage device 150 via network 120. In some embodiments, storage device 150 may be directly connected to or communicate with at least one component of imaging system 100 (e.g., terminal 130, processing device 140). In some embodiments, storage device 150 may be part of processing device 140.

[0057] Figure 2 This is a schematic diagram of exemplary hardware and / or software components of a computing device 200 according to some embodiments of the present invention. The computing device 200 may implement the processing device 140. For example... Figure 2 As shown, the computing device 200 may include a processor 210, a memory 220, an input / output (I / O) 230, and a communication port 240.

[0058] Processor 210 can execute computer instructions (program code) and perform the functions of processing device 140 according to the techniques described herein. The computer instructions may include routines, programs, objects, components, data structures, procedures, modules, and functions that perform the specific functions described herein. For example, processor 210 can process image data obtained from CT scanner 110, terminal 130, storage device 150, or any other component of imaging system 100. For example, processor 210 can preprocess and / or correct projected images. As another example, processor 210 can reconstruct a three-dimensional image based on multiple corrected projected images and store the three-dimensional image in storage device 150. In some embodiments, the processor 210 may include at least one hardware processor, such as a microcontroller, microprocessor, reduced instruction set computer (RISC), application-specific integrated circuit (ASIC), application-specific instruction set processor (ASIP), central processing unit (CPU), graphics processing unit (GPU), physical processor (PPU), microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA), advanced reduced instruction set system (ARM), programmable logic device (PLD), any circuit or processor capable of performing at least one function, or any combination thereof.

[0059] For illustrative purposes only, only one processor is described in computing device 200. However, it should be noted that computing device 200 of the present invention may also include multiple processors. Therefore, the operations and / or method steps performed by one processor of the present invention may also be performed jointly or individually by multiple processors. For example, if, in the present invention, the processor of computing device 200 performs steps A and B, it should be understood that steps A and B may also be performed jointly or individually by two different processors of computing device 200 (e.g., the first processor performs step A, the second processor performs step B, or the first and second processors jointly perform steps A and B).

[0060] The memory 220 can store data / information obtained from the CT scanner 110, terminal 130, storage device 150, or any other component of the imaging system 100. In some embodiments, the memory 220 may include a mass storage device, a removable memory, a volatile read-write memory, a read-only memory (ROM), or any combination thereof. For example, the mass storage device may include a hard disk, an optical disk, and a solid-state drive. The removable memory may include a flash drive, a floppy disk, an optical disk, a memory card, a compact disk, and a magnetic tape. The volatile read-write memory may include random access memory (RAM). The RAM may include dynamic RAM (DRAM), double data rate synchronous dynamic RAM (DDR SDRAM), static RAM (SRAM), thyristor RAM (T-RAM), and capacitorless RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), an optical disc ROM (CD-ROM), and a digital universal disk ROM, etc. In some embodiments, the memory 220 may store at least one program and / or instructions to perform the exemplary methods described in this invention. For example, the memory 220 may store a program (e.g., in the form of computer-executable instructions) for correcting a projected image for the processing device 140. As another example, the memory 220 may store a program (e.g., in the form of computer-executable instructions) for reconstructing a three-dimensional image based on a projected image for the processing device 140.

[0061] Input / output 230 can input or output signals, data, or information. In some embodiments, input / output 230 can interact with the processing device 140. In some embodiments, input / output 230 may include input devices and output devices. Exemplary input devices may include a keyboard, mouse, touchscreen, microphone, etc., or combinations thereof. Exemplary output devices may include display devices, speakers, printers, projectors, etc., or combinations thereof. Exemplary display devices may include liquid crystal displays (LCDs), light-emitting diode (LED) based displays, flat panel displays, curved screens, television equipment, cathode ray tubes (CRTs), etc., or combinations thereof.

[0062] Communication port 240 can be connected to a network (e.g., network 120) to facilitate data communication. Communication port 240 can establish a connection between the processing device 140 and the CT scanner 110, the terminal 130, or the storage device 150. The connection can be wired, wireless, or a combination of both. These connections enable data to be sent and received. Wired connections can include cables, fiber optic cables, telephone lines, etc., or any combination thereof. Wireless connections can include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobile networks (e.g., 3G, 4G, 5G), etc., or combinations thereof. In some embodiments, communication port 240 can be a standardized communication port such as RS232 and RS485. In some embodiments, communication port 240 can be a specially designed communication port. For example, communication port 240 can be designed according to the Medical Digital Imaging and Communication (DICOM) protocol.

[0063] Figure 3 This is a schematic diagram of exemplary hardware and / or software components of a mobile device 300 according to some embodiments of the present invention. The mobile device 300 can implement the terminal 130. For example... Figure 3As shown, the mobile device 300 may include a communication platform 310, a display 320, a graphics processing unit (GPU) 330, a central processing unit (CPU) 340, an input / output 350, memory 360, and a storage 390. In some embodiments, any other suitable components, including but not limited to a system bus or controller (not shown), may also be included in the mobile device 300. In some embodiments, a mobile operating system 370 (e.g., iOS, Android, Windows Phone) and at least one application 380 may be loaded from the storage 390 into the memory 360 for execution by the CPU 340. The application 380 may include a browser or any other suitable mobile application for receiving and presenting image processing-related information or other information from the processing device 140. User interaction with the information stream can be achieved through the input / output 350 and provided to the processing device 140 and / or other components of the imaging system 100 via the network 120.

