Patent Description:
To provide a clear view of soft tissue, hard tissue sometimes needs to be removed from a CT image. However, the traditional way for hard tissue removal and hardening beam artifact correction consumes much time when two or more different CT images having different reconstruction parameters need to be generated and corrected. The present disclosure provides an efficient method and system for correcting artifact due to hard tissue in a CT image.

In a first aspect of the present disclosure, a system for CT image reconstruction according to claim <NUM> is provided.

In some embodiments, a first image thickness of the first bone information image may be greater than an image thickness of the reference image, a second image thickness of the second bone information image may be greater than the image thickness of the reference image, and the first image thickness may be different from the second image thickness.

In some embodiments, a first image increment of the first bone information image may be greater than an image increment of the reference image, a second image increment of the second bone information image may be greater than the image increment of the reference image, and the first image increment may be different from the second image increment.

In some embodiments, to generate the first bone information image, the at least one processor may stack one or more reference images based on an image thickness of the first bone information image and an image thickness of the reference image.

In some embodiments, to generate the second bone information image, the at least one processor may stack one or more reference images based on an image thickness of the second bone information image and an image thickness of the reference image.

In some embodiments, an image thickness of the first full quality image and an image thickness of the second full quality image may be different.

In some embodiments, an image increment of the first full quality image and an image increment of the second full quality image may be different.

In some embodiments, the at least one processor may output the first bone information image or the second bone information image to a user.

In a second aspect of the present disclosure, a method for CT image reconstruction according to claim <NUM> is provided.

In some embodiments, to generate the first bone information image, one or more reference images may be stacked based on an image thickness of the first bone information image and an image thickness of the reference image.

In some embodiments, to generate the second bone information image, one or more reference images may be stacked based on an image thickness of the second bone information image and an image thickness of the reference image.

In a third aspect of the present disclosure, a non-transitory computer readable medium according to claim <NUM> is provided.

In a fifth aspect of the present disclosure, a system for CT image reconstruction according to claim <NUM> is provided.

In some embodiments, a first image thickness of the first bone information image may be greater than an image thickness of the reference image, a second image thickness of the second bone information image may be greater than the image thickness of the reference image, and the first image thickness is different from the second image thickness.

In some embodiments, a first image increment of the first bone information image may be greater than an image increment of the reference image, a second image increment of the second bone information image is greater than the image increment of the reference image, and the first image increment is different from the second image increment.

In some embodiments, to generate the first bone information image, the correction image generation unit may be further configured to stack one or more reference images based on an image thickness of the first bone information image and an image thickness of the reference image.

In some embodiments, to generate the second bone information image, the correction image generation unit may be further configured to stack one or more reference images based on an image thickness of the second bone information image and an image thickness of the reference image.

In some embodiments, the correction image generation unit may be further configured to output the first bone information image or the second bone information image to a user.

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure. However, it should be apparent to those skilled in the art that the present disclosure may be practiced without such details. In other instances, well known methods, procedures, systems, components, and/or circuitry have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present disclosure. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown, but to be accorded the widest scope consistent with the claims.

It will be understood that the term "system," "unit," "module," and/or "block" used herein are one method to distinguish different components, elements, parts, section or assembly of different level in ascending order. However, the terms may be displaced by other expression if they achieve the same purpose.

Generally, the word "module," "unit," or "block," as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions. A module, a unit, or a block described herein may be implemented as software and/or hardware and may be stored in any type of non-transitory computer-readable medium or other storage device. In some embodiments, a software module/unit/block may be compiled and linked into an executable program. It will be appreciated that software modules can be callable from other modules/units/blocks or from themselves, and/or may be invoked in response to detected events or interrupts. Software modules/units/blocks configured for execution on computing devices (e.g., processor <NUM> as illustrated in <FIG>) may be provided on a computer readable medium, such as a compact disc, a digital video disc, a flash drive, a magnetic disc, or any other tangible medium, or as a digital download (and can be originally stored in a compressed or installable format that needs installation, decompression, or decryption prior to execution). Such software code may be stored, partially or fully, on a storage device of the executing computing device, for execution by the computing device. Software instructions may be embedded in a firmware, such as an EPROM. It will be further appreciated that hardware modules/units/blocks may be included of connected logic components, such as gates and flip-flops, and/or can be included of programmable units, such as programmable gate arrays or processors. The modules/units/blocks or computing device functionality described herein may be implemented as software modules/units/blocks, but may be represented in hardware or firmware. In general, the modules/units/blocks described herein refer to logical modules/units/blocks that may be combined with other modules/units/blocks or divided into sub-modules/sub-units/sub-blocks despite their physical organization or storage.

It will be understood that when a unit, engine, module or block is referred to as being "on," "connected to," or "coupled to," another unit, engine, module, or block, it may be directly on, connected or coupled to, or communicate with the other unit, engine, module, or block, or an intervening unit, engine, module, or block may be present, unless the context clearly indicates otherwise.

Provided herein are systems and components for non-invasive imaging, such as for disease diagnosis or research purposes. In some embodiments, the imaging system may be a computed tomography (CT) system, an emission computed tomography (ECT) system, a magnetic resonance imaging (MRI) system, an ultrasonography system, an X-ray photography system, a positron emission tomography (PET) system, or the like, or any combination thereof.

The following description is provided to help better understanding CT image reconstruction methods or systems. The term "image" used in this disclosure may refer to a 2D image, a 3D image, a 4D image, or any related image data (e.g., CT data, projection data corresponding to the CT data). This is not intended to limit the scope the present disclosure. For persons having ordinary skills in the art, a certain amount of variations, changes, and/or modifications may be deducted under guidance of the present disclosure. Those variations, changes, and/or modifications do not depart from the scope of the present disclosure.

