JP2015112478A - Method and device for obtaining beam hardening correction coefficient for performing beam hardening correction on computer tomography data - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a beam hardening correction coefficient for performing beam hardening correction on computer tomography data.SOLUTION: A method for obtaining beam hardening correction coefficient for performing beam hardening correction on computer tomography data comprises: obtaining an original reconstructed image of an object of a specific size and an original sinogram; processing the original reconstructed image by error reduction and then obtaining an error-reduced sinogram; sampling and calculating an average of original sinograms and an average of error-reduced sinograms; optimizing the original sinogram according to the error-reduced sinogram to determine the coefficient vector of the optimization function for the object of the specific size; and fitting the coefficient vector of the optimization function of the original sinogram to obtain the beam hardening correction coefficient for the object of the specific size.

Description

  The present invention relates generally to computed tomography (CT), and more specifically to a method and device for obtaining a beam hardening correction factor for performing beam hardening correction on computer tomography data.

  Auxiliary diagnostic devices include magnetic resonance (MR) systems, ultrasound systems, computed tomography (CT) systems, positron emission tomography (PET) systems, nuclear medicine, and other types of Includes an imaging system.

  For example, during CT-type X-ray imaging of a patient with a CT system, X-rays are used to image the internal structure of the patient's body and features of the region of interest (ROI). Imaging is performed by a CT scanner. In operation, the object is scanned for collection of original data, and then after the original data is preprocessed, the image is reconstructed and further post-processing is performed to improve the quality of the image.

  Due to the spectral correlation of the light attenuation performance of the actual object, in the case of polychromatic X-rays, it can be seen that the average energy of the X-rays emitted by the object being penetrated shifts to a higher energy value. . This effect is called “beam hardening”. In the reconstructed image of the object, linear and spectrally related light attenuation can be observed via a shift to the grayscale value in the theoretical case. In particular, the shift in grayscale values in the reconstructed image caused by high nuclear charge numbers and high density materials (such as bones) or beam hardening virtual images can prevent the reconstructed image from interfering with the correct judgment of the image. In the worst case, it may cause the related doctor to misinterpret the image.

  In preprocessing, beam hardening correction is performed to at least partially remove the virtual image. Some existing beam hardening techniques have shown improved uniformity for aligned scans, but for eccentric scans, the images still show banded artifacts.

  One embodiment of the present invention provides a method for obtaining a beam hardening correction factor for performing beam hardening correction on computed tomography data. The method includes firstly obtaining an original reconstructed image and an original sinogram of an object of a particular size, and secondly processing the original reconstructed image with error reduction. Later, obtaining an error-reduced sinogram; third, sampling and calculating the original sinogram average and error-reduced sinogram average; and fourth, an object of a particular size Optimize the original sinogram according to the error-reduced sinogram to determine the coefficient vector of the optimization function, and finally the beam hardening correction factor for the object of a specific size Fitting a coefficient vector of an optimization function of the original sinogram to obtain.

  Another embodiment of the present invention provides a device for obtaining a beam hardening correction factor for performing beam hardening correction on computed tomography data. The device includes acquisition means, error reduction means, averaging means, optimization means, and fitting means. Here, the acquisition means is used to acquire the original reconstructed image and the original sinogram of the object of a specific size, and the error reduction means processes the original reconstructed image by error reduction. Is used to obtain an error-reduced sinogram, the averaging means is used to sample and calculate the average value of the original sinogram and the average value of the error-reduced sinogram, and the optimization means is Used to optimize the original sinogram according to the error-reduced sinogram to determine the coefficient vector of the optimization function for the object of a specific size, and the fitting means Fit the coefficient vector of the original sinogram optimization function to obtain the beam hardening correction factor for the object It is used to grayed.

  A method for performing beam hardening correction on computed tomography data using a beam hardening correction factor is provided for performing beam hardening correction on computer tomography data of other objects of a particular size. 6. It is obtained according to the method described in any one of 6 above.

  Yet another embodiment of the present invention provides an apparatus for performing beam hardening on computed tomography data. The apparatus includes a device for obtaining a beam hardening correction factor for performing beam hardening correction on computer tomography data as described above, and beam hardening on computer tomography data of other objects of a specific size. A correction calculation device for using the beam hardening correction factor to perform the correction.