[0064] To implement the various modules, units, and functions described in this invention, a computer hardware platform can be used as the hardware platform for at least one element described in this invention. The hardware components, operating system, and programming language of such computers are essentially conventional, and it is assumed that those skilled in the art are fully familiar with these technologies to adapt them to the corrected projected images described in this invention. A computer with user interface elements can be used to implement a personal computer (PC) or other types of workstation or terminal device, but if properly programmed, the computer can also act as a server. It is assured that those skilled in the art are familiar with the structure, programming, and general operation of such computer devices. Therefore, the accompanying drawings should be self-evident.

[0065] Figure 4 This is a schematic diagram of a processing device 140 according to some embodiments of the present invention. The processing device 140 can be used in, for example... Figure 2 The computing device 200 shown (e.g., processor 210) or such Figure 3 The processing device 140 is implemented on the CPU 340 shown. The processing device 140 may include a projected image acquisition module 410, a reference image generation module 420, a correction module 430, and a reconstruction module 440.

[0066] The projection image acquisition module 410 can be used to acquire information related to the imaging system 100. This information may include scan data (e.g., one or more gantry angles, radiation dose, machine energy spectrum, detector energy response curve, parameters related to the collimator or scanning stage, or other parameters), image data (e.g., one or more projection images), etc. For example, the projection image acquisition module 410 can acquire multiple projection images of the scanned object. These multiple projection images can be generated based on scan data acquired by the CT scanner (e.g., CT scanner 110) at multiple gantry angles. Each projection image may correspond to one gantry angle. In some embodiments, the gantry angle may be determined by a line connecting the rotation center of the radiation source 113 and the gantry 111, and a reference coordinate system (e.g., such as...). Figure 1 The angle formed by the X-axis and Y-axis (as shown). By way of example only, the CT scanner 110 can perform a scan of the object being scanned by irradiating it with X-rays. During the scan, the radiation source 113 and detector 112 can rotate with the gantry 111 around the Z-axis to scan the object at different gantry angles. This allows for the acquisition of multiple sets of scan data corresponding to multiple gantry angles. The processing device 140 and / or the CT scanner 110 can generate multiple projected images corresponding to the gantry angles based on the multiple sets of scan data and transmit the projected images to a storage device (e.g., storage device 150) for storage. The projected image acquisition module 410 can access the storage device and obtain the projected images.

[0067] The projection image acquisition module 410 can also be used to preprocess the projection image. For example, the projection image acquisition module 410 can preprocess the projection image corresponding to the gantry angle. The preprocessing of the projection image may include bad pixel correction, dark field correction, gain correction, geometric correction, beam hardening correction, scattering correction, etc., or any combination thereof.

[0068] The reference image generation module 420 can be used to generate reference images. For example, the reference image generation module 420 can acquire a reconstructed first three-dimensional image of the scanned object and generate multiple reference images based on the first three-dimensional image. In some embodiments, reference images can be generated according to the correction requirements, such that each of the generated multiple reference images corresponds to at least one of the multiple projection images acquired by the projection image acquisition module 410. This ensures that all generated reference images are useful, avoiding waste of resources. It should be noted that the correspondence between the projection image and the reference image means that the reference image and the projection image have pixels corresponding to the same spatial point. In some embodiments, the first three-dimensional image can be stored in the storage device 150, and the reference image generation module 420 can acquire the first three-dimensional image through the network 120 and / or directly from the storage device 150. Preferably, the first three-dimensional image can be a file conforming to the DICOM protocol. In some embodiments, the first three-dimensional image can be obtained by reconstructing medical images acquired by medical imaging scanning equipment such as CT (including conventional CT, cone-beam CT, spiral CT, multi-slice CT, etc.), Magnetic Resonance (MR), PET-CT, etc. In some embodiments, when the first three-dimensional image is a non-CT image, the reference image generation module 420 can also convert it into a CT image. For example, when the first three-dimensional image is an MR image, the reference image generation module 420 can convert the pixel values ​​in the MR image into CT values ​​and generate a CT image based on these CT values. Details regarding the generation of multiple reference images from the first three-dimensional image can be found elsewhere in the invention (e.g., Figure 5 , Figure 6 , Figure 7 , Figure 8 (and related descriptions).

[0069] The correction module 430 can be used to correct a projected image. For example, it can correct the projected image based on the grayscale relationship between the projected image and its corresponding reference image. In some embodiments, the grayscale relationship can be difference, sum, ratio, etc., or any combination thereof. In some embodiments, the correction module 430 can also generate a correction table based on the grayscale relationship and use the correction table to correct the projected image. In some embodiments, the correction table can be stored in memory 220 or memory 390. Details regarding the correction of the projected image based on the grayscale relationship between the projected image and its corresponding reference image can be found elsewhere in the invention (e.g., Figure 5 , Figure 7 , Figure 8 (and related descriptions).