An aspect of the present disclosure relates to systems and methods for generating and correcting different CT images based on a raw data set. According to the present disclosure, a set of reference images may be generated, and bone information images of different thicknesses and/or image increments may be generated based on the set of reference images. Another aspect of the present disclosure relates to systems and methods for identifying abnormalities of CT systems. According to the present disclosure, abnormalities of a CT system may be identified by determining difference between reference images generated from different raw data sets.

<FIG> and <FIG> are schematic diagram illustrating an exemplary CT system according to some embodiments of the present disclosure. The CT system may include a CT scanner <NUM>, a network <NUM>, a terminal <NUM>, a processing engine <NUM>, and a storage <NUM>. The connection between the components in the CT system <NUM> may be variable. Merely by way of example, as illustrated in <FIG>, the CT scanner <NUM> may be connected to the processing engine <NUM> through the network <NUM>. As another example, as illustrated in <FIG>, the CT scanner <NUM> may be connected to the processing engine <NUM> directly.

The CT scanner <NUM> may include a gantry <NUM>, a detector <NUM>, a detecting region <NUM>, a subject table <NUM>, and a radioactive scanning source <NUM>. The gantry <NUM> may support the detector <NUM> and the radioactive scanning source <NUM>. A subject may be placed on the subject table <NUM> to be scanned. The radioactive scanning source <NUM> may emit radioactive rays to the subject. The detector <NUM> may detect radiation events (e.g., gamma photons) emitted from the detecting region <NUM>. In some embodiments, the detector <NUM> may include a plurality of detector units. The detector units may include a scintillation detector (e.g., a cesium iodide detector) or a gas detector. The detector unit may be a single-row detector or a multi-rows detector.

The network <NUM> may facilitate exchange of information and/or data. In some embodiments, one or more components in the CT system <NUM> (e.g., the CT scanner <NUM>, the terminal <NUM>, the processing engine <NUM>, or the storage <NUM>) may send information and/or data to other component(s) in the CT system <NUM> via the network <NUM>. For example, the processing engine <NUM> may obtain image data from the CT scanner <NUM> via the network <NUM>. As another example, the processing engine <NUM> may obtain user instructions from the terminal <NUM> via the network <NUM>. In some embodiments, the network <NUM> may be any type of wired or wireless network, or combination thereof. Merely by way of example, the network <NUM> may include a cable network, a wireline network, an optical fiber network, a tele communications network, an intranet, an Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a wide area network (WAN), a public telephone switched network (PSTN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network <NUM> may include one or more network access points. For example, the network <NUM> may include wired or wireless network access points such as base stations and/or internet exchange points through which one or more components of the CT system <NUM> may be connected to the network <NUM> to exchange data and/or information.

The terminal <NUM> include a mobile device <NUM>-<NUM>, a tablet computer <NUM>-<NUM>, a laptop computer <NUM>-<NUM>, or the like, or any combination thereof. In some embodiments, the mobile device <NUM>-<NUM> may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, a smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistance (PDA), a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google Glass, an Oculus Rift, a Hololens, a Gear VR, etc. In some embodiments, the terminal <NUM> may be part of the processing engine <NUM>. In some embodiments, the terminal <NUM> may be connected to or otherwise communicate with the processing engine <NUM>.

The processing engine <NUM> may process data and/or information obtained from the CT scanner <NUM>, the terminal <NUM>, or the storage <NUM>. In some embodiments, the processing engine <NUM> may be a single server, or a server group. The server group may be centralized, or distributed. In some embodiments, the processing engine <NUM> may be local or remote. For example, the processing engine <NUM> may access information and/or data stored in the CT scanner <NUM>, the terminal <NUM>, and/or the storage <NUM> via the network <NUM>. As another example, the processing engine <NUM> may be directly connected to the CT scanner <NUM>, the terminal <NUM> and/or the storage <NUM> to access stored information and/or data. In some embodiments, the processing engine <NUM> may be implemented on a cloud platform. Merely by way of example, the 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 the like, or any combination thereof. In some embodiments, the processing engine <NUM> may be implemented on a computing device <NUM> having one or more components illustrated in <FIG> in the present disclosure.

The storage <NUM> may store data and/or instructions. In some embodiments, the storage <NUM> may store data obtained from the terminal <NUM> and/or the processing engine <NUM>. In some embodiments, the storage <NUM> may store data and/or instructions that the processing engine <NUM> may execute or use to perform exemplary methods described in the present disclosure. In some embodiments, the storage <NUM> may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM), or the like, or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drives, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a random access memory (RAM). Exemplary RAM may include a dynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (PEROM), an electrically erasable programmable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM, etc. In some embodiments, the storage <NUM> may be implemented on a cloud platform. Merely by way of example, the 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 the like, or any combination thereof.

In some embodiments, the storage <NUM> may be connected to the network <NUM> to communicate with one or more components in the CT system <NUM> (e.g., the processing engine <NUM>, the terminal <NUM>). One or more components in the CT system <NUM> may access the data or instructions stored in the storage <NUM> via the network <NUM>. In some embodiments, the storage <NUM> may be directly connected to or communicate with one or more components in the CT system <NUM> (e.g., the processing engine <NUM>, the terminal <NUM>). In some embodiments, the storage <NUM> may be part of the processing engine <NUM>.

<FIG> is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device <NUM> on which the processing engine <NUM> may be implemented according to some embodiments of the present disclosure. As illustrated in FIG. <NUM>-A, the computing device <NUM> may include a processor <NUM>, a storage <NUM>, an input/output (I/O) <NUM>, and a communication port <NUM>.