  The fourth embodiment of the present invention provides a computed tomography apparatus. The apparatus includes a scanning device and a processor. Here, the scanning device is used to scan the object using X-rays to obtain the original data for generating the original reconstructed image, and the processor Obtaining an original reconstructed image and original sinogram of an object of a specific size that is operably coupled and processing the original reconstructed image with error reduction, then an error-reduced sinogram To sample the original sinogram average and error-reduced sinogram average, and to determine the coefficient vector of the optimization function for a particular size object, Optimize original sinogram according to error-reduced sinogram, and beam hardening correction for specific size objects To obtain the number, it can be programmed to achieve and fitting the coefficient vector of the optimization function of the original sinogram.

  A fifth embodiment of the present invention provides a computer program product that includes instructions stored on a non-volatile recording medium, and when the instructions are executed by a processor, the steps of the method disclosed in the embodiments of the present invention are performed. Run.

  The sixth embodiment of the present invention provides a non-volatile storage medium storing instructions that, when executed on a processor, perform the steps of the methods disclosed in the embodiments of the present invention.

  In order to provide a thorough understanding of the present disclosure, embodiments of the present invention are described below with reference to the accompanying drawings.

It is a block diagram of CT imaging system by this indication. It is a schematic block diagram of the system shown in FIG. 6 is a flowchart of beam hardening correction according to an embodiment of the present disclosure. FIG. 6 is a schematic diagram of a method for determining a boundary view according to an embodiment of the present disclosure; FIG. 6 is an image of a water phantom reconstructed after using existing beam hardening correction. Figure 5 is another image of a water phantom reconstructed after using existing beam hardening correction. 3 is an image of a water phantom reconstructed after using beam hardening correction according to one embodiment of the present disclosure. 4 is another image of a water phantom reconstructed after using beam hardening correction according to one embodiment of the present disclosure. FIG. 5 is an image of a skull reconstructed after using existing beam hardening correction. 4 is an image of a head phantom reconstructed after using beam hardening correction according to one embodiment of the present disclosure. 1 is a block diagram of a device for obtaining beam hardening correction factors according to one embodiment of the present disclosure. FIG. 1 is a block diagram of a device for beam hardening correction according to one embodiment of the present disclosure. FIG.

  In the following detailed description, which refers to the accompanying drawings as a part, specific embodiments in which the present disclosure is implemented are shown. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure. The embodiments may be combined or alternative embodiments may be used and structural, logical and electrical changes may be made without departing from the scope of the various embodiments of the present disclosure. It should be understood that The following detailed description is, therefore, not to be construed as limiting, but rather as exemplification. The scope of the present invention should be defined by the appended claims and their equivalents.

  With reference to FIGS. 1 and 2, a CT imaging system 10 is shown as including a gantry 12. In a non-limiting embodiment, the system 10 includes a “third generation” CT scanner. The gantry 12 includes an x-ray source 14 that projects x-rays 16 toward a detector assembly 18 opposite the gantry 12. The detector assembly 18 includes a plurality of detectors 20 and a data acquisition system (DAS (data acquisition system)) 32. The plurality of detectors 20 sense projected X-rays that pass through the medical patient 22. Each detector 20 impacts a patient 22 and thus generates an analog electrical signal that represents the intensity of x-rays attenuated by the patient as it passes through the patient 22. The detector 20 typically includes a collimator that collimates an X-ray beam received by the detector, a scintillator that is adjacent to the collimator and converts X-rays into luminous energy, and receives luminous energy from the adjacent scintillator. And a photodiode for generating an electrical signal therefrom. In general, each scintillator in the scintillator array converts X-rays into luminous energy and emits luminous energy toward an adjacent photodiode. Each photodiode detects light intensity energy and generates a corresponding electrical signal. Each detector 20 in the detector array 18 generates a separate electrical signal. The electrical signal represents the intensity of the impinging radiation beam (eg, x-ray beam) and can therefore be used to estimate the attenuation of the radiation beam as it passes through the object or patient 22.