[0070] The reconstruction module 440 can be used to reconstruct images. For example, the reconstruction module 440 can reconstruct a CT image of an object based on multiple (corrected) projected images corresponding to multiple gantry angles. In some embodiments, the reconstruction module 440 can reconstruct images according to reconstruction techniques. Exemplary reconstruction techniques may include, but are not limited to, algebraic reconstruction technique (ART), simultaneous algebraic reconstruction technique (SART), filtered back projection (FBP) technique, FDK reconstruction technique, etc., or any combination thereof.

[0071] The modules in processing device 140 can be connected or communicate with each other via wired or wireless means. Wired connections may include metal cables, optical fibers, hybrid cables, or any combination thereof. Wireless connections may include local area networks (LANs), wide area networks (WANs), Bluetooth, ZigBee, near field communication (NFC), or any combination thereof.

[0072] It should be noted that the above description is for illustrative purposes only and is not intended to limit the scope of protection of this invention. Those skilled in the art can make various changes and modifications under the guidance of this invention. Nevertheless, these changes and modifications do not depart from the scope of protection of this invention.

[0073] In some embodiments, two or more modules can be combined into one module, and any module can be divided into two or more units. For example, the projection image acquisition module 410 and the reference image generation module 420 can be integrated into one module, which performs the functions of the projection image acquisition module 410 and the reference image generation module 420. As another example, the reference image generation module 420 can be divided into two units. The first unit can be used to acquire a first three-dimensional image of the scanned object. The second unit can be used to generate multiple reference images based on the first three-dimensional image.

[0074] In some embodiments, the processing device 140 may include one or more additional modules. For example, the processing device 140 may include a post-processing module (not shown). The post-processing module may be used to post-process the corrected projected image, for example, to perform beam hardening, scattering correction, image enhancement, etc., on the corrected projected image. As another example, the processing device 140 may include a storage module (not shown). The storage module may be used to store data generated by any component of the processing device 140 during any process of execution.

[0075] Figure 5 This is a basic flowchart of a projection image correction method according to some embodiments of the present invention. The correction method 500 can be used in, for example... Figure 1 This is implemented in the imaging system 100 shown. For example, the correction method 500 may be stored in the storage device 150 and / or memory 220 in the form of instructions (e.g., application), and processed by the processing device 140 (e.g., such as...). Figure 2 The processor 210 shown, or as Figure 4 One or more modules in the processing device 140 shown are invoked and / or executed. The operation of the correction method shown below is exemplary. In some embodiments, the correction method 500 may be accomplished by at least one additional operation not mentioned and / or at least one operation not discussed. Additionally, Figure 5 The order of operations of the correction method 500 shown is not intended to limit the invention. For example, steps 510 and 520 may be performed simultaneously. Or, for example, step 520 may be performed before step 510.

[0076] In step 510, the projection image acquisition module 410 can acquire multiple projection images of the scanned object. The scanned object can be a living organism (e.g., a patient, animal, organ, or tissue) or a non-living organism (e.g., a phantom).

[0077] Multiple projection images can be generated based on scan data acquired by a CT scanner (e.g., CT scanner 110) at multiple gantry angles. Each projection image can correspond to a gantry angle. For example, CT scanner 110 can perform a scan on a subject by irradiating it with X-rays. During the scan, radiation source 113 and detector 112 can scan the subject at different gantry angles around a rotation axis with the gantry 111. The scan data of the subject can include multiple sets of data corresponding to multiple gantry angles. Processing device 140 and / or CT scanner 110 can generate multiple projection images corresponding to multiple gantry angles based on the multiple sets of data and transmit the projection images to a storage device (e.g., storage device 150) for storage. Projection image acquisition module 410 can access the storage device and obtain the projection images.

[0078] In some embodiments, the frame angle can be determined by a line connecting the rotation center of the radiation source 113 and the frame 111, and a reference coordinate system (e.g., ...). Figure 1The angle formed by the X-axis and Y-axis (as shown). For example, the gantry angle of the multiple projected images acquired in step 510 can be in the range of 0° to 360°. In some embodiments, when the gantry 111 rotates, the radiation source 113 can continuously emit X-rays to the scanned object. For example, the gantry angle can be in the range of 0° to 360°, and multiple sets of data corresponding to multiple gantry angles (e.g., 1200 or 2400 gantry angles) can be collected by the detector 112, and correspondingly, 1200 projected images corresponding to 1200 gantry angles or 2400 projected images corresponding to 2400 gantry angles can be generated. Optionally, the radiation source 113 can emit X-rays to the scanned object discontinuously. For example, the gantry angle can be in the range of 0° to 360°, and the radiation source 113 can emit X-rays to the scanned object every 1° change in the gantry angle, and correspondingly, 360 projected images can be generated. For example, the gantry angle can be in the range of 0° to 360°, and the radiation source 113 can emit X-rays to the scanned object every 0.5° change in the gantry angle, thereby generating 720 projected images.