The processor <NUM> may execute computer instructions (program code) and perform functions of the processing engine <NUM> in accordance with techniques described herein. The computer instructions may include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions described herein. For example, the processor <NUM> may process image data obtained from the CT scanner <NUM>, the terminal <NUM>, the storage <NUM>, or any other component of the CT system <NUM>. In some embodiments, the processor <NUM> may include a microcontroller, a microprocessor, a reduced instruction set computer (RISC), an application specific integrated circuits (ASICs), an application-specific instruction-set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physics processing unit (PPU), a microcontroller unit, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced RISC machine (ARM), a programmable logic device (PLD), any circuit or processor capable of executing one or more functions, or the like, or any combinations thereof.

Merely for illustration, only one processor is described in the computing device <NUM>. However, it should be note that the computing device <NUM> in the present disclosure may also include multiple processors, thus operations and/or method steps that are performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, if in the present disclosure the processor of the computing device <NUM> executes both step A and step B, it should be understood that step A and step B may also be performed by two different processors jointly or separately in the computing device <NUM> (e.g., a first processor executes step A and a second processor executes step B, or the first and second processors jointly execute steps A and B).

The storage <NUM> may store data/information obtained from the CT scanner <NUM>, the terminal <NUM>, the storage <NUM>, or any other component of the CT system <NUM>. In some embodiments, the storage <NUM> may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM), or the like, or any combination thereof. For example, the mass storage may include a magnetic disk, an optical disk, a solid-state drives, etc. The removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. The volatile read-and-write memory may include a random access memory (RAM). The RAM may include a dynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM (PEROM), an electrically erasable programmable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM, etc. In some embodiments, the storage <NUM> may store one or more programs and/or instructions to perform exemplary methods described in the present disclosure.

The I/O <NUM> may input or output signals, data, or information. In some embodiments, the I/O <NUM> may enable a user interaction with the processing engine <NUM>. In some embodiments, the I/O <NUM> may include an input device and an output device. Exemplary input device may include a keyboard, a mouse, a touch screen, a microphone, or the like, or a combination thereof. Exemplary output device may include a display device, a loudspeaker, a printer, a projector, or the like, or a combination thereof. Exemplary display device may include a liquid crystal display (LCD), a light-emitting diode (LED)-based display, a flat panel display, a curved screen, a television device, a cathode ray tube (CRT), or the like, or a combination thereof.

The communication port <NUM> may be connected to a network (e.g., the network <NUM>) to facilitate data communications. The communication port <NUM> may establish connections between the processing engine <NUM> and the CT scanner <NUM>, the terminal <NUM>, or the storage <NUM>. The connection may be a wired connection, a wireless connection, or combination of both that enables data transmission and reception. The wired connection may include electrical cable, optical cable, telephone wire, or the like, or any combination thereof. The wireless connection may include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobile network (e.g., <NUM>, <NUM>, <NUM>, etc.), or the like, or a combination thereof. In some embodiments, the communication port <NUM> may be a standardized communication port, such as RS232, RS485, etc. In some embodiments, the communication port <NUM> may be a specially designed communication port. For example, the communication port <NUM> may be designed in accordance with the digital imaging and communications in medicine (DICOM) protocol.

<FIG> is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device <NUM> on which the terminal <NUM> may be implemented according to some embodiments of the present disclosure. As illustrated in <FIG>, the mobile device <NUM> may include a communication platform <NUM>, a display <NUM>, a graphic processing unit (GPU) <NUM>, a central processing unit (CPU) <NUM>, an I/O <NUM>, a memory <NUM>, and a storage <NUM>. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in the mobile device <NUM>. In some embodiments, a mobile operating system <NUM> (e.g., iOS, Android, Windows Phone, etc.) and one or more applications <NUM> may be loaded into the memory <NUM> from the storage <NUM> in order to be executed by the CPU <NUM>. The applications <NUM> may include a browser or any other suitable mobile apps for receiving and rendering information relating to image processing or other information from the processing engine <NUM>. User interactions with the information stream may be achieved via the I/O <NUM> and provided to the processing engine <NUM> and/or other components of the CT system <NUM> via the network <NUM>.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform(s) for one or more of the elements described herein. The hardware elements, operating systems and programming languages of such computers are conventional in nature, and it is presumed that those skilled in the art are adequately familiar therewith to adapt those technologies to the blood pressure monitoring as described herein. A computer with user interface elements may be used to implement a personal computer (PC) or other type of work station or terminal device, although a computer may also act as a server if appropriately programmed. It is believed that those skilled in the art are familiar with the structure, programming and general operation of such computer equipment and as a result the drawings should be self-explanatory.

<FIG> is a schematic diagram illustrating an exemplary processing engine <NUM> according to some embodiments of the present disclosure. Processing engine <NUM> may process data obtained from or via CT scanner <NUM>, processor <NUM>, storage <NUM>, input/output (I/O) <NUM>, communication port <NUM>, or the like, or any combination thereof. The data processed by processing engine <NUM> may include back projection data, forward projection data, correction data, filtered data, image data (e.g., original image data), or the like, or any combination thereof. Processing engine <NUM> may perform operations including, for example, data preprocessing, image reconstruction, image correction, image composition, lookup table creation, or the like, or any combination thereof. Processing engine <NUM> may include a data acquisition unit <NUM>, an image reconstruction unit <NUM>, a correction image generation unit <NUM>, and a correction unit <NUM>. Processing engine <NUM>, or a portion thereof, may be implemented on the computing device <NUM> as illustrated in <FIG>, or the mobile device as illustrated in <FIG>.