  During scanning, the gantry 12 and components attached to the gantry 12 rotate about a center of rotation 24 to acquire X-ray projection data. The rotation of the gantry 12 and the operation of the X-ray source 14 can be managed by the control mechanism 26 of the CT system 10. Management mechanism 26 includes an x-ray controller 28 that provides power and timing signals to x-ray source 14 and a gantry motor controller 30 that controls the rotational speed and position of gantry 12. The DAS 32 in the control mechanism 26 samples the analog data from the detector 20 and converts the analog data into a digital signal for subsequent processing. The output of DAS 32 includes a projection data set in an attenuation measurement obtained at a specific gantry rotation angle (eg, angle of view). As the gantry 12 rotates, multiple views can be obtained with a single rotation. A single rotation refers to a full 360 degree rotation of the gantry 12. Each view has a corresponding angle of view and a specific position on the gantry 12.

  The reconstructed image is used as input to computer 36, which stores the image in mass storage device 38.

  The computer 36 also receives commands and scan parameters from the operator via the operator console 40. The operator console 40 has a specific form of operator interface, such as a keyboard, mouse, voice activated controller, or any other suitable input device. The associated display 42 allows the operator to view other data from the computer 36 and the reconstructed image. Commands and parameters from the operator can be used by computer 36 to provide control signals and information to DAS 32, X-ray controller 28, and gantry motor controller 30. In addition, the computer 36 can operate a table motor controller 44 that controls the motorized table 46 to determine the position of the patient 22 and the gantry 12. In particular, the table 46 moves the patient 22 in whole or in part through the gantry opening 48 as shown in FIG.

  In one embodiment, the computer 36 is a device 50, such as a floppy disk drive, CD-ROM drive, DVD drive, magneto-optical disk (MOD), or floppy disk. Any other digital device, including a network connection device, such as an Ethernet device, for reading instructions and / or data from a computer readable medium 52, such as CD-ROM, DVD, or the Internet, and still developed Including digital devices that are not. In another embodiment, computer 36 executes instructions stored in firmware (not shown). In some configurations, computer 36 and / or image reconstructor 34 are programmed to perform the functions described herein.

  FIG. 3 is a flowchart of beam hardening correction according to an embodiment of the present disclosure. Various sizes of water phantoms are used to calculate the beam hardening correction factor. However, the present invention is not limited to the use of only a water phantom, and can be applied to the use of any phantom. Different sized water phantoms correspond to different sized scan fields of view (SFOV (Scan Fields of View)) ready to scan. Assume that N water phantoms of different sizes have been selected.

  For a particular size water phantom, at step 302, the original reconstructed image Iorig and the original sinogram Iorig sin are obtained. The original reconstructed image Iorig and the original sinogram Iorig sin can be input after the reconstruction of the projection data of the DAS 32 by the image reconstructor 34, can also be obtained from the mass storage device 38, and optionally Can be obtained from the computer 36.

  At step 304, orthographic projection is performed on the original reconstructed image, which involves the need to process the projection values by error reduction, thereby obtaining an error reduced sinogram IUnif sin. The approach to error reduction processing is not limited, but to obtain the value set P ′, before dividing the sum by the number of pixels, the CT of the pixels through which the X-rays detected by each detector pass according to the view. To sum the values, locate the set of projection values P corresponding to the value set P ′ in the original sinogram, and obtain the error-reduced sinogram IUnif sin, the following equation:

Where a is a constant factor, for example a value defined by the system for a water phantom.

  Next, at step 306, a boundary view is determined. In the original sinogram Iorig sin, the first view Orig_View1, eg, view 400, where the detector channel intersects the water phantom is found from top to bottom, while the detector channel intersects the water phantom in another direction. A first view Orig_View2, eg, view 200, is found from bottom to top. As shown in FIG. 4, the abscissa represents the view and the ordinate represents the detector channel. The upper line is the first horizontal line that intersects the original sinogram from above, and the abscissa corresponding to the intersection represents the view 400. The lower line is the first horizontal line that intersects the original sinogram from below, and the abscissa corresponding to the intersection represents the view 200. Similarly, views Unif_View1 (eg, view 400) and Unif_View2 (eg, view 200) corresponding to Orig_View1 (eg, view 400) and Orig_View2 (eg, view 200) are found in the error-reduced sinogram IUnif sin.