[0079] In some embodiments, the gantry angle of the projected image can be obtained from one or more components of the imaging system 100 (e.g., a gantry angle encoder). Alternatively, the gantry angle of the projected image can be determined by the processing device 140 based on data analysis of the gantry angles of other projected images. By way of example only, the processing device 140 can determine the gantry angle of the projected image using an interpolation algorithm based on multiple other projected images corresponding to other gantry angles.

[0080] In some embodiments, multiple projected images may include multiple 2D images. In some embodiments, a single projected image may include multiple pixels. A pixel may have a pixel value, such as a grayscale value, a brightness value, or any combination thereof. The pixel value (e.g., grayscale value) corresponding to a spatial point of the scanned object may have a linear relationship with the radiation (or “radiation dose”) delivered to that spatial point of the scanned object. When the gantry 111 rotates, if the spatial point of the scanned object receives the same or substantially the same radiation dose, the pixels corresponding to that spatial point in multiple projected images may also have the same or substantially the same pixel value. However, when the gantry 111 rotates, the radiation dose delivered to the scanned object may change, and the pixel values ​​of pixels corresponding to the same spatial point in different projected images may be different. Multiple projected images corresponding to different gantry angles need to be corrected and / or normalized to reduce the impact of non-uniform radiation doses at different gantry angles.

[0081] In some embodiments, in step 510, the projection image acquisition module 410 may further preprocess the acquired multiple projection images. Preprocessing of the projection images may include bad pixel correction, dark field correction, gain correction, geometric correction, beam hardening correction, scattering correction, etc., or any combination thereof. Scattering correction may be performed based on a scattering correction algorithm. Exemplary scattering correction algorithms may include convolution algorithms, model evaluation algorithms, deconvolution algorithms, Monte Carlo simulation algorithms, single-scattering simulation algorithms, dual-energy window techniques, etc., or any combination thereof.

[0082] In some embodiments, the projection image acquisition module 410 may preprocess the projection image corresponding to a gantry angle based on the radiation dose delivered to the object at that gantry angle. The radiation dose delivered to the object at that gantry angle may be a planned dose or a dose measured by the ionization chamber when the CT scanner 110 scans the object at that gantry angle. For example, the projection image acquisition module 410 may determine the X-ray intensity passing through the object based on the radiation dose delivered to the object at that gantry angle, and the projection image acquisition module 410 may perform scattering correction on the projection image based on the X-ray intensity.

[0083] In step 520, the reference image generation module 420 can acquire a reconstructed first three-dimensional image of the scanned object and generate multiple reference images based on the first three-dimensional image. Each of the generated multiple reference images corresponds to at least one of the multiple projection images acquired by the projection image acquisition module 410. Preferably, the first three-dimensional image is a three-dimensional image reconstructed based on the most recently acquired scan data of the scanned object. It can be understood that "most recently" here is a relative concept, which can vary depending on the rate of change of the scanned object. For a slowly changing scanned object, "most recently" can be one year, six months, one month, fifteen days, five days, etc. For a rapidly changing scanned object, "most recently" can be one month, fifteen days, ten days, five days, three days, one day, twelve hours, eight hours, four hours, etc. In some embodiments, the first three-dimensional image can be obtained by reconstructing a medical image acquired by medical imaging scanning equipment such as CT (including conventional CT, cone-beam CT, spiral CT, multi-slice CT, etc.), Magnetic Resonance (MR), PET-CT, etc. In some embodiments, when the first three-dimensional image is a non-CT image, the reference image generation module 420 can also convert it into a CT image. For example, when the first three-dimensional image is an MR image, the reference image generation module 420 can convert the pixel values ​​in the MR image into CT values ​​and generate a CT image based on the CT values.

[0084] In some embodiments, the reference image generation module 420 can generate a reference image containing scattering amounts. In some more specific embodiments, the method by which the reference image generation module 420 generates multiple reference images containing scattering amounts based on the first three-dimensional image can include analytical methods, Monte Carlo methods, methods for solving the Boltzmann equations, convolution stacking algorithms, etc., or any combination thereof. For example, the analytical method can project the first three-dimensional image orthographically at different angles to obtain multiple first projected images, and calculate multiple first scattering images corresponding to the first projected images, and then synthesize the multiple first projected images and the multiple first scattering images into multiple reference images. As another example, the Monte Carlo method can directly generate multiple reference images containing main rays and scattered rays using Monte Carlo software based on input geometric information (e.g., including gantry angle), machine energy spectrum, detector energy response curve, etc. As yet another example, the method for solving the Boltzmann equations can directly generate multiple reference images containing main rays and scattered rays using software for solving the Boltzmann equations based on input geometric information (e.g., including gantry angle), machine energy spectrum, detector energy response curve, etc.

[0085] Figure 6 This is a schematic diagram of a reference image generator 600 based on an analytical method according to some embodiments of the present invention. The reference image generator 600 mainly includes an orthographic projection module 610, a scattering evaluation module 620, and an image synthesis module 630.