Data acquisition unit <NUM> may obtain a raw data set related to an object under examination. The raw data set may include a plurality of raw data related to the object under examination. The data acquisition unit <NUM> may obtain the raw data set or the raw data via the detector <NUM> or the storage <NUM>. The term "raw data" may refer to the data that may be detected by the detector <NUM>, and the raw data may be utilized to construct a CT image. The raw data may be generated by traversing X-rays through an object under examination. The object may include a substance, a tissue, an organ, a specimen, a body, or the like, or any combination thereof. In some embodiments, the object may include a patient or a part thereof. The objet may include a head, a breast, a lung, a pleura, a mediastinum, an abdomen, a long intestine, a small intestine, a bladder, a gallbladder, a triple warmer, a pelvic cavity, a backbone, extremities, a skeleton, a blood vessel, or the like, or any combination thereof.

Image reconstruction unit <NUM> may generate a CT image set based on the raw data set obtained from data acquisition unit <NUM>. The CT image set may include one or more CT images. The CT image may be generated based on a reconstruction algorithm. The reconstruction algorithm may include a Fourier slice theorem algorithm, a filtered back projection (FBP) algorithm, a fan-beam reconstruction algorithm, an iterative reconstruction algorithm, an analytic reconstruction algorithm, an algorithm based on compressed sensing (CS), or the like, or any combination thereof.

A CT image may be a representation of a cross section of tissue of an object (e.g., a CT image slice (or referred to as a slice for brevity) of the object) under examination having some thickness. A CT image may include one or more pixels arranged in a reconstruction matrix. The size of the reconstruction matrix may determine number of pixels in a CT image. A pixel value may refer to the value of a property of the pixel. For instance, a pixel value may refer to luminance value of a pixel, grey value of a pixel, color or RGB value of a pixel, saturation value of a pixel, or the like, or any combination thereof. In a CT image, the pixel value may represent density of tissue. With different pixel values in the CT image, the CT image may represent structure of the slice of an object under examination.

In some embodiments, the image reconstruction unit <NUM> may generate different CT images with different reconstruction parameters. The reconstruction parameter may include field of view (FOV), image thickness, image increment, kernel, or the like, or any combination thereof. Thickness of a slice of an object may be referred as slice thickness. The position of the slice of an object may be referred as slice position. Image thickness may refer to nominal width of reconstructed image along z axis. The z axis may be parallel to moving direction of the subject table <NUM>. The image thickness may be determined based on the slice thickness. In some embodiments, the image thickness of a CT image may be equal to or less than the slice thickness. The larger image thickness is, the higher resolution of the reconstructed image is. For a slice of the object under examination, at a certain slice position with a certain slice thickness, one or more CT images may be reconstructed to represent the slice. An image increment may refer to a distance between two consecutive CT images in terms of their slice positions in a CT image set including a stack of CT image slices. A field of view (FOV) may have the diameter of a CT image. The use of a small FOV may allow increased spatial resolution in a CT image, because the whole reconstruction matrix of the CT image may be used for reconstructing a smaller region than is the case with a larger FOV. CT images in a CT image set may be reconstructed based on the same reconstruction parameter(s). Accordingly, the reconstruction parameter(s) of the CT image set may be considered as the reconstruction parameter(s) of the CT images in the CT image set.

The correction image generation unit <NUM> may generate bone information (bone information) image set based on the CT image generated by the image reconstruction unit <NUM>. The bone information image set may include one or more bone information images. The bone information image may include beam hardening artifact of a CT image. Beam hardening artifact may be observed in a CT image when a polychromatic X-ray beam passes through an object where the lower energy photons are absorbed leaving only the higher energy photons passing through the object and detected. The bone information image may correct the beam hardening artifact in the CT image. In some embodiments, the bone information image may be generated based on the same reconstruction parameter(s) of the CT image generated by the image reconstruction unit <NUM>. For example, if the image reconstruction unit <NUM> reconstruct a CT image with an image thickness A and image increment B, a bone information image may also have the image thickness A and image increment B. In some embodiments, the bone information image may be generated based on a reference image. The reference image may refer to a bone information image reconstructed with a small image thickness and/or image increment. In some embodiments, the image thickness and/or image increment of the bone information image may be greater than the image thickness and/or image increment of the reference image. In some embodiments, the image thickness and/or image increment of the bone information image may be an integral multiple of image thickness and/or image increment of the reference image. In some embodiments, the reference image may be reconstructed with the smallest image thickness and/or the smallest image increment that the CT system <NUM> may achieve. Some of the reconstruction parameters of the bone information image and the reference image may be the same, such as FOV, kernel, etc., and some may be different, such as image thickness and image increment. The bone information image for a certain slice position may be generated by stacking a certain number of consecutive reference images that correspond to a same or similar portion of the object compared to the to the bone information image or the CT image slice of that slice position. For example, for a bone information image of a slice having an image thickness of <NUM> and image increment of <NUM>, and the reference image of the slice having an image thickness of <NUM> and image increment of <NUM>, two reference images of the slice may be stacked to generate the bone information image.

The hardening beam artifact may have two distinct appearances, streaks or dark bands in a CT image, and cupping artifact in a CT image. Such artifact in the CT image may be misinterpreted by a user (e.g., a doctor) as a feature of some disease. For example, hardening beam artifact with appearance of dark bands in a CT image may be misinterpreted as a feature of a tumor. Since bone information image may have characteristic of the hardening beam artifact, a bone information set may be used by a user (e.g., a doctor) to distinguish the hardening beam artifact from a feature of some disease. The bone information image set may be presented in the form of a bone information image set or a bone information image. In some embodiments, the correction image generation unit <NUM> may output a bone information image set or a bone information image to a user (e.g., a doctor) via the I/O <NUM>.