  Then, at step 308, the average sine value of the view boundary is calculated. The original sinogram and the error-reduced sinogram each need to be calculated. First, sampling is necessary. In the original sinogram, orthographic values of two boundary views and several views before and after the two boundary views are averaged. For example, for Orig_View1, the 20 views before and after Orig_View1 are identified, and for Orig_View2, the 20 views before and after Orig_View2 are identified, to obtain an average value Orig_Aver, the average of the orthographic values of 42 views in total The value is calculated. The same operation is applied to the sinogram with reduced error. For Unif_View1, the 20 views before and after Unif_View1 are identified, and for Unif_View2, the 20 views before and after Unif_View2 are identified, and to obtain the average value Unif_Aver, the average value of the orthographic values of the 42 views in total Calculated. Here, the number of views to be averaged can be interpreted as a parameter depending on the noise level and can therefore be set experimentally. The number may be 10, 20, or 40, but in one embodiment, the number of views obtained from the two sinograms is equal.

  Then, at step 310, the average view is sampled to optimize the original sinogram Iorig sin by bringing the original sinogram Iorig sin as close as possible to the error-reduced sinogram Iorig sin. The optimization approach is shown with reference to the following equation:

Here, Q and Q ′ are Orig_Aver and Unif_Aver, respectively.

  The following formula,

Is taken as the ideal orthographic value, where a is a constant factor, for example a value defined by the system for a water phantom. For this purpose, the original orthographic value P is passed through a set of basis functions Bj (j = 1, 2,... N, where n is determined experimentally as the number of basis functions used). I = 1, 2,. . . m. Where m is the highest self-defined order. The purpose of this step is to determine the coefficient vector b of the optimization function.

  For different sized water phantoms, steps 302-310 need to be repeated, thereby determining N coefficient vectors b for N different sized water phantoms.

  In step 312, N coefficient vectors b are fitted to obtain the beam hardening correction coefficient c. The fitting is, for example, the following formula:

Where h = 1, 2,. . . h, where h is the highest self-defined order that may be the same as or different from m.

  The beam hardening correction coefficient vector c can then be used to perform beam hardening correction on CT data of other objects of different sizes. The correction formula is the same as the formula described in the above fitting. The corrected projection data Pnew is as follows.

It should be noted that the system needs to have the ability to perform beam hardening correction on various size objects. However, considering the individual objects, the beam hardening correction factor can be obtained for an individual object only if steps 302-312 are performed only once.

  FIG. 5 is an image of a water phantom reconstructed after using existing beam hardening correction, resulting from an 5.6 mm eccentric scan. It can be seen that there is an undesirable black circle between the water phantom and the boundary. FIG. 6 is another image of a water phantom reconstructed after using existing beam hardening correction, resulting from a 5 cm eccentric scan. It can be seen that the problem becomes more severe as the eccentric distance increases. There are broader band artifacts in the image that are clearly undesirable. FIG. 7 is an image of a water phantom reconstructed after using beam hardening correction according to an embodiment of the present disclosure, also obtained from an 5.6 mm eccentric scan, in which black dots are evident Has disappeared. FIG. 8 is another image of a water phantom reconstructed after using beam hardening correction according to an embodiment of the present disclosure, obtained from a 5 cm eccentric scan. Here, the band-like artifact disappears, and a relatively uniform image that is desirable and conforms to the actual image is obtained. Here, the result of actually imaging the patient's head can be seen. FIG. 9 is an image of the skull reconstructed after using existing beam hardening correction, obtained from a 5 cm eccentric scan. It can be seen that there are band-like artifacts in the skull image that will affect the diagnosis of the doctor. FIG. 10 is an image of a head phantom reconstructed after using beam hardening correction according to an embodiment of the present disclosure, also obtained from a 5 cm eccentric scan. It can be seen that the problem of zonal artifacts present in the skull image is greatly reduced. In practice, the method of the present invention removes or reduces “black circles” that can occur during slightly eccentric scans, and banding artifacts that can occur during scans that are clearly eccentric. It is possible to operate.

  FIG. 11 is a block diagram of a device for obtaining beam hardening correction factors according to an embodiment of the present disclosure. Here, the device 1100 for obtaining the beam hardening correction coefficient includes an acquisition unit 1101, an error reduction unit 1102, an averaging unit 1103, an optimization unit 1104, and a fitting unit 1105. Acquisition means 1101 is coupled at least to error reduction means 1102, averaging means 1103, and optimization means 1104. Error reduction means 1102 is coupled at least to averaging means 1103 and optimization means 1104. The optimization means 1104 is further coupled to at least the averaging means 1103 and the fitting means 1105. In FIG. 11, for convenience of illustration, all means are coupled to each other. However, it should be noted that all means can be coupled together in any other way, as long as the various functions described below can be achieved. Furthermore, the functions of several means can be combined to be realized with one of the means, and each of the means can be further divided into more means to realize the same in the system The means may be two or more quantities.