[0086] The orthographic projection module 610 receives machine parameters and a first three-dimensional image, and generates a first projected image based on the machine parameters and the first three-dimensional image. The machine parameters may include geometric information (e.g., gantry angle), machine energy spectrum, detector energy response curve, etc., or any combination thereof. In some embodiments, the orthographic projection module 610 can generate the first projected image by performing the following steps:

[0087] The energy spectrum of the machine is divided into multiple compartments;

[0088] Based on the conversion relationship between CT values ​​and electron density, such as a conversion table, convert CT images into electron density images;

[0089] Based on electron density images, CT images are divided into several known materials (e.g., air, lung tissue, soft tissue, bone, etc.).

[0090] The attenuation coefficient corresponding to the CT image is calculated based on the electron density image, material classification, and attenuation coefficient conversion table.

[0091] Under each energy sub-bin, the first three-dimensional image is orthographically projected from different angles based on the attenuation coefficient; and

[0092] Based on the detector's energy response curve, multiple first projection images are obtained by synthesizing orthographic projection images at different energies.

[0093] The scattering evaluation module 620 receives machine parameters and a first three-dimensional image, and generates a first scattering image based on the machine parameters and the first three-dimensional image. In some embodiments, the scattering evaluation module 620 may use convolution to calculate the first scattering image corresponding to the first projected image. It is understood that the first scattering image and the first projected image may be in a one-to-one correspondence, or one first scattering image may correspond to multiple first projected images, or multiple first scattering images may correspond to one first projected image.

[0094] The image synthesis module 630 is used to receive multiple first projection images and one or more first scattering images, and to synthesize the multiple first projection images and one or more first scattering images into multiple reference images.

[0095] It is understood that the modules in the reference image generator 600 can be connected or communicate with each other via wired or wireless means. Wired connections may include metal cables, optical fibers, hybrid cables, etc., or any combination thereof. Wireless connections may include wireless local area networks (WLANs), wireless wide area networks (WWANs), Bluetooth, ZigBee, near field communication (NFC), etc., or any combination thereof. In some embodiments, two or more modules may be combined into one module, and any module may be divided into two or more units. In some embodiments, the reference image generator 600 may include one or more additional modules. For example, the reference image generator 600 may include a storage module (not shown). The storage module can be used to store data generated by any component of the reference image generator 600 during any process of execution. It is understood that the modules in the reference image generator 600 can also use the storage module for data sharing.

[0096] It is understood that when the projection image acquisition module 410 performs scattering correction on the projection image in step 510, the influence of scattering lines can be ignored when generating the reference image in step 520, that is, a reference image without scattering can be generated in step 520.

[0097] It is understood that step 520 includes two sub-steps: "acquiring the reconstructed first 3D image of the scanned object" and "generating multiple reference images based on the first 3D image." These two sub-steps are grouped into one step merely for ease of description; the present invention does not limit the operational order of these two sub-steps with other steps. For example, step 510 can be executed between these two sub-steps; that is, "acquiring the reconstructed first 3D image of the scanned object" can be executed first, followed by the acquisition of multiple projected images of the scanned object in step 510, and then "generating multiple reference images based on the first 3D image" can be executed.

[0098] In step 530, the correction module 430 can correct the projected image based on the grayscale relationship between the projected image and its corresponding reference image. The projected image can be the original projected image or a preprocessed projected image. Preprocessing of the projected image can include bad pixel correction, dark field correction, gain correction, geometric correction, beam hardening correction, scattering correction, etc., or any combination thereof. In some embodiments, the correction module 430 can traverse all projected images, find the projected image corresponding to the reference image, select the same region of interest (ROI) in the projected image and the reference image according to the positional relationship between the corresponding projected image and the reference image, calculate the grayscale relationship, generate correction coefficients according to the grayscale relationship, and finally correct the projected image according to the correction coefficients to normalize the multiple projected images acquired by the projected image acquisition module 410. In some embodiments, the correction module 430 can traverse all reference images, find the reference image corresponding to the projected image, select the same Region of Interest (ROI) in the projected image and reference image according to the positional relationship between the corresponding projected image and reference image, calculate the grayscale relationship, generate correction coefficients based on the grayscale relationship, and finally correct the projected image according to the correction coefficients to normalize the multiple projected images acquired by the projected image acquisition module 410. In some embodiments, the positional relationship between the corresponding projected image and reference image can be obtained through image registration. For example, a two-dimensional image registration algorithm or a three-dimensional image registration algorithm can be used. In some embodiments, the grayscale relationship can include grayscale distribution differences. Grayscale distribution differences can be the difference, sum, ratio, etc., of the grayscale values ​​of corresponding pixels in the projected image and reference image, or any combination thereof. In some embodiments, the correction module 430 can generate correction coefficients based on the grayscale distribution differences according to certain rules. These rules can be, for example, taking the average value, taking the median value, etc. For example, the average value of the grayscale distribution differences of all pixels in the region of interest can be taken as the correction coefficient. Or, the median value of the grayscale distribution differences of all pixels in the region of interest can be taken as the correction coefficient. In some embodiments, correcting the projected image according to the correction coefficient may involve multiplying or dividing the grayscale value of a pixel in the projected image by the correction coefficient, or adding or subtracting the correction coefficient from the grayscale value of a pixel in the projected image. In some embodiments, the correction module 430 may also generate a correction table based on the grayscale relationship and use the correction table to correct the projected image.