In some embodiments, the correction unit <NUM> may determine the working status of the CT system <NUM> when performing scans of a first object and a second object based on the similarity between a first reference image set and a second reference image set relating to the two objects. Different bone information image sets may be generated based on different raw data sets obtained by scanning the two different objects. Normally, a first bone information image set of the first object may be similar to a second bone information image set of the second object, considering that a bone information image set represent hardening beam artifact caused by hard tissue, and the proportion of hard tissue relative to soft tissue may be similar between different objects (e.g., different patients under examination). If the first bone information set is different from the second bone information set (e.g., the similarity of the first bone information set and the second bone information set is under a threshold), the working status of the CT system <NUM> may be considered to have changed between the scanning of the first object and the scanning of the second object. The correction unit <NUM> may identify abnormalities of the CT system <NUM> based on whether the working status of the CT system <NUM> has changed. In some embodiments, the working status of the CT system <NUM> may include the working status of the detector <NUM> (e.g., whether the detector <NUM> may work at a predetermined temperature) and the working status of the radioactive scanning source <NUM> (e.g., whether the radioactive scanning source <NUM> may emit a predetermined amount of X-ray).

The correction unit <NUM> may be configured to correct the CT image generated by the image reconstruction unit <NUM>. The correction unit <NUM> may remove hard tissue in the CT image generated in the image reconstruction unit <NUM> to generate a hard tissue corrected image. The hard tissue corrected image may include removed or reduced hard tissue. The correction unit <NUM> may correct hardening beam artifact in the CT image generated by the image reconstruction unit <NUM> to generate a beam hardening artifact corrected image. The beam hardening artifact corrected image may include removed or reduced beam hardening artifact. The correction may be performed by changing the pixel value of a pixel in the CT image. For example, to remove hard tissue in a CT image, correction unit <NUM> may change the pixel value of a pixel whose pixel value exceeds a threshold. As another example, to correct hardening beam artifact in a CT image, correction unit <NUM> may change the pixel value of a pixel based on the bone information image.

<FIG> is a flowchart of an exemplary process for generating a full quality image set according to some embodiments of the present disclosure. In some embodiments, one or more operations of process illustrated in <FIG> for generating a full quality image set may be implemented in the CT system <NUM> illustrated in <FIG>. For example, the process illustrated in <FIG> may be stored in the storage <NUM> in the form of instructions, and invoked and/or executed by the processing engine <NUM> (e.g., the processor <NUM> of the computing device <NUM> as illustrated in <FIG>, the CPU <NUM> of the mobile device <NUM> as illustrated in <FIG>).

In <NUM>, the data acquisition <NUM> may obtain a raw data set related to an object under examination. The raw data set may include a plurality of raw data related to the object under examination. The data acquisition <NUM> may obtain the raw data set via the CT scanner <NUM> or the storage <NUM>. The raw data set may be generated by emitting X-rays toward the object under examination.

In <NUM>, the image reconstruction unit <NUM> may generate a full quality image set based on the raw data set. The full quality image set may include one or more full quality images. The term "full quality image" may refer to a CT image with a small FOV (e.g., <NUM>). Details of soft tissue may be observed in a full quality image. As used herein, soft tissue may refer to a tissue that connects, supports, or surrounds another structure and/or organ of the body, not being hard tissue such as bone. In some embodiments, the soft tissue may include tendons, ligaments, fascia, skin, fibrous tissues, fat, and synovial membranes (which are connective tissue), muscles, nerves and blood vessels (which are not connective tissue), or the like, or any combination thereof. The hard tissue may refer to a tissue that is mineralized and has a firm intercellular matrix. In some embodiments, the hard tissue may include bone, tooth enamel, dentin, and cementum, or the like, or any combination thereof.

In some embodiments, the reconstruction of the full quality image images may be based on techniques including Fourier slice theorem, filtered back projection algorithm, fan-beam reconstruction, iterative reconstruction, etc. The image reconstruction unit <NUM> may reconstruct the full quality image based on a set of reconstruction parameters, such as a field of view (FOV), image thickness, image increment, kernel, or the like, or any combination thereof.

In <NUM>, the image reconstruction unit <NUM> may generate a max FOV image set based on the raw data set. The max FOV image set may include one or more max FOV images. The term "max FOV image" may refer to a CT image with a large FOV (e.g., <NUM>) larger than the FOV of a corresponding full quality image obtained based on the same raw data set. The boundary between soft tissue and hard tissue may be observed in the max FOV image.

In some embodiments, the reconstruction of the max FOV image may be based on techniques including Fourier slice theorem, filtered back projection algorithm, fan-beam reconstruction, iterative reconstruction, etc. The image reconstruction unit <NUM> may reconstruct a max FOV image based on a set of reconstruction parameter, such as a field of view (FOV), image thickness, image increment, kernel, or the like, or any combination thereof. In some embodiments, the FOV of a max FOV image may be larger than that of a full quality image, and the spatial resolution of the full quality image may be higher than that of the max FOV image. In some embodiments, image increment and/or image thickness of the max FOV image set and the full quality image set may be the same.

In <NUM>, the correction image generation unit <NUM> may generate a bone information image set based on the max FOV image set. The bone information image set may include one or more bone-info images. The term "bone information image" may refer to an image for correcting hardening beam artifact caused by the hard tissue. The bone information image may represent hardening beam artifact of a CT image. The hardening beam artifact may have two distinct appearances, streaks or dark bands in a CT image, or cupping artifact in a CT image. Pixels of the bone information image may represent at least one of these appearances.