  The acquisition means 1101 is mainly used to acquire the original reconstructed image and the original sinogram. The error reduction means 1102 is mainly used to obtain a relatively ideal sinogram. The averaging means 1103 is mainly used to calculate the average orthographic value of the water phantom boundary view and several neighboring views. The optimization means 1104 is mainly used to calculate optimized basis function coefficients according to the result of the averaging means 1103 and the original sinogram. The fitting means 1105 is mainly used for fitting the beam hardening correction coefficient based on the result of the optimization means 1104.

  Assume that N water phantoms of different sizes are selected. For a particular size water phantom, first, the acquisition means acquires the original reconstructed image Iorig and the original sinogram Iorig sin. The original reconstructed image Iorig and the original sinogram Iorig sin can be input after the reconstruction of the projection data of the DAS 32 by the image reconstructor 34, can also be obtained from the mass storage device 38, and optionally Can be obtained from the computer 36.

  The error reduction means 1102 then performs an orthographic projection on the original reconstructed image, which involves the need to process the projection values by error reduction, thereby reducing the error-reduced sinogram IUnif sin. get. The approach to error reduction processing is not limited, but to obtain the value set P ′, before dividing the sum by the number of pixels, the CT of the pixels through which the X-rays detected by each detector pass according to the view. To sum the values, locate the set of projection values P corresponding to the value set P ′ in the original sinogram, and obtain the error-reduced sinogram IUnif sin, the following equation:

Where a is a constant factor, for example a value defined by the system for a water phantom.

  Next, the averaging unit 1103 determines the boundary view by the boundary determining unit 11031. In the original sinogram Iorig sin, the first view Orig_View1, eg, view 400, where the detector channel intersects the water phantom is found from top to bottom, while the detector channel intersects the water phantom in another direction. A first view Orig_View2, eg, view 200, is found from bottom to top. As shown in FIG. 4, the abscissa represents the view and the ordinate represents the detector channel. The upper line is the first horizontal line that intersects the original sinogram from above, and the abscissa corresponding to the intersection represents the view 400. The lower line is the first horizontal line that intersects the original sinogram from below, and the abscissa corresponding to the intersection represents the view 200. Similarly, views Unif_View1 (eg, view 400) and Unif_View2 (eg, view 200) corresponding to Orig_View1 (eg, view 400) and Orig_View2 (eg, view 200) are found in the error-reduced sinogram IUnif sin.

  Next, the averaging means 1103 calculates the average sine value of the boundary view by the average calculating means 11032. The original sinogram and the error-reduced sinogram each need to be calculated. First, sampling is necessary. In the original sinogram, orthographic values of two boundary views and several views before and after the two boundary views are averaged. For example, for Orig_View1, the 20 views before and after Orig_View1 are identified, and for Orig_View2, the 20 views before and after Orig_View2 are identified, to obtain an average value Orig_Aver, the average of the orthographic values of 42 views in total The value is calculated. The same operation is applied to the sinogram with reduced error. For Unif_View1, the 20 views before and after Unif_View1 are identified, and for Unif_View2, the 20 views before and after Unif_View2 are identified, and to obtain the average value Unif_Aver, the average value of the orthographic values of the 42 views in total Calculated. Here, the number of views to be averaged can be interpreted as a parameter depending on the noise level and can therefore be set experimentally. The number may be 10, 20, or 40, but in one embodiment, the number of views obtained from the two sinograms is equal.

  The optimizer 1104 then samples the average view to optimize the original sinogram Iorig sin by bringing the original sinogram Iorig sin as close as possible to the error-reduced sinogram Iorig sin. The optimization approach is shown with reference to the following equation:

Here, Q and Q ′ are Orig_Aver and Unif_Aver, respectively.

Is taken as the ideal orthographic value, where a is a constant factor, for example a value defined by the system for a water phantom.