[0099] In step 540, the reconstruction module 440 can reconstruct a three-dimensional image of the scanned object based on the corrected multiple projected images. In some embodiments, the reconstruction module 440 can reconstruct the three-dimensional image according to reconstruction techniques. Exemplary reconstruction techniques may include, but are not limited to, algebraic reconstruction technique (ART), simultaneous algebraic reconstruction technique (SART), filtered back projection (FBP) technique, FDK reconstruction technique, etc., or any combination thereof. The three-dimensional image may include multiple pixels, and the pixel values ​​of the multiple pixels may represent the attenuation coefficients of different parts of the object.

[0100] In some embodiments, beam hardening correction and / or scattering correction are not performed on the acquired multiple projection images in step 510, but beam hardening correction and / or scattering correction are performed on the corrected multiple projection images before three-dimensional image reconstruction in step 540.

[0101] It should be noted that the above description of the correction method 500 is for illustrative purposes only and is not intended to limit the scope of the invention. Various changes and modifications can be made by those skilled in the art under the guidance of this invention. Nevertheless, these changes and modifications do not depart from the scope of the invention.

[0102] In some embodiments, the correction method 500 may generate a reference image in step 520 and perform two-dimensional image registration on the corresponding projected image and the reference image in step 530 to obtain the positional relationship between the corresponding projected image and the reference image.

[0103] Figure 7 This is a schematic diagram of the reference image generation and correction steps in some embodiments of the present invention.

[0104] In step 520, the reference image generation module 420 can acquire a reconstructed first 3D image of the scanned object and generate multiple reference images based on the first 3D image. Each of the generated multiple reference images corresponds to at least one of the multiple projection images acquired by the projection image acquisition module 410. In some embodiments, the method by which the reference image generation module 420 generates multiple reference images based on the first 3D image may include analytical methods, Monte Carlo methods, methods for solving the Boltzmann equations, convolution stacking algorithms, or any combination thereof.

[0105] Step 530 (correction step) may include steps 531, 532, 533 and 534.

[0106] In step 531, the correction module 430 can perform image registration on the corresponding projected image and reference image to obtain the positional relationship between the projected image and its corresponding reference image. Here, the aforementioned image registration is two-dimensional image registration. Two-dimensional image registration can include rigid image registration methods and / or non-rigid image registration methods. During the registration process, the registration method can be executed iteratively. The registration method can include a series of coarse registration, fine registration, and ultra-fine registration.

[0107] In step 532, the correction module 430 can select the same region of interest (ROI) in the projected image and its corresponding reference image based on the positional relationship between them to calculate the grayscale distribution difference between them. The ROI can be a region in the projected image or reference image relating to a specific organ or tissue, such as the lung region or rib region. In some embodiments, the grayscale distribution difference can be the difference, sum, ratio, or any combination thereof relating to the grayscale values ​​of corresponding pixels in the projected image and reference image. Preferably, the grayscale distribution difference can be the ratio of the grayscale values ​​of corresponding pixels in the projected image and reference image.

[0108] In step 533, the correction module 430 can generate correction coefficients based on the grayscale distribution differences calculated in step 532. In some embodiments, the correction module 430 can generate correction coefficients based on the grayscale distribution differences according to certain rules. These rules could be, for example, taking the average value, taking the median value, etc. For instance, the average grayscale distribution differences of all pixels within the region of interest can be used as the correction coefficient. Alternatively, the median grayscale distribution differences of all pixels within the region of interest can be used as the correction coefficient.

[0109] In step 534, the correction module 430 can correct the projected image according to the correction coefficient. Preferably, the correction module 430 corrects each projected image. The projected image can be the original projected image or a pre-processed projected image. Pre-processing of the projected image can include bad pixel correction, dark field correction, gain correction, geometric correction, beam hardening correction, scattering correction, etc., or any combination thereof. In some embodiments, correcting the projected image according to the correction coefficient can be done by multiplying or dividing the gray value of the pixels in the projected image by the correction coefficient, or by adding or subtracting the correction coefficient from the gray value of the pixels in the projected image.

[0110] Figure 8 This is a schematic diagram illustrating the reference image generation and correction steps of some other embodiments of the present invention. Step 520 (reference image generation step) may include steps 521, 522, and 523, etc. Step 530 (correction step) may include steps 531', 532', and 531', etc.

[0111] In step 521, the reference image generation module 420 can acquire multiple projected images of the scanned object and reconstruct the scanned object based on the multiple projected images to obtain a second three-dimensional image. In some embodiments, the projected images can be original projected images or pre-processed projected images. Pre-processing of the projected images can include bad pixel correction, dark field correction, gain correction, geometric correction, beam hardening correction, scattering correction, etc., or any combination thereof. In some embodiments, the reconstruction module 440 can reconstruct the three-dimensional image according to reconstruction techniques. Exemplary reconstruction techniques may include, but are not limited to, algebraic reconstruction technique (ART), simultaneous algebraic reconstruction technique (SART), filtered back projection (FBP) technique, FDK reconstruction technique, etc., or any combination thereof. The three-dimensional image can include multiple pixels, and the pixel values ​​of the multiple pixels can represent the attenuation coefficients of different parts of the object.