In some embodiments, a bone information image set may be generated based on a reference image set. The reference image set may include at least one reference image. The term "reference image" may refer to a bone information image constructed with a small image thickness and/or image increment, while other reconstruction parameters (e.g., FOV, kernel, etc.) of the reference image are the same as those of the bone information image. In some embodiments, the image thickness and/or image increment of the bone information image may be greater than the image thickness and/or image increment of the reference image. In some embodiments, the image thickness of the bone information image may be an integral multiple of the image thickness of the reference image. In some embodiments, the image increment of the bone information image may be an integral multiple of image increment of the reference image. In some embodiments, the reference image may be reconstructed with the smallest image thickness and/or smallest image increment that the CT system <NUM> may achieve. The bone information image for a certain slice may be generated by stacking a certain number of consecutive reference images that correspond to a same or similar portion of the object compared to the bone information image or the CT image slice of the slice position. For example, for a bone information image of a slice having an image thickness of <NUM> and image increment of <NUM>, and the reference image of the slice having an image thickness of <NUM> and image increment of <NUM>, two reference images may be stacked to generated a bone information image. In some embodiments, the bone information image(s) may be used correct the hardening beam artifact in the full quality image. The bone information image set may be generated in advance and stored in the storage <NUM>. The correction image generation unit <NUM> may generate the bone information image based on the reference image via accessing the storage <NUM>. Detailed description of reference image generation may be found in <FIG> and the description thereof.

In <NUM>, the correction unit <NUM> may remove or reduce the hard tissue in the full quality image based on the max FOV image set to generate a hard tissue corrected image. The hard tissue corrected image may include removed or reduced hard tissue. Considering that an FOV of the full quality image may be smaller than that of the max FOV image, the integrity of the hard tissue in the max FOV image may be better than in the full quality image set. For example, in a CT image slice corresponding to the head of the object under examination, a full quality image related to the slice may display a part of the skull of the object under examination, while a max FOV image related to the slice may display the whole skull. The correction unit <NUM> may extract shape characteristics (e.g., a profile curve of the hard tissue) of the hard tissue in the max FOV image, and remove the hard tissue in the full quality image based on the shape characteristics of the hard tissue in the full quality image.

In some embodiments, the correction image generation unit <NUM> may output the bone information image set or the bone information image to a user (e.g., a doctor) via the I/O <NUM>, on the basis of which the user may distinguish the hardening beam artifact and a feature of some disease. In some embodiments, the correction image generation unit <NUM> may forward the bone information image set of the bone information image to a storage device (e.g., storage <NUM>, etc.) for future use.

In <NUM>, the correction unit <NUM> may correct the beam hardening artifact of the full image set based on the bone information image set to generate a beam hardening artifact corrected image. The beam hardening artifact corrected image may include removed or reduced beam hardening artifact. The bone information image may be used to correct beam hardening artifact by changing pixel value of pixels in the full quality image which represent the beam hardening artifact. Pixel in the full quality image may have a corresponding pixel in the bone information image representing same position in the slice. The correction unit <NUM> may change the pixel value of a pixel in the max FOV image based on the pixel value of the corresponding pixel in the bone information image.

<FIG> is a flowchart of an exemplary process for generating a reference image according to some embodiments of the present disclosure. In some embodiments, one or more operations of process illustrated in <FIG> for generating a full quality image set may be implemented in the CT system <NUM> illustrated in <FIG>. For example, the process illustrated in <FIG> may be stored in the storage <NUM> in the form of instructions, and invoked and/or executed by the processing engine <NUM> (e.g., the processor <NUM> of the computing device <NUM> as illustrated in <FIG>, the CPU <NUM> of the mobile device <NUM> as illustrated in <FIG>).

In <NUM>, the image reconstruction unit <NUM> may generate a max FOV image. The operation <NUM> may be performed according to the relevant portion of the process illustrated in <FIG> and the description thereof.

In <NUM>, the correction image generation unit <NUM> may segment the max FOV image into hard tissue image and soft tissue image. The correction image generation unit <NUM> may segment the max FOV image by assigning the pixels whose pixel values exceed a threshold to the hard tissue image, and assigning the pixels whose pixel values are below the threshold to the soft tissue image.

In <NUM>, the correction image generation unit <NUM> may forward project the hard tissue image and the soft tissue image. By the forward projection, the hard tissue image and the soft tissue image may be transformed from an image domain into a projection domain. The correction image generation unit <NUM> may obtain projection data related to the hard tissue image and projection data related to the soft tissue image in the projection domain.

In <NUM>, the correction image generation unit <NUM> may determining the soft tissue thickness in the soft tissue image and hard tissue thickness in the hard tissue image based on the projection data related to the hard tissue image and the projection data related to the soft tissue image. In some embodiments, correction image generation unit <NUM> may determine the thickness of the soft tissue in the soft tissue image and thickness of the hard tissue in the hard tissue image based on the assumption that the object under examination include only a slab of hard tissue and a slab of soft tissue. An X-ray beam may enter into the slab of hard tissue of thickness XB and after passing through the slab of hard tissue may enter into the slab of soft tissue of thickness XT, and then the X-ray beam may be detected by the detector <NUM> after it exits the soft tissue. It should be noted that even if the hard tissue and the soft tissue along the beam path may distribute differently, for the purposes of determining the hard tissue thickness and the soft tissue thickness, the assumption essentially does not change the forward projection data obtained in <NUM>.

In <NUM>, the correction image generation unit <NUM> may obtain correction data based on the soft tissue thickness and the hard tissue thickness. With a known soft tissue thickness (e.g., XT) and a known hard tissue thickness (e.g., XB), correction image generation unit <NUM> may obtain correction data based a correction table. In some embodiments, the correction table may be generated in advance and stored the storage <NUM>. The correction table may include correction data due to the hardening beam artifact for the conversion of polychromatic projection data that are impacted by the hardening beam effect (e.g., forward projection data obtained in <NUM>), into monochromatic projection data. The correction image generation unit <NUM> may obtain a CT image (e.g., a reference image) containing only the hardening beam artifact with reconstructing the correction data. In some embodiments, the correction image generation unit <NUM> may perform interpolation or extrapolation based on values available in the correct table if correction data corresponding to the soft tissue thickness XT and the hard tissue thickness XB do not exist in the correction table. For example, for a soft tissue thickness of <NUM>, the correction table only have correction data A for a soft tissue thickness of <NUM> and correction data B for a soft tissue thickness of <NUM>, the correction unit <NUM> may determine an average of the correction data A and correction data B as the correction data for the soft tissue thickness of <NUM>.