  For this purpose, the original orthographic value P is passed through a set of basis functions Bj (j = 1, 2,... N, where n is determined experimentally as the number of basis functions used). I = 1, 2,. . . m. Where m is the highest self-defined order. The purpose of this step is to determine the coefficient vector b of the optimization function.

  For different size water phantoms, the above operation needs to be repeated, thereby determining N coefficient vectors b for N different size water phantoms. The fitting means 1105 fits N coefficient vectors b in order to obtain the beam hardening correction coefficient c. The fitting is, for example, the following formula:

Where h = 1, 2,. . . h, where h is the highest self-defined order that may be the same as or different from m.

  The beam hardening correction coefficient vector c can then be used to perform beam hardening correction on CT data of other objects of different sizes.

  FIG. 12 is a block diagram of a device 1200 for beam hardening correction according to an embodiment of the present disclosure. Here, the device for beam hardening correction includes a device 1100 for obtaining a beam hardening correction coefficient and a correction calculation device 1201 connected thereto as shown in FIG. After the device 1100 for obtaining the beam hardening correction coefficient obtains the correction coefficient vector c, the correction calculation device 1201 applies the coefficient vector c to correct the projection data p to obtain Pnew. .

As used herein, the term “a” or “an” is intended to mean both the singular and the plural. The term “or” means non-exclusive or unless otherwise specified.

  Also, as used herein, the phrase “reconstruct an image” is not intended to exclude embodiments of the invention in which data representing an image is generated instead of a visible image. Thus, the term “image” generally refers to a visible image and data representing the visible image. However, many embodiments generate (or are configured to generate) at least one visible image.

  The operating environment of the present disclosure has been described in connection with a 16-slice CT system. However, those skilled in the art will appreciate that the present disclosure is applicable to systems in multi-slice configurations and systems that can move or “jitter” the focus during operation. Let's go. Further, the present disclosure has been described in the context of X-ray detection and conversion. However, those skilled in the art will further appreciate that the present disclosure is applicable to the detection and conversion of other higher frequency electromagnetic energy. Although the specific embodiments described above have been described in connection with a third generation CT system, the methods described herein can be applied to a fourth generation CT system (a fixed detector with a rotating X-ray source) and Also compatible with fifth generation CT systems (fixed detectors and x-ray sources). In addition, it is contemplated that the benefits of the present disclosure occur in imaging modalities other than CT, such as MRI, SPECT, and PET.

  Various embodiments, and components thereof, can be implemented as part of a computer system. The computer system can include a computer, an input device, a display unit, and an interface for accessing the Internet, for example. The microprocessor can be connected to a communication bus. The computer can also include a memory. The memory may include random access memory (RAM) and read only memory (ROM). The computer system can further include a storage device, which can be a hard disk drive or a removable storage device such as a floppy disk and an optical drive. The storage device may also be used with other similar devices for loading computer programs or other instructions into the computer system.

  In various embodiments of the present disclosure, a method for obtaining a beam hardening correction factor as described herein may be implemented in the form of a processor. Typical examples of processors implement a general purpose computer, a programmed microprocessor, a digital signal processor (DPS), a microcontroller, peripheral integrated circuit elements, and method steps described herein. Including other devices or equipment that can.

  As used herein, the term “computer” is not limited to these integrated circuits, which are referred to in the art as computers, but a system that uses a microcontroller, a reduced instruction set circuit (RISC) (reduced instruction set circuit). )), Systems that use application specific integrated circuits (ASICs), systems that use logic circuits, and any that can perform the functions described herein Any processor-based or non-processor-based system can be included, including systems that use other circuits or processors. The above examples are exemplary only and are not intended to limit in any way the definition and / or meaning of the term “computer”. Terms such as computer, processor, microcontroller, microcomputer, programmable logic controller, application specific integrated circuit, and other programmable circuits are used interchangeably herein.

  The processor executes a set of instructions (eg, corresponding to method steps) stored in one or more storage elements (also known as computer usable media). The memory element can take the form of a database or physical storage element within the processor. The memory element can also be held as necessary data or other information. The physical memory can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples of physical memory include, but are not limited to, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM (erasable programmable read-only memory) or flash memory), A hard disk drive (HDD) and CD-ROM memory (CDROM) can be included. These memory types are exemplary only, and therefore the types of memory that can be used to store computer programs are not limiting.