[0112] In step 522, the reference image generation module 420 can acquire a first three-dimensional image of the scanned object and perform three-dimensional image registration on the first and second three-dimensional images to obtain the positional relationship between the first and second three-dimensional images. In some embodiments, three-dimensional image registration may include rigid image registration methods and / or non-rigid image registration methods. During the registration process, the registration methods can be executed iteratively. The registration methods may include a series of coarse registration, fine registration, and ultra-fine registration.

[0113] In step 523, the reference image generation module 420 can generate multiple reference images based on the positional relationship and the first 3D image. Each of the generated multiple reference images corresponds to at least one of the multiple projection images acquired by the projection image acquisition module 410. In some embodiments, the method by which the reference image generation module 420 generates multiple reference images based on the first 3D image may include analytical methods, Monte Carlo methods, methods for solving the Boltzmann equations, convolution stacking algorithms, or any combination thereof. It can be understood that since the first 3D image and the second 3D image have been registered in step 522, the pixels of the reference image generated in step 523 at the same spatial location point are already corresponding to those of the corresponding projection image, just as the reference image and its corresponding projection image are after 2D image registration.

[0114] In step 531', the correction module 430 can select the same region of interest (ROI) in both the projected image and its corresponding reference image to calculate the grayscale distribution difference between the projected image and its corresponding reference image. The region of interest can be a region in the projected image or reference image relating to a specific organ or tissue, such as the lung region or rib region. In some instances, the grayscale distribution difference can be the difference, sum, ratio, or any combination thereof relating to the grayscale values ​​of corresponding pixels in the projected image and the reference image. Preferably, the grayscale distribution difference can be the ratio of the grayscale values ​​of corresponding pixels in the projected image and the reference image.

[0115] In step 532', the correction module 430 can generate correction coefficients based on the grayscale distribution differences calculated in step 531'. In some embodiments, the correction module 430 can generate correction coefficients based on the grayscale distribution differences according to certain rules. These rules could be, for example, taking the average value, taking the median value, etc. For instance, the average grayscale distribution differences of all pixels within the region of interest can be used as the correction coefficient. Alternatively, the median grayscale distribution differences of all pixels within the region of interest can be used as the correction coefficient.

[0116] In step 533', the correction module 430 can correct the projected image according to the correction coefficient. The projected image can be the original projected image or a pre-processed projected image. Pre-processing of the projected image can include bad pixel correction, dark field correction, gain correction, geometric correction, beam hardening correction, scattering correction, etc., or any combination thereof. In some embodiments, correcting the projected image according to the correction coefficient can be done by multiplying or dividing the gray value of the pixels in the projected image by the correction coefficient, or by adding or subtracting the correction coefficient from the gray value of the pixels in the projected image.

[0117] In this embodiment, since three-dimensional image registration is performed first, and then a reference image is generated based on the registration result, the generated reference image and its corresponding projection image have high registration accuracy. Therefore, this embodiment has higher correction accuracy than the embodiment that performs two-dimensional image registration.

[0118] In the above embodiments, the reference image is generated based on a three-dimensional image. Those skilled in the art will understand that the reference image can also be a projected image generated by an imaging device.

[0119] In some embodiments, a projected image generated by one imaging device can be used as the projected image to be corrected, while a projected image generated by another imaging device can be used as a reference image. For example, a projected image obtained by directly projecting a high-quality cone-beam CT scan onto the patient can be used as the reference image.

[0120] In some embodiments, when correcting a projected image corresponding to a certain rack angle, a reference image may also be formed using the other multiple projected images corresponding to that rack angle. Figure 9 This is a basic flowchart of a projection image correction method 700 according to other embodiments of the present invention. The correction method 700 can be used in, for example... Figure 1 This is implemented in the imaging system 100 shown. For example, the correction method 700 may be stored in the storage device 150 and / or memory 220 in the form of instructions (e.g., application), and processed by the processing device 140 (e.g., such as...). Figure 2 The processor 210 shown, or as Figure 4 One or more modules in the processing device 140 shown are invoked and / or executed. (See reference) Figure 9 As shown, the projection image correction method 700 may include:

[0121] Step 710: Acquire multiple projected images of the scanned object. These multiple projected images are generated based on scanning data acquired by the imaging device at multiple gantry angles, with each gantry angle corresponding to at least two projected images. In some embodiments, scattering correction may also be performed on the multiple projected images in this step.

[0122] Step 720: Select a portion of the projection images from at least two projection images corresponding to each rack corner to form the projection image to be corrected, and at least a portion of the remaining projection images corresponding to each rack corner to form the reference image.