In <NUM>, the correction image generation unit <NUM> may generate a reference image based on the correction data. With the construction process, the correction image generation unit <NUM> may transform the correction data from the projection data domain to the image domain. The reconstruction of the reference image may be based on techniques including Fourier slice theorem, filtered back projection algorithm, fan-beam reconstruction, iterative reconstruction, etc. The correction image generation unit <NUM> may reconstruct the reference image based on a set of reconstruction parameters including, for example, field of view (FOV), image thickness, image increment, kernel, or the like or any combination thereof. In some embodiments, the reference image may be reconstructed based on the smallest image thickness and/or smallest image increment that the CT system <NUM> may achieve.

<FIG> is a flowchart of an exemplary process for removing hard tissue and/or correcting hardening beam artifact according to some embodiments of the present disclosure. For diagnosis purposes, different full quality images reconstructed with different reconstruction parameters may be generated. In some embodiments, one or more operations of process illustrated in <FIG> for generating a full quality image set may be implemented in the CT system <NUM> illustrated in <FIG>. For example, the process illustrated in <FIG> may be stored in the storage <NUM> in the form of instructions, and invoked and/or executed by the processing engine <NUM> (e.g., the processor <NUM> of the computing device <NUM> as illustrated in <FIG>, the CPU <NUM> of the mobile device <NUM> as illustrated in <FIG>). The user (e.g., a doctor), to determine a feature of some disease, may need a first full quality image reconstructed with a first image increment and/or a first image thickness, and a second full quality image reconstructed with a second images increment and/or a second image thickness. Both the first full quality image and the second full quality image may need to be processed to remove the hard tissue and/or correct the hardening beam artifact. The correction image generation unit <NUM> may generate different bone information images reconstructed with different reconstruction parameters based on the same reference image.

In <NUM>, the image reconstruction unit <NUM> may generate a first image set based on a raw data set. The first image set may include a first full quality image and a first max FOV image. The first full quality image may be generated based on reconstruction parameters including FOV A1, image thickness A2, and image increment A3. The first max FOV image may be generated based on reconstruction parameters including FOV B1, image thickness A2, and image increment A3. In some embodiments, the FOV A1 may be smaller than FOV B1. Operation of <NUM> may be performed according to the relevant portion (e.g., operation <NUM>, operation <NUM>, etc.) of the process illustrated in <FIG> and the description thereof.

In <NUM>, the correction image generation unit <NUM> may generate a reference image based on the first max FOV image. The reference image may be generated based on reconstruction parameters including FOV B1, image thickness M1, and image increment M2. In some embodiments, the value of A2 may be greater than the value of M1. In some embodiments, the value of A2 may be an integral multiple of the value of M1. In some embodiments, image thickness M1 may be the smallest image increment that the CT system <NUM> may achieve. In some embodiments, the value of A3 may be greater than the value of M2. The value of A3 may be an integral multiple of the value of M2. In some embodiments, the image increment M2 may be the smallest image increment that the CT system <NUM> may achieve. The operation <NUM> may be performed according to the relevant portion (e.g., operation <NUM>, operation <NUM>, operation <NUM>, operation <NUM>, operation <NUM>, etc.) of the process illustrated in <FIG> and the description thereof.

In <NUM>, the correction image generation unit <NUM> may generate a first bone information image based on the reference image. The correction image generation unit <NUM> may reconstruct a first bone information image by stacking a certain number of reference images. The first bone information image may have reconstruction parameters including FOV B1, image thickness A2, and image increment A3. The operation <NUM> may be performed according to the relevant portion (e.g., operation <NUM>, etc.) of the process illustrated in <FIG> and the description thereof.

In some embodiments, the correction image generation unit <NUM> may output the first bone information image to a user (e.g., a doctor) via the I/O <NUM> on the basis of which the user may distinguish the hardening beam artifact and a feature of some disease.

In <NUM>, the correction unit <NUM> may correct the first full quality image based on the first bone information image and the first max FOV image. The correction unit <NUM> may correct the hardening beam artifact of the first full quality image based the first bone information image to generate a beam hardening artifact corrected image. The beam hardening artifact corrected image may include removed or reduced beam hardening artifact. In some embodiments, the correction unit <NUM> may remove hard tissue of the first full quality image based on the first max FOV image to generate a hard tissue corrected image. The hard tissue corrected image may include removed or reduced hard tissue. The operation <NUM> may be performed according to the relevant portion (e.g., operation <NUM>, etc.) of the process illustrated in <FIG> and the description thereof.

In <NUM>, the image reconstruction unit <NUM> may generating a second image set based on the raw data set. The raw data set used in <NUM> may be same as the raw data used in <NUM>. The second image set may include a second full quality image. The second full quality image may have reconstruction parameters including FOV A1 (which is same as FOV of first full quality image), image thickness C2, image increment C3. Operation of <NUM> may be performed according to the relevant portion (e.g., operation <NUM>, operation <NUM>, etc.) of the process illustrated in <FIG> and the description thereof.