  The instruction set can include various commands that instruct the processor to perform specific operations, such as processes of various embodiments of the present invention. The instruction set may be in the form of a software program. The software may be system software or application software. In addition, the software may be an independent program, a program module within a larger program, or a set of several program modules. The software can also include a modular program design in the form of object-oriented programming. The processor may process the input data in response to a user command, a result of previous processing, or a request sent from another processor.

  In various embodiments of the present invention, the method for obtaining the beam hardening correction factor may be implemented by software, hardware, or a combination thereof. For example, the methods provided in the various embodiments of the present disclosure can be implemented in software using standard programming languages (C, C ++, Java, etc.). As used herein, the terms “software” and “firmware” can be used interchangeably and can include any computer program stored in memory for execution by a computer.

  In addition, although the methods described herein have been described in the context of CT systems used in medical situations, these advantages include magnetic resonance (MR) systems, ultrasound systems, positron emission tomography It can be expected that imaging (PET) systems, nuclear medicine, and other types of imaging systems can be facilitated. Actions include specific organs, including biological organs such as the brain, stomach, heart, lung, or liver, anatomy such as the diaphragm, chest wall, rib cage, ribs, spine, sternum, or pelvis, tumors, injuries, or heels It can be applied to structures such as compression fractures.

  This specification discloses the invention, including preferred embodiments, and also includes those skilled in the art to make or use any device or system and perform any integration method. An example is used to enable the practice of the present invention. The patentable scope of the embodiments of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples include when they have structural elements that do not differ from the language of the claims, or when they have equivalent structural elements that do not differ substantially from the language of the claims, It is intended to be within the scope of the claims.

DESCRIPTION OF SYMBOLS 10 CT imaging system 12 Gantry 14 X-ray source 16 X-ray 18 Detector assembly 20 Detector 22 Patient 24 Center of rotation 26 Control mechanism 28 X-ray controller 30 Gantry motor controller 32 Data acquisition system, DAS
34 Image Reconstructor 36 Computer 38 Mass Storage Device 40 Operator Console 42 Display 44 Table Motor Controller 46 Motorized Table, Table 48 Gantry Opening 1100 Obtaining 1101 Beam Hardening Correction Coefficient 1101 Acquisition Means 1102 Error Reduction Means 1103 Average 1104 Optimizing means 1105 Fitting means 1200 Device for beam hardening correction 1201 Correction calculating device 11031 Boundary determining means 11032 Average calculating means

Claims (14)