[0123] Step 730: Correct each projection image to be corrected based on the grayscale relationship between the projection image to be corrected and the corresponding reference image. In some embodiments, this step determines the grayscale relationship based on the same region of interest in the projection image to be corrected and its corresponding reference image.

[0124] In some embodiments, step 730 may include the following sub-steps:

[0125] The difference in grayscale distribution is calculated by selecting the same region of interest in the projected image to be corrected and the reference image;

[0126] Correction coefficients are generated based on differences in grayscale distribution; and

[0127] The projected image is corrected based on the correction factor.

[0128] The difference in grayscale distribution can be determined by calculating the grayscale ratio of the same region of interest in the projected image to be corrected and the reference image.

[0129] In some embodiments, the image correction method 700 may further include step 740: performing three-dimensional image reconstruction on the corrected projected image.

[0130] Figure 10 This is a basic flowchart of a projection image correction method 800 according to some embodiments of the present invention. The projection image correction method 800 is a summary of the above embodiments and includes:

[0131] 810: Acquire multiple first projection images of the scanned object;

[0132] 820: Acquire a plurality of reference images, wherein each reference image corresponds to at least one of a plurality of first projected images; and

[0133] 830: Correct the first projected image based on the grayscale relationship between the first projected image and its corresponding reference image.

[0134] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0135] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0136] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0137] A computer-readable signal medium may contain a propagated data signal containing computer program encoding, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program encoding located on the computer-readable signal medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0138] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0139] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention 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; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely through software solutions, such as installing the described system on existing servers or mobile devices.

[0140] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0141] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples by terms such as "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary ± as described. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0142] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other modifications may also fall within the scope of this application. Therefore, alternative configurations of the embodiments of this application are considered as examples and not limitations, and are regarded as consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly described and illustrated in this application.

Claims

1. A method for correcting a projected image, comprising: a. Acquire multiple first projection images of the scanned object; b. Acquire a pre-stored first three-dimensional image of the scanned object, the first three-dimensional image being a file conforming to the DICOM protocol; generate a plurality of reference images based on the first three-dimensional image, wherein the reference images contain scattering data, and each reference image corresponds to at least one of the plurality of first projection images; and c. Correct the first projected image based on the grayscale relationship between the first projected image and its corresponding reference image; Step b includes: b1. Reconstruct the scanned object based on the plurality of first projection images to obtain a second three-dimensional image; b2. Register the first 3D image and the second 3D image to obtain the positional relationship between the first 3D image and the second 3D image; and b3. Generate the plurality of reference images based on the positional relationship and the first three-dimensional image.

2. The correction method of claim 1, wherein In step c, the grayscale relationship is determined based on the same region of interest in the first projected image and its corresponding reference image.

3. The correction method according to claim 1, characterized in that, Step c includes: c21. Select the same region of interest in the first projected image and the reference image to calculate the difference in grayscale distribution; c22. Generate correction coefficients based on the aforementioned grayscale distribution differences; and c23. Correct the first projected image according to the correction coefficient.

4. The correction method according to claim 3, characterized in that, In step c21, the grayscale distribution difference is determined by calculating the grayscale ratio of the same region of interest in the first projected image and the reference image.

5. The correction method according to claim 1, characterized in that, Step a further includes performing one or more of the following on the plurality of first projected images: bad pixel correction, dark field correction, gain correction, and geometric correction.

6. The correction method according to claim 1, characterized in that, If the first 3D image is a non-CT image, the step of generating the plurality of reference images based on the first 3D image includes: Convert the first 3D image into a CT image; and The plurality of reference images are generated based on the CT images.

7. The correction method according to claim 1 or 6, characterized in that, The multiple reference images are obtained through one or more of the following methods: analytical method, Monte Carlo method, method of solving Boltzmann equations, and convolution stacking algorithm.

8. The correction method according to claim 1, characterized in that, Also includes: d. Reconstruct the corrected plurality of first projection images to obtain a three-dimensional image of the scanned object.

9. A device for correcting a projected image, comprising: The projection image acquisition module is suitable for acquiring multiple first projection images of the scanned object; A reference image generation module is adapted to acquire a pre-stored first three-dimensional image of the scanned object, the first three-dimensional image being a file conforming to the DICOM protocol, and to generate multiple reference images based on the first three-dimensional image, wherein the reference images contain scattering data, and each reference image corresponds to at least one of the multiple first projection images; and The correction module is adapted to correct the first projected image based on the grayscale relationship between the first projected image and its corresponding reference image; The reference image generation module is used to acquire multiple projected images of the scanned object and reconstruct the scanned object based on the multiple projected images to obtain a second three-dimensional image. Perform 3D image registration on the first 3D image and the second 3D image to obtain the positional relationship between the first 3D image and the second 3D image; The plurality of reference images are generated based on the positional relationships and the first three-dimensional image.

10. A device for correcting a projected image, comprising: Memory is used to store instructions that can be executed by the processor; A processor for executing the instructions to implement the method as described in any one of claims 1-8.

11. A computer-readable storage medium having stored thereon computer instructions, wherein when the computer instructions are executed by a processor, the method as described in any one of claims 1-8 is performed.

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