In <NUM>, the correction image generation unit <NUM> may generate a second bone information image based on the reference image. The correction unit <NUM> may reconstruct a second bone information image by stacking a certain number of reference images. The second bone information image may have reconstruction parameter including FOV B1, which is same as FOV of the first max FOV image and the first bone information image, image thickness C2, and image increment C3. In some embodiments, the image thickness C2 of the second bone information image may greater than the image thickness M1 of the reference image. In some embodiments, the image thickness C2 of the second bone information image may be an integral of the image thickness M1 of the reference image. In some embodiments, the image increment C3 of the second bone information image may be greater than the image increment M2 of the reference image. In some embodiments, the image increment C3 of the second bone information image may greater than the image increment M2 of the reference image. The image increment C3 of the second bone information image may be an integral of the image increment M2 of the reference image. Detailed description of step <NUM> may be found in <FIG> and the description thereof. The operation <NUM> may be performed according to the relevant portion (e.g., operation <NUM>, etc.) of the process illustrated in <FIG> and the description thereof.

In some embodiments, the correction image generation unit <NUM> may output the second bone information image to a user (e.g., a doctor) via the I/O <NUM> to distinguish the hardening beam artifact and feature from a feature of some disease.

In <NUM>, the correction unit <NUM> may correct the second full quality image based on the second bone information image. The correction unit <NUM> may correct the hardening beam artifact of the second full quality image based on the second bone information image to generate a beam hardening artifact corrected image. The beam hardening artifact corrected image may include removed or reduced beam hardening artifact. The operation <NUM> may be performed according to the relevant portion (e.g., operation <NUM>, etc.) of the process illustrated in <FIG> and the description thereof.

<FIG> is a flowchart of an exemplary process for checking the working status of CT system <NUM> based on the bone information image according to some embodiments of the present disclosure. Different bone information image sets may be generated based on different raw data sets obtained by scanning the two different objects. In some embodiments, one or more operations of process illustrated in <FIG> for generating a full quality image set may be implemented in the CT system <NUM> illustrated in <FIG>. For example, the process illustrated in <FIG> may be stored in the storage <NUM> in the form of instructions, and invoked and/or executed by the processing engine <NUM> (e.g., the processor <NUM> of the computing device <NUM> as illustrated in <FIG>, the CPU <NUM> of the mobile device <NUM> as illustrated in <FIG>). Normally, a first bone information image set of a first object may be similar to a second bone information image set of a second object, since bone information image set represents hardening beam artifact caused by hard tissue, and the proportion of hard tissue and relative to soft tissue may be similar between different objects (e.g., different patients under examination). If the first bone information set is different from the second bone information set (e.g., the similarity of the first bone information set and the second bone information set is under a threshold), the working status of the CT system <NUM> may have changed, and the correction unit <NUM> may identify abnormalities of the CT system <NUM> based on whether the working status of the CT system <NUM> have changed. In some embodiments, the working status of the CT system <NUM> may include working status of the detector <NUM> (e.g., whether the detector <NUM> may work at a predetermined temperature, etc.) and the working status of the radioactive scanning source <NUM> (e.g., whether the radioactive scanning source <NUM> may emit predetermined amount of X-ray).

In <NUM>, the correction unit <NUM> may generate a first reference image based on a first raw data set. The first raw data set may include a plurality of raw data related to a first object under examination. Detailed description of <NUM> may be found in <FIG> and the description thereof.

In <NUM>, the correction unit <NUM> may generate a second reference image based on a second raw data set. The second raw data set may include a plurality of raw data related to a second object under examination. Detailed description of step <NUM> may be found in <FIG> and the description thereof.

In <NUM>, the correction unit <NUM> may determine the working status of the CT system <NUM> based on the similarity between the first reference image and the second reference image. If the similarity between the first reference image and the second reference image exceeds a threshold, the working status of the CT system <NUM> when scan the first object may be different from the working status of the CT system <NUM> when scan the second object, and the. The correction unit <NUM> may identify abnormalities of the CT system <NUM> based on difference between the working status of the CT system <NUM> when scan the first object and the working status of the CT system <NUM> when scan the second object. If the similarity between the first reference image and the second reference image do not exceed a threshold, working status of the CT system <NUM> when scan the first object may be the same with the working status of the CT system <NUM> when scan the second object.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein.

A non-transitory computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave.

Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes and methods to any order except as may be specified in the claims. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description of embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the various inventive embodiments. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, inventive embodiments lie in less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, and so forth, used to describe and claim certain embodiments of the application are to be understood as being modified in some instances by the term "about," "approximate," or "substantially. " For example, "about," "approximate," or "substantially" may indicate ±<NUM>% variation of the value it describes, unless otherwise stated. Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the application are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable.

Claim 1:
A system, comprising:
a computer-readable storage medium (<NUM>) storing a set of instructions for CT image reconstruction;
a processor (<NUM>) in communication with the computer-readable storage medium (<NUM>), wherein when executing the set of instructions, the system is directed to:
obtain a raw data set related to an object;
generate a first image set by image reconstruction based on the raw data set, wherein the first image set includes a first full quality image and a max field of view image, both the first full quality image and the max field of view image are obtained by reconstruction of the raw data set, by varying the field of view parameter, and the max field of view image has a larger field of view than the first full quality image;
generate a hard tissue corrected image by removing hard tissue from the first full quality image, by changing pixel values of pixels representing the hard tissue, based on the max field of view image;
generate a plurality of reference images based on reconstruction parameters, wherein the reconstruction parameters include a slice thickness or a slice increment;
generate a first bone information image by stacking a first number of the plurality of reference images;
generate a second image set by image reconstruction based on the raw data set, wherein the second image set includes a second full quality image, wherein the first full quality image and the second full quality image are reconstructed by different reconstruction parameters, wherein the reconstruction parameters include a slice thickness or a slice increment;
generate a second bone information image by changing the reconstruction parameters by stacking a second number of reference images; and
generate a beam hardening artifact corrected image by correcting beam hardening artifact of the second full quality image, by changing pixel values of pixels representing the beam hardening artifact, based on the second bone information image.