A method for obtaining a beam hardening correction coefficient for performing beam hardening correction on computer tomography data,
Obtaining an original reconstructed image and original sinogram of an object of a particular size;
Obtaining an error reduced sinogram after processing the original reconstructed image with error reduction;
Sampling and calculating the average value of the original sinogram and the average value of the error-reduced sinogram;
Optimizing the original sinogram according to the error-reduced sinogram to determine a coefficient vector of an optimization function for the object of the particular size;
Fitting the coefficient vector of the optimization function of the original sinogram to obtain the beam hardening correction factor for the object of the particular size. The computed tomography of claim 1, wherein coefficient vectors of optimization functions can be calculated for objects of different sizes, and the fitting step covers all the coefficient vectors of the optimization function. A method for acquiring the beam hardening correction coefficient for performing beam hardening correction on imaging data. The step of processing the original reconstructed image by error reduction further comprises:
Summing the computed tomography values of the pixels through which the X-rays detected by each detector pass according to the view, before the step of dividing the sum by the number of pixels passed to obtain the value set P ′;
To locate a set of projection values P corresponding to the value set P ′ in the original sinogram and obtain the sinogram with reduced error, the following equation:
The method for performing beam hardening correction on computed tomography data according to claim 1, wherein a is a coefficient defined by a system for the object of the particular size A method for obtaining the beam hardening correction factor. The step of sampling and calculating the average value of the original sinogram and the average value of the error-reduced sinogram further comprises:
Determining a boundary view of the original sinogram and a neighboring view of the original sinogram as a first sampling view, and the boundary view of the error-reduced sinogram and the neighboring view of the error-reduced sinogram as a second sampling view Step to determine as
Calculating an average value of the first sampling view and an average value of the second sampling view as an average value of the original sinogram and an average value of the error-reduced sinogram, respectively. A method for obtaining the beam hardening correction coefficient for performing beam hardening correction on the computed tomography data according to claim 1. The step of optimizing the original sinogram according to the error-reduced sinogram further comprises:
Calculating a coefficient vector b that makes the value of N as close to zero as possible, where Q and Q ′ are the average value of the original sinogram and the average value of the error-reduced sinogram, respectively, and Bj is used Basis functions, j = 1, 2,. . . n, where n is the number of basis functions used, i = 1, 2,. . . The method of obtaining the beam hardening correction factor for performing beam hardening correction on computed tomography data according to claim 1, wherein m is m and m is the highest defined order. Fitting the coefficient vector of the optimization function of the original sinogram;
formula,
, Further calculating a beam hardening correction vector c, k = 1, 2,. . . 6. The method of obtaining the beam hardening correction factor for performing beam hardening correction on computed tomography data according to claim 5, wherein h is h and is the highest defined order. A device for obtaining a beam hardening correction coefficient for performing beam hardening correction on computer tomography data,
An acquisition device for acquiring an original reconstructed image and original sinogram of an object of a particular size;
An error reduction device for obtaining an error reduced sinogram after processing the original reconstructed image by error reduction;
An averaging device for sampling and calculating the average value of the original sinogram and the average value of the error-reduced sinogram;
An optimization device for optimizing the original sinogram according to the error-reduced sinogram to determine a coefficient vector of an optimization function for the object of the specific size;
A fitting device for fitting the coefficient vector of the optimization function of the original sinogram to obtain the beam hardening correction factor for the object of the specific size. 8. Computed tomography data according to claim 7, wherein coefficient vectors of optimization functions can be calculated for objects of different sizes, and the fitting device fits all the coefficient vectors of the optimization function. A device for obtaining the beam hardening correction coefficient for performing beam hardening correction. The error reduction device further comprises:
To obtain the value set P ′, before dividing the sum by the number of pixels passed, sum the computed tomography values of the pixels through which the X-rays detected by each detector pass according to the view,
To locate a set of projection values P corresponding to the value set P ′ in the original sinogram and obtain the sinogram with reduced error, the following equation:
The beam hardening for performing beam hardening correction on computed tomography data according to claim 7, wherein a is a coefficient defined by a system for the object. A device for obtaining correction factors. The averaging device further comprises:
Determining a boundary view of the original sinogram and a neighboring view of the original sinogram as a first sampling view, and the boundary view of the error-reduced sinogram and the neighboring view of the error-reduced sinogram as a second sampling view Determined as
Operable to calculate an average value of the first sampling view and an average value of the second sampling view as an average value of the original sinogram and an average value of the error-reduced sinogram, respectively. A device for obtaining the beam hardening correction coefficient for performing beam hardening correction on the computed tomography data according to claim 7. The optimization device further comprises:
Is operable to calculate a coefficient vector b that approximates as close to zero as possible, Q and Q ′ are the mean value of the original sinogram and the mean value of the error-reduced sinogram, respectively, and Bj is Is the basis function used, j = 1, 2,. . . n, where n is the number of basis functions used, i = 1, 2,. . . 8. A device for obtaining the beam hardening correction factor for performing beam hardening correction on computed tomography data according to claim 7, wherein m is m and m is the highest defined order. The fitting device is
formula,
And is further operable to calculate a beam hardening correction vector c, k = 1, 2,. . . The device for obtaining the beam hardening correction factor for performing beam hardening correction on computed tomography data according to claim 11, wherein h is h and h is the highest defined order. 8. A correction calculation device configured to use the beam hardening correction factor to perform beam hardening correction on computed tomography data of at least one other object of a particular size. A device for obtaining the beam hardening correction coefficient for performing beam hardening correction on the computed tomography data. A scanning device for scanning an object using x-rays to obtain original data for generating an original reconstructed image;
Operably coupled to the scanning device;
Get an original reconstructed image and an original sinogram of an object of a certain size,
After processing the original reconstructed image with error reduction, an error reduced sinogram is obtained;
Sampling and calculating the mean value of the original sinogram and the mean value of the error-reduced sinogram;
Optimizing the original sinogram according to the error-reduced sinogram to determine a coefficient vector of an optimization function for the object of the specific size;
A computer tom comprising: a processor configured to fit the coefficient vector of the optimization function of the original sinogram to obtain a beam hardening correction factor for the object of the particular size Shooting device.